esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Approach to Bias Mitigation for Sentiment Analysis of Social Media Data
## 1 Introduction
### Background and Motivation  
The rise of the Internet and, more recently, social networks has led to an explosion of text data [8; 9], giving us an unprecedented opportunity to better understand human emotions and opinions. Sentiment analysis has been gaining momentum as a way to automatically process and make sense of this overwhelming volume of information. Its applications are broad, ranging from automating customer service interactions [10] to tracking public sentiment around elections [11], and even monitoring mental health trends [12]. However, as the technology has evolved, so have the challenges. One of the toughest hurdles is the inherent complexity of human emotion. People express their feelings in so many ways, sometimes with sarcasm, idioms, or cultural references--that it can be difficult for a machine to detect these subtleties [13]. As a result, creating a model that works universally, in different contexts and cultures, is difficult.  
An additional challenge comes from the potential for bias in sentiment analysis models [14; 15]. These biases often creep in through the training data and can lead to skewed predictions. For instance, if a model is trained on data from one specific demographic, it may not be able to accurately interpret the sentiments of people from other backgrounds. This becomes especially problematic when applying sentiment analysis to sensitive areas such as sexual orientation, religion, or disability, where biased results could have serious consequences. Given these challenges, our research is driven by two primary motivations: developing a sentiment analysis approach that is both technically accurate and ethically sound. Our goal is to address the technical hurdles of sentiment analysis while also confronting the ethical issues involved in deploying these models, particularly in delicate or high-impact areas.
## 4 Conclusions  
The main purpose of this work was the study of possible alternative materials to Portland cement to obtain more sustainable and durable cementitious materials. The most important conclusions that can be drawn from this study are the following:  
* All the residues used in this work show pozzolanic behavior (SLS and Fratini tests) and have good capacities for the manufacturing of alternative cements. The residues from the ceramic industry are those with the greatest pozzolanic character.
* Silicate-activated slag systems (AAS-WG) present the best mechanical performance, but hybrid mortars present an important alternative, obtaining competitive results.
* Ceramic waste hybrid mortars show worse mechanical resistance results. It is necessary to increase the percentage of CEM I to improve the hydration process.
* Most of the alternative hybrid systems proposed showed an improvement in durability to abrasion compared to Portland cement (CEM I), highlighting the behavior of AAS-WG and HS-5%.  
Therefore, it can be concluded that these alternative systems to Portland cement (both alkaline-activated and hybrid mortars) show adequate behavior from a mechanical and durable point of view, thus opening a gate to development and industrial implementation in the construction sector.  
Conceptualization, M.T.-C., S.S., L.R., and F.V.; methodology, M.T.-C., S.S. and L.R.; validation, M.T.-C., S.S., L.R., A.B., M.F.-A. and F.V.; formal analysis, M.T.-C., S.S., L.R., A.B., M.F.-A. and F.V.; investigation, M.T.-C., S.S., L.R., A.B. and F.V.; writing--original draft preparation, M.T.-C., S.S., L.R., A.B., M.F.-A. and F.V.; writing--review and editing, M.T.-C., S.S., L.R., A.B., M.F.-A. and F.V.; visualization, M.T.-C., S.S., L.R., A.B., M.F.-A. and F.V.; supervision, M.T.-C., A.B. and F.V.; project administration, M.T.-C., A.B. and F.V.; funding acquisition, M.T.-C., A.B. and F.V. All authors have read and agreed to the published version of the manuscript.  
This word was founded by the Ministerio de Ciencia, Innovacion y Universidades of Spain through the project RTI2018-096428-B-I00 and by the regional project of Comunidad de Madrid, HORATSO- UC3M-CM.  
This study was funded by the Ministerio de Ciencia, Innovacion y Universidades of Spain through project RTI2018-096428-B-I00 and through regional project HORATSO-UC3M-CM.  
The authors declare no conflict of interest.  
Figure 7: Profilometry 3D image of representative worn surfaces after the Böhme test of (**a**) CEM I, (**b**) HS-5%, (**c**) HFA-5%, (**d**) HWT-5% and (**e**) HWP-5%.
## 4 Test on Cosmo-SkyMed Data  
The performance of the procedure has been evaluated processing a long temporal series of CSK data using three different case studies which are described in detail in Section 4.1. The results estimated from satellite data are validated by comparison with the in-situ observations in Section 4.2.
## 3 Results
### Vitamin B12  
In order to assess the effect of microalgae addition on the content and bioavailability of vitamin B12, samples enriched with _Spirulina_ and _Chlorella_ at concentrations of 0-5% were analyzed (Figure 6). Panel A shows the amount of released vitamin B12 (ng/g), while panel B shows the relative bioavailability (%) of this vitamin after in vitro digestion simulation. The introduction of _Spirulina_ and _Chlorella_ extract to kefir samples led to a significant (\(p<0.05\)) increase in the content of released vitamin B12. The highest level was noted in the sample with 5% _Spirulina_ addition (5.01 \(\pm\) 0.27 ng/g), which had a significantly higher level in relation to all other variants. _Spirulina_ also significantly increased the amount of vitamin B12, but these values were slightly lower than _Chlorella_ at the highest level (5%) of algae addition (4.24 \(\pm\) 0.23 ng/g). Despite the increased total B12 content, a clear decrease in its bioavailability was observed with increasing microalgae content (Figure 1B). For samples with 5% addition of both _Spirulina_ and _Chlorella_, the bioavailability dropped below 10%, indicating a potential interference of the algal matrix in the digestion and absorption processes. The highest bioavailability (-45-50%) was recorded in the control samples and those with the lowest level of addition (0.1%).  
The obtained results are consistent with previous reports indicating that microalgae can be a source of the bioactive form of vitamin B12, but the presence of pseudoforms (e.g., biologically inactive B12 analogues) may affect their bioavailability (Stein
# Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images
## 4 Results
### Results of Training Mask-RCNN 'head' Layers
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **Train** & \(\mathbf{AP_{50}}\) & \(\mathbf{AP_{5}}\) \\ \hline YOLO + Mask-RCNN & RGB & 21.42\% & 1.52\% \\ Tiny-YOLO + Mask-RCNN & RGB & 36.30\% & 4.03\% \\ Tiny-YOLOv2 + Mask-RCNN & RGB & 33.72\% & 2.17\% \\ Tiny-YOLOv3 + Mask-RCNN & RGB & 31.71\% & 3.78\% \\ Tiny-YOLOv4 + Mask-RCNN & RGB & 7.12\% & 0.12\% \\ \hline YOLO + Mask-RCNN & CIR & 24.48\% & 1.66\% \\ Tiny-YOLO + Mask-RCNN & CIR & 36.67\% & 3.34\% \\ Tiny-YOLOv2 + Mask-RCNN & CIR & 23.96\% & 0.93\% \\ Tiny-YOLOv3 + Mask-RCNN & CIR & 36.02\% & 4.50\% \\ Tiny-YOLOv4 + Mask-RCNN & CIR & 19.30\% & 1.16\% \\ \hline YOLO + Mask-RCNN & NDRE & 4.55\% & 0.47\% \\ Tiny-YOLO + Mask-RCNN & NDRE & 0.11\% & 0.0\% \\ Tiny-YOLOv2 + Mask-RCNN & NDRE & 0.02\% & 0.0\% \\ Tiny-YOLOv3 + Mask-RCNN & NDRE & 5.68\% & 1.94\% \\ Tiny-YOLOv4 + Mask-RCNN & NDRE & 0.0\% & 0.0\% \\ \hline YOLO + Mask-RCNN & NDWI & 2.22\% & 0.0\% \\ Tiny-YOLO + Mask-RCNN & NDWI & 1.68\% & 0.0\% \\ Tiny-YOLOv2 + Mask-RCNN & NDWI & 11.69\% & 0.61\% \\ Tiny-YOLOv3 + Mask-RCNN & NDWI & 9.14\% & 0.0\% \\ Tiny-YOLOv4 + Mask-RCNN & NDWI & 1.20\% & 0.0\% \\ \hline YOLO + Mask-RCNN & NDVI & 45.65\% & 6.88\% \\ Tiny-YOLO + Mask-RCNN & NDVI & 23.41\% & 1.61\% \\ Tiny-YOLOv2 + Mask-RCNN & NDVI & 37.95\% & 6.76\% \\ Tiny-YOLOv3 + Mask-RCNN & NDVI & 0.07\% & 0.0\% \\ Tiny-YOLOv4 + Mask-RCNN & NDVI & 9.87\% & 0.0\% \\ \hline YOLO + Mask-RCNN & GS & 35.32\% & 6.46\% \\ Tiny-YOLO + Mask-RCNN & GS & 23.32\% & 1.75\% \\ Tiny-YOLOv2 + Mask-RCNN & GS & 18.91\% & 3.20\% \\ Tiny-YOLOv3 + Mask-RCNN & GS & 46.48\% & 6.48\% \\ Tiny-YOLOv4 + Mask-RCNN & GS & 15.38\% & 0.04\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mask-RCNN ‘heads’ layers training for 30 epochs. Method: weights used + Mask-RCNN; Train: dataset used in training; \(\mathbf{AP_{50}}\): average precision with an IoU of 0.5; \(\mathbf{AP_{75}}\): average precision with an IoU 0.75. Some datasets, such as NDARE, NDVI, and NDWI, also tended to either have too many detected gaps or none at all, as seen in Figure 7. There could be two ways of looking at it: the first and the one that probably has more influence on the results is the smaller amount of training images; the other is that these images have generally less contrast, which can either lead to nothing being detected or everything being detected as a gap. Figure 5: Examples of Mask-RCNN, after training ‘head’ layers for 30 epochs, detecting too many objects. Dataset: CIR; Weights: Tiny-YOLO; (**a**): Original; (**b**): Detected. Figure 6: Examples of Mask-RCNN, after training ‘head’ layers for 30 epochs, miscategorizing the path. Dataset: GS; Weights: Tiny-YOLO; (**a**): Original; (**b**): Detected. At this point, the network seems to also be mostly incapable of detecting the gaps that are closer to 45\({}^{\circ}\) in orientation, as seen in Figure 8. This is due to the decreased amount of images, with crops being displayed diagonally in the dataset and the training time being only 30 epochs.
## 3 Distance-Based Opportunistic Push/Subscribe Content Dissemination Model
### Decision-Making Layer
#### 3.4.2 Publishing Content  
Every node periodically records its location. The location file format is (date, \(GPS_{x},GPS_{y}\)). When a provider accesses CSS, it checks the subscription table. If it has the subscribed content in a subscription, the provider gets the deadline of the subscription and the subscriber's location. Then it calculates the remaining days from the current date to the deadline. For example, if the remaining days are 3 days, it reads the past 3 days' location records from its location file. Then the provider calculates the distances from its current location to each location (\(GPS_{x},GPS_{y}\)) in the past 3 days, and it gets the longest distance \(d_{max}\). Afterwards, it computes \(d_{ps}\), which is the distance from current location to the subscriber's location. If \(d_{max}\geq d_{ps}\), the provider will publish the content, otherwise, it will not.  
In Figure 3, node_4 subscribes to video_1 on CSS and node_1 has this video. When node_1 contacts CSS, it finds that a node subscribed video_1. Node_1 gets the deadline of the subscription and the subscriber's location, (x\({}_{1}\), y\({}_{1}\)). For example, if today is 19 April 2016, the remaining days from today to 20 April 2016 is 1 day. Node_1 calculates the distance \(d_{ps}\) between it and (x\({}_{1}\), y\({}_{1}\)). Assume \(d_{ps}\) to be 4.5 km. Node_1 calculates the longest distance it travelled yesterday. Suppose \(d_{max}\) to be 7 kilometers, node_1 begins to publish video_1 onto the opportunistic network through direct WiFi connections because \(d_{max}>d_{ps}\). Node_1 sends video_1 to node_2 and node_3. Node_3 is carried by its user from one place to another place and meets node_4. Node_3 transmits video_1 to node_4 when they encounter each other.
## 2 Data and Methods  
### EO-1 Instrument Characteristics  
The EO-1 ALI is an 8-band MS imager having a 15\({}^{\circ}\) Wide Field Telescope (WFT) and a partially populated focal plane occupying 1/5 th of the field-of-view, giving a ground swath width of 37 km. Hyperion is a grating imaging spectrometer with a 7.7 km swath and it provides 220 functional bands with an approximately 10 nm sampling interval from 430-2400 nm [3]. Both ALI and Hyperion have 30 m nadir ground pixels to match Landsat-7. All collected data are archived and distributed by the USGS/EROS data center [24].
# Fatigue of Friction Stir Welded Aluminum Alloy Joints: A Review
## 3 Factors Affecting Fatigue Performance  
### Process Parameters  
The rotation speed and welding speed of the stirring tool are the main factors affecting fatigue performance of FSW joints. [PERSON] [24] achieved the best fatigue performance on 6061 aluminum alloy by controlling the tool rotation speed at 1000 rpm and tool travel speed at 80 mm/min, indicating that heat input was closely related to the fatigue performance of welded joints. For the parameter of tool tilt angle, high quality joint with better fatigue life can be produced between 2 and 5\({}^{\circ}\)[36; 37; 38]. Different process parameters for the same material lead to differences in fatigue crack growth factor. Unreasonable process parameters are likely to make defects. The summary of stress amplitude--half of the stress range \(\Delta\sigma\) corresponding to 95% survival rate--for \(N_{f}=10\)[6] with the optimal process parameters is shown in Table 1[24; 39; 40; 41; 42; 43; 44; 45; 46].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Material** & **Rotating Speed** & **Welding Speed** & **Thickness** & **Stress** & **A\(\sigma\)/2 (\(N_{f}=10^{6}\))** & **Ref.** \\  & **(rpm)** & **(mm/min)** & **(mm)** & **Ratio, R** & **A\(\sigma\)/2 (\(N_{f}=10^{6}\))** & **Ref.** \\ \hline A356-T6 & 500/1000 & 150 & 5 & \(-1\) & 155/130 & [PERSON] et al. [39] \\ AA5083-H321 & - & - & 6 & 0.1 & 82.5 & [PERSON] et al. [40] \\
5083-O & - & - & 6 & 0.1 & 102 & [PERSON] et al. [41] \\
2024-T3 & 2400 & 240 & 1.6 & 0.1 & 159.2 & [PERSON] et al. [42] \\
2014A-T6 & - & - & 6 & 0.1 & 47.77 & [PERSON] et al. [41] \\
6013-T6 & 2000 & 208 & 1.6 & 0.1 & 111.88 & Magnusson and Kallman. [43] \\ A60N1-T5 & & & 12 & 0.1 & 91.18 & Kawasaki. [44] \\
7475-T76 & 950 & 110 & 2 & 0.1 & 115.58 & Magnusson and Kallman. [43] \\ AA6082-T6 & 2500 & 1400 & 4 & 0.5 & 51.51 & Ericsson and Sandstro [45] \\
5083-H321 & 500 & 80 & 8 & \(-1\) & 144.92 & [PERSON] and [PERSON] [46] \\ AA5083-H3214 & - & 450 & 5 & 0.1 & 94.34 & Pocaterra and Tovo [47] \\ ALUSTRA-H321 & - & 350 & 5 & 0.1 & 70.08 & Pocaterra and Tovo [47] \\ AA6082-T5 & - & - & 5 & 0.5 & 53.02 & [PERSON] [48] \\
6005A & 2100 & 1000 & 4.5 & 1 & 105 & [PERSON] et al. [52] \\
5024-H116 & 1200 & 720 & 3.3 & \(-1\) & 180 & [PERSON] et al. [50] \\ AA2195-T8 & 800 & 54 & 5 & 0.1 & 185 & [PERSON] et al. [51] \\ AA2198-T851 & 1000 & 80 & 4 & 0.33 & 178 & [PERSON] et al. [52] \\ AA6082-T6 & 1500 & 300 & 4 & \(-1\) & 170 & [PERSON] et al. [53] \\
6061 & 1000 & 80 & 2 & 0.3 & 38 & [PERSON] et al. [24] \\
7050-T7451 & 800 & 150 & 12 & \(-1\) & 202 & [PERSON] et al. [54] \\
2024-T4 & 800-1000 & 150–250 & 4 & 0.1 & 73.71 & [PERSON] et al. [55] \\ \hline \hline \end{tabular}
\end{table}
Table 1: A summary of fatigue test data obtained in the literature.
## 3 Results and Discussion
### Analysis of Clad Rebar End-Seal Technology  
To ensure that the end face of the clad rebar can be effectively protected against corrosion, three types of clad rebar end-sealing techniques are proposed in this paper, namely weld sealing, epoxy sealing, and sleeve sealing. The welding seal was welded by means of argon arc welding with a wire material of 316L, a welding current of 210 A, and a welding voltage of 23 V. The epoxy resin seal was applied by hand; two coats of resin were applied 6 h apart. After sealing the steel ends, they were placed in a corrosion chamber and subjected to a salt spray corrosion test for 30 d. At the end of the experiment, the steel was soaked to remove the rust.  
Figure 17 shows the shape of the end face of the rebar after rust removal. Figure 17a shows that after 30 days of corrosion, most of the epoxy resin on the end face of the rebar had peeled off, indicating that the adhesion of the epoxy resin is poor under hot and humid conditions, and cannot provide long-term effective protection of the end face of the rebar. Figure 17b shows the end profile of the sleeve seal. A large amount of crevice corrosion occurred at the sleeve joint, which was caused by the presence of receding grooves on the surface of the reinforcement, preventing an effective seal from being formed between the sleeve and the reinforcement. Figure 17c shows the shape of the end face without the seal treatment. As can be clearly seen, severe lamellar corrosion occurred at the end face, and corrosion pits of various sizes formed. Surprisingly, no obvious pits or rust pits were found at the end face of the welded seal (Figure 17d), indicating that the end face had good corrosion resistance after the seal was welded.  
Figure 18 shows a diagram of the corrosion depth on the end face of the reinforcement. The corrosion depth of the end faces decreases as follows: unsealed end face  
Figure 16: Shape of the bent specimen post-cut: (**a**) forward-bent shape; (**b**) reverse-bent shape.  
Figure 17: End profiles of reinforcement after desaling: (**a**) end profile with epoxy seal; (**b**) surface profile at sleeve joint; (**c**) end profile without seal treatment; (**d**) end profile with welded seal.  
(2.96 mm) > epoxy-sealed end face (1.92 mm) > thread-sealed end face (0.68 mm) > well-sealed end face (0 mm). Special attention needs to be paid to the fact that the composite interface is more seriously corroded than the carbon steel base, the reason for which is that there will be electrochemical corrosion on the composite interface in the conditions of high humidity, heat, and salt spray while, under the same circumstances, the rate of electrochemical corrosion will be much higher than that of chemical corrosion.
# Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant
## Abbreviations  
The following abbreviations are used in this manuscript:  
ARIMA Autoregressive Integrated Moving Average  
ARR Artificial Neural Network  
BOG Boil-Off Gas  
FIR Fuzzy Inductive Reasoning  
FT Failure Threshold  
LNG Liquefied Natural Gas  
LSTM Long Short-Term Memory  
MARS Multivariate Adaptive Regression Splines  
MILP Mixed-Integer Linear Programming  
MLP Multilayered Perceptron  
MLR Multiple Linear Regression  
NG Natural Gas  
NVT Tank level  
PHM Prognosis and Health Management  
PLC Programmable Logic Controller  
PPR Rapid Pressure Setting  
PT Tank pressure  
QIC NG consumption  
RUL Remaining Useful Life  
SC-SVR Structure-Calibrated Support Vector Regression  
SSLNG Small-Scale LNG
# Automatic Detection of Whistler Waves in the Top-Side Ionosphere: The WhISPER Technique
## 2 Data and Methods  
In the present study, we used data from CSES-01, the first satellite of the CSES (China Seismo-Electromagnetic Satellite) constellation. The CSES mission is a collaboration between the China National Space Administration (CNSA) and the Italian Space Agency (ASI), which aims to obtain global data of the electromagnetic field, plasma, and energetic particles in the top-side ionosphere and inner Van Allen radiation belts [31].  
CSES-01 was launched on a Sun-synchronous quasi-polar orbit (with an altitude of 507 km) on 2 February 2018 [32]. Its payloads include a plasma analyser package (PAP), a Langmuir probe (LAP), the High-Energy Particle Package (HEPP) and High-EnergyParticle Detector (HEPD), a tri-band beacon (TBB), a search coil magnetometer (SCM), an electric field detector (EFD), a high-precision magnetometer (HPM), and a global navigation satellite system occultation receiver (GOR). All payloads are planned to work continuously in the region between \(-65^{\circ}\) and \(65^{\circ}\) of latitude [32]. The present study is based on EFD and SCM vector data. The observation data are organized into ascending (nocturnal) and descending (diurnal) orbits separately, with each half orbit (hereafter semiorbit) lasting approximately 34 min.
## 2 Materials and Methods
### Design of the DC-AC Controller
#### 2.3.3 Active and Reactive Power Control  
For power control, a rotating reference frame is used. Figure 15 shows the stationary and rotating reference frames obtained by performing a \(dq\) or Park transformation. The subscripts \(d,q\) are components in the rotating reference frame, and \(\alpha,\beta\) are components in the stationary frame of reference. The term \(\omega=\dot{\theta}\) is the angular frequency of the grid [29].  
In the rotating reference frame \(dq\), the active output power of the inverter [8] can be determined as:  
\[i_{d}^{*}=\frac{2P_{ref}}{V_{d}}, \tag{61}\]  
\[i_{q}^{*}=-\frac{2Q_{ref}}{V_{d}}. \tag{62}\]  
To guarantee that the maximum active power is always injected into the grid, we define \(P_{ref}=P_{max}\) (MPP), assuming that there are no losses [8]. However, the reference voltage \(V_{ref}\) of the DC bus can be changed to regulate the active power supplied to the grid.  
The current reference in the rotating reference frame, \(i_{d}^{*}\) and \(i_{q}^{*}\), is transformed into the stationary reference frame by using the inverse Park transformation. Furthermore, these variables are regulated by the current control. The inverse Park transform (\(dq\Rightarrow\alpha\beta\)) is given by:  
\[\begin{bmatrix}i_{a}\\ i_{\beta}\end{bmatrix}=\begin{bmatrix}\cos(\theta)&-\sin(\theta)\\ \sin(\theta)&\cos(\theta)\end{bmatrix}\begin{bmatrix}i_{d}\\ i_{q}\end{bmatrix}. \tag{63}\]  
In this case, only the real signal needs to be generated, that is, \(i_{a}\). Therefore, the current reference given by the active and reactive power control is:  
\[i_{PQref}=i_{d}^{*}\cos(\theta)-i_{q}^{*}\sin(\theta), \tag{64}\]  
where \(\theta\) is the voltage angle of the grid obtained by the PLL.