esa-sceva/satcom-chunk-collection huggingface dataset:
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 3 Implementation
### Satellite Terminal  
Inmarsat (as shown in Table 1) was selected as the satellite internet service provider in this study, and its Broadband Global Area Network (BGAN) service plan is used. BGAN uses GEO systems that do not require antenna redirection, so there is no need to maintain antenna pointing. Inmarsat's BGAN service plan covers the whole world without additional charges and reconfiguration, making the system universal in every country. Another reason to choose Inmarsat BGAN over other satellite phones is that it offers the fastest connection speeds and flexible data usage plans [16] with a cost as low as USD $65 for 20 MB.  
The Hughes 9201 satellite terminal [17] was chosen because it is the last generation system with a sharp price drop. Compare it to the latest generation system (Hughes 9202 BGAN Satellite Terminal [18]), which costs USD $2795, the 9201 terminal costs only USD $350. The Hughes 9201 offers the same internet speed and similar connection speeds.
# A Comprehensive Approach to Bias Mitigation for Sentiment Analysis of Social Media Data
## 2 Related Work
### Bias in Sentiment Analysis  
The issue of bias in machine learning, particularly in sentiment analysis, has gained significant attention. Bias can appear in many forms, including gender, race, age, and socio-economic status [26]. Tackling these biases is crucial for ethically sound sentiment analysis models. The bias in sentiment analysis models often comes from the training data [26]. For example, if the data primarily consist of reviews from one demographic, the model may end up biased toward the perspectives of that group. Similarly, if the data include gendered language or culturally specific phrases, the model can inherit these biases [38]. Understanding the makeup of the training data is the first step in recognizing where bias could be introduced. Detecting bias in sentiment analysis models is both challenging and necessary [39]. Several tools and methods have been developed to help identify and measure bias. For instance, the AI Fairness 360 toolkit offers a set of metrics to understand bias in model predictions [40]. These metrics can reveal disparities in how a model performs between different demographic groups, helping researchers focus on where bias needs to be addressed.  
Once bias is detected, the next step is finding ways to reduce it. Various techniques have been proposed [41; 42], each with its own strengths and weaknesses. One common approach is to re-sample the training data to ensure they represent different groups more fairly [43]. Another strategy is to add regularization terms to the model loss function, penalizing biased predictions [44]. More advanced techniques, such as adversarial training, aim to prevent the model from learning sensitive attributes, reducing the chance of biased predictions [45]. The intersection of sentiment analysis and bias mitigation has led to the emerging field of bias-aware sentiment analysis [46; 47]. This area focuses on building models that are not only good at predicting sentiment but are also mindful of the biases they may introduce. Bias-adaptive training methods [47; 48] adjust the training process in response to detected biases in the data or model. For example, if the model shows a bias towards a certain demographic, the training algorithm can be modified to place more emphasis on correctly classifying underrepresented groups [45]. Another way to achieve bias-aware sentiment analysis is by tweaking the loss function during training [49]. Bias in sentiment analysis can have real-world effects, from reinforcing harmful stereotypes to unfairly disadvantage certain groups. Our work aims to make a meaningful contribution to this area by proposing a new methodology that not only improves accuracy, but also actively reduces bias.
## 3 Methodology
### Video Conference Monitoring Interface  
In architecture A, the system can accept live screen recording or recorded video as the input. This python-based interface was developed using OpenCV to receive the captured real-time video stream through the open-source software OBS with the help of the NginX streaming server. This application is capable of splitting video frames into pieces depending on the row and column configuration given by the user. In addition to the number of columns and rows, there is an offset control feature to define the region of interest in the video frame before slicing (Figure 2b). Then, the image of each participant is sent to the server as a form submission using the API interface.  
The video conference interface is one of the systems which interacts with the user directly; hence, this system needs to have a user-friendly interface. The Zoom web SDK (a version of Zoom Meeting SDK) was used to create the capturing software in design architecture B; its main task is to provide the web interface to give access to the AI tool to attend the meeting as a virtual participant. The remainder of the process is fully automated. This AI tool works as an observer, and it is capable of navigating through the entire meeting even when the participants are shown on multiple pages. The AI tool uses pagination buttons of the Zoom interface in the same way that a person would do to see all participants. This is possible because the tool can automatically create events such as hovering and button clicking by running JavaScript programs. After scanning the details and face images of all the participants in the meeting, the information is sent to the server as a form submission via an API interface.
# An Anonymous Offline RFID Grouping-Proof Protocol
## 6 Description of EAGP
### Eagp\({}^{(2)}\)
#### 6.1.2 Grouping-Proof Generation Phase  
The framework is demonstrated in Figure 3.  
1. Reader generates a random number \(r_{s}\), calculates \(C_{0}=r_{s}P\), \(C_{1}=r_{s}Y\), and \(s=r_{s}+yx(C_{1})\). Then, the \(\{s,C_{0},C_{1},r_{s}\}\) is sent to the tag A along with the message of \"_start left_\".
2. Tag A verifies the equation \(sP=C_{0}+x(C_{1})Y\). If it does not hold, the protocol is terminated. Otherwise, it generates a random number \(k_{1}\), calculates \(r_{a}=x(k_{1}P)\), generates the session secret key \(k_{a}=x(Y)\bigoplus r_{a}\). Then, it seals its secret key \(k_{ai}\) into message \(m_{a}\) as follows: \[m_{a}=k_{1}^{-1}(r_{s}+k_{ai}\times r_{a})\] (2) Finally, tag A sends \(\{m_{a},\,r_{a}\}\) to the reader.
3. Reader sends \(\{m_{a},\,s,\,C_{0},\,C_{1},\,r_{s}\}\) along with the message of \"_start right_\" to tag B.
4. Tag B verifies the equation \(sP=C_{0}+x(C_{1})Y\). If it does not hold, the protocol is terminated. Otherwise, it generates a random number \(k_{2}\), calculates \(r_{b},k_{b},m_{b},T_{b}\) and sends \(\{m_{b},\,T_{b},\,r_{b}\}\) to the reader.
5. Reader sends the message \(T_{b}\) to tag A.
6. Tag A calculates \(T_{a}=(m_{a}+x(T_{b})k_{a})Y\), and sends it to the reader.
7. Reader generates the grouping-proof \(G\) shown in Equation (3) \[G=\{m_{a},\,T_{a},\,m_{b},\,T_{b},\,r_{a},\,r_{b},\,s\}\] (3)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(r_{s},r_{a},r_{b}\) & The random number generated by reader, tag A and tag B. \\ \(P\) & The base point on the elliptic curve \(E(\mathbb{F}_{q})\). \\ \(Y,y\) & The public/private key of Group \(G\). \\ \(k_{a},k_{b}\) & Temporary grouping-proof key of tag A and tag B. \\ \(k_{ai},k_{bi}\) & Secret key of tag A and tag B. \\ \(PK_{A},PK_{B}\) & Public key of tag A and tag B. \\ \(x(T)\) & The x-coordinate of point \(T\). \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of notations in EAGP\({}^{(2)}\).
## 5 Comparison of Clustering Analysis Methods
### Gustafson-Kessel Algorithm  
The Gustafson-Kessel algorithm obtains the objective function by introducing the covariance matrix, which is suitable for the clustering analysis of the correlation between the variables and is suitable for the distribution of irregular data [32]. The clustering algorithm uses the adaptive distance of the clustering covariance matrix to measure. By obtaining the objective function to achieve the membership matrix of the fuzzy clustering,, and the clustering center \(V=(v_{1},v_{2},v_{3},\cdots,v_{c})^{T}\), where: c is the number of samples; \(u_{ij}\) is the clustering center membership relative to the data point; and they meet \(u_{ij}\in[0,1]\), \(\sum\limits_{i=1}^{c}u_{ij}=1\), \(1\leq j\leq N\). The data sequence \(X=(x_{1},x_{2},\cdots,x_{N})\) is given, and its minimized objective function is:  
\[J(X,V,U)=\sum\limits_{i=1}^{c}\sum\limits_{j=1}^{N}(u_{ij})^{m}D_{ij}^{2} \tag{7}\]  
\[D_{ij}^{2}=\left\|x_{j}-v_{i}\right\|_{A_{i}}^{2}=\big{(}x_{j}-v_{i}\big{)}^{T }A_{i}\big{(}x_{j}-v_{i}\big{)} \tag{8}\]  
\[A_{i}=\det(F_{i})^{\frac{1}{4}}F_{i}^{-1} \tag{9}\]  
where: \(m\) is the fuzzy index, representing the degree of fuzzy clustering, and the greater the value of \(m\), the greater the degree of overlap between the major clusters; usually, \(m\) takes one or two; \(D_{ij}^{2}\) is the distance from any data point \(x_{j}\) to the cluster center \(v_{i}\), and it is a square inner product norm; \(A_{i}\) is a positive definite symmetric matrix, and it is determined by the clustering matrix covariance matrix \(F_{i}\).  
However, Formula (9) cannot be minimized directly considering its linear features. In order to obtain a viable solution, \(A_{i}\) must be constrained in some way. The usual way is to constrain the determinant of \(A_{i}\). The allowable matrix \(A_{i}\) varies with its determinant, corresponding to the shape of the optimized cluster, while its volume remains unchanged:  
\[\left\|A_{i}\right\|=\rho_{i},\rho>0 \tag{10}\]  
where \(\rho_{i}\) is certain for each cluster. Using the Lagrange multiplier method, \(A_{i}\) is obtained:  
\[A_{i}=[\rho_{i}\text{det}(F_{i})]^{1/n}F_{i}^{-1} \tag{11}\]  
where \(F_{i}\) is defined by:  
\[F_{i}=\frac{\sum\limits_{k=1}^{N}(\mu_{ik})^{m}(x_{k}-v_{i})(x_{k}-v_{i})^{T} }{\sum\limits_{k=1}^{N}(\mu_{ik})^{m}} \tag{12}\]
###### Abstract  
Bisphosphonates (BPs) are widely used in Osteogenesis imperfeta (OI). Cone Beam Computed Tomography (CBCT) shows clinical usefulness in evaluating impacted teeth and adjacent structure relationships, extraction socket healing, bone mineral density (BMD) and BP-related jaw osteogenesis (BRONJ). The aim of the study was to compare alveolar sockets and the adjacent bone area before and after third molar extraction in OI type I (OI-I) adolescents treated with BPs and age-matched healthy subjects (HSs) by CBCT. **Methods:** Five adolescents with genetically proven OI-I treated with BPs (three males and two females, mean age: 15.2 \(\pm\) 1.78 years) and four age-matched healthy subjects (two males and two females, mean age: 15.5 \(\pm\) 1.29 years) were included in this study. Eight Regions of Interest (ROIs) were evaluated: between 3.7 and 3.8 (ROI-1) and 4.7 and 4.8 (ROI-2); after 3.8 (ROI-3) and 4.8 (ROI-4); alveolar sockets 3.8 (ROI-5) and 4.8 (ROI-6); left (ROI-7) and right (ROI-8) cortical bone. **Results:** ROIs were evaluated on both sides of the mandible for all the subjects except one OI patient in which CBCTs were performed pre- and-post third molar extraction only on the right side. CBCT was performed 12.8 \(\pm\) 4.60 and 11.5 \(\pm\) 2.51 days before and 8.0 \(\pm\) 1.41 and 7.7 \(\pm\) 0.5 months after extraction in OI-I and HSs, respectively. BPs were discontinued 62.0 \(\pm\) 36.5 months before extraction. None of the OI-I adolescents developed BRONJ. Statistically significant greater values were observed in OI-I for ROI-1 and -2 (\(p\) = 0.0464), ROI-3 and -4 (\(p\) = 0.0037) and ROI-7 and -8 (\(p\) = 0.0079) after extraction. **Conclusions:** This descriptive study confirms that, in OI-I adolescents treated with BPs, third molar extraction is safe, and socket healing occurs properly. In addition, it demonstrates that, if the same device and imaging conditions are used and comparisons to predetermined standard values are avoided, CBCT can be used to monitor BMD changes. The significant greater BMD observed for different ROIs in OI-I could reflect the increased secondary mineralization related to the BP-dependent reduction in bone turnover.  
osteogenesis imperfeta; cone beam computed tomography; region of interest; bone mineral density; pediatric dentistry; bisphosphonates; third molar extraction; socket healing
###### Abstract  
This study aimed to evaluate the influence of the guidance level on the accuracy of immediately placed implants. Methods: Eighteen identical maxillary models (randomly split into three groups (n = 6): fully guided (FG), pilot-guided (PG), and freehand (FH)) were used to place 72 implants (n = 24). After placement, the mean global, angular, mesial-distal, buccal-palatal, and vertical deviation at the platform and apex of the placed implants, relative to the preoperatively planned positions, was calculated. Results: Significant differences in global and mesial-distal deviation were found between implants in the FG group and implants in the PG and FH groups. Significant differences were also found between the implants in the FG and FH groups as well as the implants in the PG and FH groups in terms of angular and mesial-distal apex deviation. Finally, significant differences were found between the implants in the FG and PG groups in terms of buccal-palatal platform deviation. Conclusions: The results of this in vitro study show that immediate implants placed using fully guided surgical guides have significantly less deviation, and are therefore more accurate, than implants placed freehand or using pilot guides, but this should be further validated in a clinical trial.  
Keywords:dental implant; single tooth; immediate implants; accuracy; guided surgery; 3D-printed surgical guide +
Footnote †: journal: Applied Sciences
###### Abstract  
In recent years, machine learning methods have shown the great potential for real-time transient stability status prediction (TSSP) application. However, most existing studies overlook the imbalanced data problem in TSSP. To address this issue, a novel data segmentation-based ensemble classification (DSEC) method for TSSP is proposed in this paper. Firstly, the effects of the imbalanced data problem on the decision boundary and classification performance of TSSP are investigated in detail. Then, a three-step DSEC method is presented. In the first step, the data segmentation strategy is utilized for dividing the stable samples into multiple non-overlapping stable subsets, ensuring that the samples in each stable subset are not more than the unstable ones, then each stable subset is combined with the unstable set into a training subset. For the second step, an AdaBoost classifier is built based on each training subset. In the final step, decision values from each AdaBoost classifier are aggregated for determining the transient stability status. The experiments are conducted on the Northeast Power Coordinating Council 140-bus system and the simulation results indicate that the proposed approach can significantly improve the classification performance of TSSP with imbalanced data.  
Keywords:imbalanced data; data segmentation; ensemble classification; transient stability status +
Footnote †: journal: Applied Sciences
# Combination of Variable Loads in Structural Design
## 1 Introduction
### Novelty  
This article presents a new model for the combination of variable loads and calculation of combination factors in fundamental design scenarios involving snow, wind, and imposed loads, featuring six discoveries:  
* Variable loads are traditionally perceived as independent when considered as single load pairs and are normally combined independently. However, this article reveals that certain scenarios necessitate dependent load combinations for accurate analysis.
* A new procedure is provided for calculating combination factors for one load in proportion \(\alpha\) and the other \(1-\alpha\), including the basic scenarios where the loads have the same magnitude, i.e., the load proportion is 0.5 and the combination factor is the lowest.
* The current combination factors in codes are based on load proportions where loads are equal, i.e., a load proportion of 0.5. However, it is imperative to incorporate values derived from various load proportion scenarios in setting combination factors for codes. This approach yields higher values compared to those obtained solely from a load proportion of 0.5. To facilitate practical application, approximate code values are provided.
* The primary objective of employing combination factors is to mitigate excessive safety margins inherent in the combination of two variable loads. The existing model for variable load modelling, relying on a single combination factor, is deemed inadequate. While it offers a notable advantage when dealing with rare cases with loads of similar magnitudes, its effectiveness diminishes in scenarios involving frequent cases with loads of varying magnitudes, including a harmful unsafe error.
* There is no basis for setting a target reliability limit for the combination of serviceability limit state (SLS) loads. Any limit results in a combination factor arbitrarily decreasing or increasing the combination load. However, the SLS loads are mean loads which are added up deterministically and dependently without constraints regardless of whether the loads are independent or dependent. A combination factor may be applied due to intermittence or to convert the loads to simultaneous.
* This article conducts a comparative analysis of the gains introduced by combination factors with errors and approximations inherent in structural codes. It concludes that the gains associated with combination factors are negligible regarding the unavoidable errors and approximations present in the codes, highlighting the insignificance of combination factors in structural analysis.  
By introducing these innovations, this article aims to enhance the precision and user-friendliness of structural design methodologies, particularly in scenarios involving variable loads, thereby contributing to advancements in the field of structural engineering.
## 4 Selenium in Thyroid Bland
### Role of Selenoproteins in Thyroid Gland  
The DIO family consists of 3 types of DIO (DIO1, DIO2 and DIO3), that are integral membrane dimeric selenoproteins, each having different catalytic properties in specific tissue and developmental expressions [80] (Table 1). DIO convert pro-hormone T4 to active T3 and reverse T3 (rT3) as well as to diiodothyronine (T2) [80]. They contain a thyroredoxin fold that catalyzes the stereospecific and sequential elimination of iodine atoms from tyrosine residues. Their reactions are complementary and essential to the metabolism and activities of Thyroid hormones [82]. Table 1 summarizes the main functions of selenoproteins in thyroid. Both DIO1 and DIO2 are hosted in the thyrocytes and provide T3 obtained by deiodination of T4. T3 is the main mediator of the effects of thyroid hormones. Extra-thyroid DIO1 is involved in the conversion in the liver of T4 to T3. DIO2 controls the intracellular activation of T4 into T3 in target tissues and into endocrine cells [86]. Sensitive cells are protected from biologically active T3 thyrotoxic concentrations, so DIOs control the regulation of the thyroid axis [87]. The thyroid physiological role of DIO1 and DIO2 expression is not fully understood. The hierarchy that controls the expression of selenoproteins gives priority to the distribution of Se to essential selenoenzymes and to the most physiologically important tissues preserving their metabolism under Se deficiency conditions [80]. In cultured cells and rodent tissues, the activities of DIO are protected against Se deficiency at the expense of other selenoproteins. Similar compensation pathways also function in humans, as there is no report of an association between limited Se intake and general defects in the central nervous system and in vision or hearing development under otherwise normal conditions [80]. Glutathione peroxidase is the best characterized selenoprotein family in human. Among the selenoproteins, the GPx family constitutes the main components of human antioxidant defense with five isozymes (GPx1-GPx4, GPx6) [83]. The role of the GPx is to reduce H\({}_{2}\)O\({}_{2}\) and organic hydroperoxides to protect cells from the effects of reactive oxygen species (ROS) [82]. This group of selenoproteins is largely involved in thyroid gland function [87]. GPx3 is the most actively expressed isoform in thyroid gland and kidneys [87]. Probably its role in thyroid colloid is to degrade the large amount of H\({}_{2}\)O\({}_{2}\) produced during the iodination of thyroid hormones [88]. GPx4 isoform is present in mitochondria of thyrocyte, its role is the protection of membrane lipids by reducing phospholipid hydroperoxides while, GPx1 is a cytosolic enzyme [87]. Among the thyroid selenoproteins, GPx1 and GPx3 are the most sensitive to the Se level [89].
## 4 Discussion  
### Validation of Calculated Parameters  
Samples studied are all carbonate rocks divided in three different rock types: chalk, limestone, and dolomite. Although some differences have been demonstrated the previous part, each rock type has its specific properties. Nevertheless, the 30 samples have petrophysical characteristics consistent with carbonate rocks. Concerning structural properties, cementation indexes are usually between 1.8 and 4 for carbonate rocks [4] and more precisely between 2 and 2.2 for grainstone carbonates, with an increase of \(m\) in low-connected samples [31]. For samples with porosity comprised between 20 and 40% (such as Normandie), permeability is usually comprised between 1 and 10,000 mD [10], \(F\) should be between 6 and 125 (Equation (5)) while \(\tau\) between 1.6 and 5 (Equation (6)), with the highest \(F\) and \(\tau_{e}\) for \(m=3\) and the lowest for \(m=2\)[47,48]. For porosities between 10 and 20% (as Euville and Lexos), permeability is from 0.01 to 1000 mD [10], \(F\) and \(\tau\) are higher and could reach respectively 100 and 3.1 for \(m=2\), while 1000 and 10 for \(m=3\). This is the case for our 30 samples (Table 1).  
Given this consistence with literature data, we compared our dataset with other studies.