esa-sceva/satcom-chunk-collection huggingface dataset:
# A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data
## 3 Material and Methods
### Sentibert  
BERT [29] is an attention-based language model that utilizes a stack of Transformer encoders and decoders to learn textual information. It also uses a multi-headed attention mechanism to extract useful features for the task. The bidirectional Transformer neural network, as the encoder of BERT, converts each word token into a numeric vector to form a word embedding, so that words that are semantically related would be translated to embeddings that are numerically close. BERT also employs a mask language model (MLM) technique and a next sentence prediction (NSP) task in training to capture word-level and sentence-level contextual information. BERT and its variants have been applied to numerous NLP tasks such as named entity recognition, relation extraction, machine translation, and question and answering, and achieved the state-of-the-art performance. In this study, we choose a BERT variant, SentiBERT, which is a transferable transformer-based architecture dedicated to the understanding of sentiment semantics. As shown in Figure 4, SentiBERT modifies BERT by adding a semantic composition unit and a phrase node prediction unit. Specifically, the semantic composition unit aims to obtain phrase representations that are guided by contextual word embeddings and an attentive constituency parsing tree. Phrase-level sentiment labels are used for phrase node prediction. Due to the addition of phrase-level sentiment detection, a sentence can be broken down and analyzed at a finer granularity to capture more sentiment semantics.  
Figure 3: An overview of the SentiBERT-BiLSTM-CNN learning pipeline.  
Let \(\mathbf{s}=\{w_{i}|i=1,...,n\}\) denote a Tweet message with \(n\) word tokens, which are the input of SentiBERT. Our goal is to leverage the power of SentiBERT to generate sentiment-enhanced word embeddings, which can be denoted by \(\mathbf{e}=\{e_{i}=\text{SentiBERT}(w_{i})|i=1,...,n\}\). In this study, each Tweet should have no more than 64 tokens; the Tweets with less than 64 tokens are padded, namely, \(n=64\). Reference [29] experimentally showed that the output of the last four hidden layers of BERT encodes more contextual information than that of the previous layers. To this end, we also chose a concatenation of the outputs of the last four hidden layers as the word embedding representation.
# An Indoor Collaborative Coefficient-Triangle APIT Localization Algorithm
###### Abstract  
The Approximate Point-In-Triangulation (APIT) localization algorithm is a widely used indoor positioning technology due to its simplicity and low power consumption. However, in practice, In-to-Out misjudgments exist regularly in APIT, and a considerable amount of nodes cannot be positioned due to the low node density. To tackle this issue, a Collaborative Coefficient-triangle APIT Localization (CCAL) algorithm is proposed. Firstly, an effective triangle criterion is put forward to reduce the probability of In-to-Out misjudgment and reduce the computational complexity. Then, a further Received Signal Strength Indicator (RSSI) location and weighted triangle coordinate calculation method is adopted to reduce the positioning error. Meanwhile, the idea of iterative collaborative positioning of the positioned unknown nodes is introduced to remarkably expand the localization coverage rate. Simulation results show that the proposed algorithm outperforms APIT, RSSI, and other improved algorithms in terms of both node location error and localization coverage rate.  
Keywords:indoor positioning; In-to-Out misjudgment; coefficient-triangle APIT location; collaborative communication +
Footnote †: journal:
# Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications
## 2 Imaging Modalities of Electrical Impedance Tomography (EIT)  
EIT image reconstruction techniques can be divided into three sub-categories, frequency difference, absolute imaging, and time difference. All these techniques are reliant on a computer-based model (called the mesh) of the subject under analysis, that is used to simulate the estimated electric potential distributions for the injected current patterns. A Jacobian matrix is required for all the reconstruction techniques that are calculated depends upon the forward solutions. The Jacobian matrix indicates the variations in conductivity to fluctuations in the measured voltages by linearizing the computer model and notifies the imaging algorithms of the search direction. The image results will be good if the mesh is closer for computation.
## 5 Experimental Setup and Results Discussion
### IMA Localization Test  
In the previous experiment of the triangle center of gravity algorithm, we found that when the label is placed with large X and Y coordinate values, that is, when it is farther away from the origin, the intersection of the three circles will become narrower and longer, and the three intersecting circles thus cannot be approximated as a triangle. If the triangle center of gravity algorithm is used to estimate the coordinates of the target object, it will be inaccurate because the triangle is too narrow and long. Therefore, we have proposed a solution to locate the intersection midpoint. As shown in Figure 12, we selected the intersection point of the red circle and the blue circle as well as the intersection point of the red circle and the green circle, and then took the midpoint as the post-positioning coordinates. This can avoid the intersection of the blue circle and the green circle. The overlapping area becomes large or narrow, and the accuracy of using the center of gravity positioning method becomes poor.  
The experimental results show that when the positioning coordinates are (7, 5), the calculated positioning coordinates are (7.09, 5.10), which is converted into an actual error of about 8.57 cm; when the positioning coordinates are (10, 7), the positioning coordinates are (10.18, 7.15), and the actual error is about 14.48 cm; when positioning the coordinates (11, 5), the positioning coordinates are (11.10, 5.04), which translates into an actual error of about 6.93 cm, and there are 10 coordinate points in the positioning space. The detailed experimental results are shown in Table 1. It can be seen from the table that the positioning errors are almost all quite small, which significantly improves the positioning accuracy.
# Portable X-ray Fluorescence Analysis of Organic Amendments: A Review
## 3 Results and Discussion
### Determination of Elemental Content with pXRF
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample Type (Number of Samples)** & **Sample Preparation** & **Instrument** & **Mode\({}^{2}\)Measurements** & **Elements Studief/Determined\({}^{2}\)** & **Ref.** \\  & & & & Conditions & & \\ \hline Manure compost (70) & field moisture & Alpha Series Handheld (Innov-X & 5 min scan & Cu, As, Zn (R\({}^{2}>0.80\))), Mn, Fe, Ca & [42] \\  & Systems, Webborn, M\({}_{\rm{AA}}\), USA & & (0.6\(>R^{2}>0.51\)), K (R\({}^{2}=0.14\)) & \\ \hline Compound from several & moist and dried & Delta Premium DP-0400 (Olympus & Soil, 90 s \(\times\)3, repositioning & As, Ca, Cd, Cr, Cu, Fe, K, Mn, Ni, P, Pb, Zn & [28] \\  & & Innov-X, Webborn, MA, USA) & Soil, 90 s \(\times\)3, repositioning & As, Ca, Cd, Cr, Cu, Fe, K, Mn, Ni, P, Pb, Zn & [28] \\ \hline Biosolids (128) & freeze, dried, and ground & Delta 400 (Olympus & Innov-X, & Soil, CRM calibration, Cd, Cr, Cu, Fe, Hg, Mo, Ni, Pb, Sb, Se, Sn, Zn & [39] \\ \hline Topsoil (183) & & 5-1 Turo (Roxar Nano), & Soil 120 s \(\times\)2 & As, Ca, Cr, Cu, Fe, K, Mn, Ni, Pb, Sc, Ti & [43] \\  & 0.87–96.3\% SOM \({}^{3}\) & Dried & & & \\ \hline Liter (48) & dried and ground & Trace III-V (Roxar Nano, & Flooding or not the system & & \\  & & Berlin, Germany) & with high-purity helium & Ca, K, Fe, Mg, Mn, Na, P, S, Al & [44] \\ \hline  & & DP-6000 (Olympus, Waltham, & Soil, 90 s \(\times\)3, repositioning & Ca, Cd, Cr, Cu, Fe, K, Mn, Pb, Rh, Sc, Ti, V & [30] \\  & & & MA, USA) & Soil, 90 s \(\times\)3, repositioning & K, Ca, Ti, V, Cr, Mn, Fe, Cu, Zn, Sc, Ti & [31] \\ \hline  & & & & \\ \hline Waste products & moist and dried in dip-lock & Nitrogen X1490s, (Thermo Scientific, & Mining, 80 s \(\times\)3 \(\times\)8 scan points & As, Ca, Cd, Cr, Cu, Fe, K, Mn, Pb, V, Zn & [32] \\  & bags & Bilberts, MA, USA) & & & \\ \hline Organic amendments & & TRACER (Rexher Nano, & Plant and soil calibrations with & Ca, Fe, K, Mg, Mn, P, S, Zn & [34] \\  & & Berlin, Germany) & EasyCal software, 90 s & & \\ \hline  & & & & & \\ \hline  & & & & & \\ \hline  & & & & & \\ \hline  & & & & & \\ \hline  & & & & & \\ \hline  & & & & \\ \hline  & & & & \\ \hline  & & & & \\ \hline  & & & & \\ \hline  & & & & \\ \end{tabular} \({}^{1}\) Mode refers to the pre-calibrated procedure in the instruments.\({}^{2}\) Elements with good correlation/well quantified are in bold. \({}^{3}\) SOM: Soil organic matter.  
\end{table} 
Table 2: Details of the studies that make use of pNRF in organic amendments and soils rich in organic matter.
## 2 Descriptions of the DSDC  
### Working Principle  
As shown in Figure 1a, the diaphragm compressor (DC) operates by driving hydraulic oil through piston movement, which induces diaphragm deformation to alter gas chamber volume and achieve gas compression within the chamber. The diaphragm physically separates the gas chamber from the oil chamber. Meanwhile, the gas-side and oil-side chambers of the diaphragm head assembly feature specialized cavity profiles to constrain diaphragm movement. As shown in Figure 1b, the dual-stage diaphragm compressor incorporates two distinct diaphragm heads driven by a crank-connecting rod mechanism, enabling sequential gas compression through first-stage and second-stage diaphragm compressors to attain the target pressure. Typically, the traditional single exponential diaphragm cavity profile (SEP) is mathematically described as  
\[h_{a}(r)=\frac{H}{Z_{a}-1}\bigg{[}2\Big{(}\frac{r}{R}\Big{)}^{Z_{a}+1}-(Z_{a}+1 )\Big{(}\frac{r}{R}\Big{)}^{2}+(Z_{a}-1)\bigg{]} \tag{1}\]  
where \(h_{a}\) is the traditional diaphragm cavity profile function. \(Z_{a}\) is the deflection exponent. \(H\) is the cavity depth. \(R\) is the cavity radius. \(r\) is the radial distance from any point on the circular diaphragm to its central axis.  
The complete operational cycle of the diaphragm compressor is described in Figure 2. During piston movement from the top dead center (TDC) to the bottom dead center (BDC), the oil filling pump supplies hydraulic oil to the oil chamber. When the gas chamber pressure drops below the preset suction pressure, the gas inlet valve opens, initiating the gas suction stage. As the piston moves from the BDC to the TDC, both gas and hydraulic oil pressures increase progressively, marking the compression stage. When the gas pressure exceeds the preset gas discharge pressure, the gas outlet valve opens, transitioning the diaphragm compressor into the gas discharge stage. When the hydraulic oil pressure surpasses the oil discharge threshold, the oil discharge valve opens, commencing the oil discharge stage.  
Figure 1: Geometric structures of the diaphragm compressor. (**a**) Diaphrag head. (**b**) DSDC.  
Figure 3 shows the movement characteristics of the diaphragm during different stages. As shown in Figure 3a, when the piston reaches the BDC, the diaphragm also reaches its closest position to the oil chamber surface. Typically, a residual hydraulic oil clearance is intentionally maintained between the diaphragm and oil chamber surface at this position to prevent diaphragm rupture caused by stress concentration near oil holes under oil-starved conditions. As shown in Figure 3b, when the piston moves upward from the BDC toward the TDC, the diaphragm gradually approaches the gas chamber surface. Finally, as illustrated in Figure 3c, when the piston reaches the TDC, the diaphragm contacts the gas chamber surface.
## 3 Methodology
### Results Summary  
As shown in Figure 2, after searching the literature, 255 papers were obtained (after removing 23 duplicates), referred to as the 'identification' stage in the diagram; after applying the inclusion criteria identified in Section 3.2 'Screening for Inclusion' and in the'screening' section of the diagram, 172 papers were excluded, resulting in 83 papers.  
A full-text evaluation of the papers was performed, thus excluding papers that did not match the intended focus, some that were just a review, and papers that did not have complete information; this step is represented in the figure as \"eligibility.\"  
The 48 papers remaining at the end were featured in the synthesis and were the \"included\" studies in the flowchart.  
This study focused on finding papers directly linked to identifying, detecting, and eventually automatically classifying anomalies in agricultural crops. In this context, the following papers show several examples of this process where several techniques were presented as a solution. Some datasets were also mentioned, where one can see the origin of the information in each experience presented. In addition, it takes into account that one of the main focuses of the authors in their research was to highlight the accuracy of the algorithms or models used to classify diseases or pests in crops.
# A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges
## 5 Discussion
### Future Work  
* Tailoring Adaptive Learning Journeys Using emotion recognition and behavioral analysis, the existing multimodal education systems aim to provide real-time adaptive feedback, but more studies are needed to fine-tune personalized learning paths with better use of multimodal data. For instance, [PERSON] et al. [78] developed a personalized multimodal feedback generation network that integrates multiple modal inputs to produce customized feedback on student assignments, ultimately improving learning efficiency.
* Flexibility when Deployed in Low-Resource Contexts Multimodal interaction technology frequently requires high-performance computing resources, making its deployment in resource-constrained regions difficult. [PERSON] et al. [64] highlighted the vital need to develop multimodal education systems tailored to low-bandwidth, low-computation environments, thus fostering educational equity and technological access.
* International Adaptability Additionally, existing multimodal interaction systems are typically designed within specific cultural and linguistic contexts, which may restrict cross-cultural relevance and scalability (e.g., in multicultural educational environments). Future research should focus on developing multimodal systems that are adaptable to diverse cultural contexts, ensuring broader usability and acceptance across various educational institutions.
## 3 Experiment
### Data Processing  
#### 3.2.1 Data Noise Reduction  
Because the bearing is affected by different loads, rotational speeds, and other factors during operation, the bearing usually contains a lot of noise, such as directly into the model to predict the RUL, which will affect the accuracy of prediction, so it is necessary to carry out noise reduction on the bearing signal. The author uses discrete wavelet transform for noise reduction in the signal and decomposes the bearing signal into a high-frequency part (detail coefficients, CD) and low-frequency part (approximation coefficient, CA) by selecting the wavelet base as'sym8'. The number of times this is performed is 3. The resulting CA and CD are reconstructed using the inverse wavelet transform to suppress the high-frequency components of the signal and realize noise reduction in the signal. The discrete wavelet transform decomposition flowchart is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Working Condition** & **Rotational Speed (rpm)** & **Load (N)** & **Bearing Data** \\ \hline Working condition 1 & 1800 & 4000 & 1,1,1,2,1,3,1,4,1,5,1,6,1,7 \\ Working condition 2 & 1650 & 4200 & 2,1,2,2,2,3,2,4,2,5,2,6,2,7 \\ Working condition 3 & 1500 & 5000 & 3,1,3,2,3,3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data of PHM2012.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test** & **Source Domain Data** & **Target Domain Data** \\ \hline Test 1 & bearing 1-3 & bearing 2-3, bearing 2-4, bearing 3-1, bearing 3-3 \\ Test 2 & bearing 2-3 & bearing 2-4, bearing 2-5, bearing 3-3, bearing 3-3 \\ Test 3 & bearing 3-2 & bearing 1-3, bearing 1-4, bearing 2-3, bearing 2-4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Construction of experimental dataset.  
Figure 7: PRONOSTIA experimental platform.
## 3 Results and Discussions
### PGA and Shear Strain Variation  
Figure 9 displays the depth-wise variation in the average values (red solid lines) of the PGA, developed for all the considered site profiles and applied ground motions. The patterns depicted show an overall increasing trend in the average PGA values (at the surface) compared to the peak bedrock acceleration (PBRA) values for S-1, S-3, and S-4, respectively, while a decreasing trend in the average PGA values was observed for S-2, S-5, and S-6, respectively. This can be attributed to the fact that the PGA values tended to increase as the Vs decreases. The site profiles of S-1, S-3, and S-4 did not exhibit any Vs reversals, whereas S-2, S-5, and S-6 clearly showed Vs reversals (Figure 4). This resulted in an attenuation of the PGA values as they approached the surface level.  
In addition to the occurrence of Vs reversals in the site profiles, the level of shear strains significantly influenced the soil amplification/de-amplification, as shown in Figure 10. Higher shear strain values corresponded to higher soil viscous damping, potentially leading to soil de-amplification. Furthermore, a significant observation was noted in the acceleration response with increasing PBRA, where high-intensity motions resulted in the attenuation of acceleration values. For applied PBRA values of 0.52 g (IM3), 0.65 g (IM4), and 0.73 g (IM5), despite the non-occurrence of Vs reversals in the site profiles, the results showed a decreasing trend in the acceleration values (surface) for S-1 and S-4. This occurrence of low surface accelerations for applied ground motions can be attributed to the high viscous damping of soft soils, resulting from higher induced strains. Similarly for S-2, despite the occurrence of Vs reversals, amplification was observed for the PBRA values of 0.39 g (IM1), 0.48 g (IM2), and 0.36 g (IM6), respectively. Therefore, the combination of Vs reversals and induced shear strains controlled the response of PGA values (amplification/attenuation) in the site profiles.  
Figure 10: Depth-wise shear strain variation for site profiles (S-1 to S-6), highlighting the influence of induced strains.  
Figure 9: Depth-wise PGA variation for site profiles (S-1 to S-6), highlighting amplification trends for profiles without Vs reversals (S-1, S-3, and S-4) and attenuation near the surface for those with Vs reversals (S-2, S-5, and S-6). The results emphasize the critical role of the Vs in shaping PGA behavior.
# Shunt Damping Vibration Control Technology: A Review
## 2 Fundamentals of the Piezoelectric and Electromagnetic Transducers
### Electromagnetic Transducer  
Since [PERSON] discovered electromagnetic induction in 1831, electromagnetic devices have been widely used in engineering applications. The electromagnetic transducer consists of a coil and permanent magnets. An electromotive force (emf) is produced when there is the relative motion between the coil and magnets [48]. Inversely, the electromagnetic transducer generates the output force or displacement when the external controlled current is exerted. Under this circumstance, the transducer will be an actuator. The parameter design and optimization can improve the electromechanical coupling feature of an electromagnetic transducer, which will improve the vibration control performance [49; 50]. The electromagnetic coupling coefficient can be obtained by theoretical analysis or experimental test.  
As to a permanent magnet, the magnetic moment can be represented in terms of the volume current density \(\mathbf{j}_{m}\) and the surface current density \(\mathbf{k}_{m}\) as [51]  
\[\begin{array}{l}\mathbf{j}_{m}=\
abla\times\mathbf{M}\\ \mathbf{k}_{m}=\mathbf{M}\times\mathbf{n}\end{array} \tag{6}\]  
where \(\mathbf{M}\) is the magnetization vector, \(\mathbf{n}\) is the unit vector normal to the surfaces of the magnet. Note that the volume current vanishes and some surface current densities that are normal to \(\mathbf{M}\) become zero  
Figure 2: Equivalent electrical model of piezoelectric material.  
if the permanent magnet is uniformly magnetized. The magnetic flux density at any point P outside the magnet is the sum of the magnetic flux densities \(\mathbf{B}_{pi}\) produced by each \(\mathbf{k}_{m}\),  
\[\mathbf{B}_{p}=\sum_{i=1}^{N}\mathbf{B}_{pi} \tag{7}\]  
where \(N\) is the number of \(\mathbf{k}_{m}\), and each \(\mathbf{B}_{pi}\) can be obtained by the Biot-Savart law.  
According to [PERSON]'s law, the induced emf can be obtained [52]  
\[\mathbf{V}_{e}=\int\left(\mathbf{v}\times\mathbf{B}\right)\cdot\mathrm{d} \mathbf{l} \tag{8}\]  
The electromagnetic force of the current-carrying element \(I\mathrm{d}\mathbf{l}\) can be obtained as [53]  
\[\mathbf{F}_{e}=\int I\mathrm{d}\mathbf{l}\times\mathbf{B} \tag{9}\]  
Disregarding the shape of the permanent magnet and coil, the emf and electromagnetic force can be formulated as [30; 31; 54]  
\[V_{e}=C_{e}v \tag{10}\]  
where \(C_{e}\) and \(C_{m}\) are the electromagnetic coupling coefficient, and \(C_{m}=C_{e}\). \(v\), \(I\) and \(F_{e}\) denote the relative velocity, the current flowing in the circuit, and the control force, respectively. \(C_{m}\) of the rectangle-shaped can be found in [52; 53; 55; 56], the cylinder-shaped in [48; 57], and the annular-shaped in [8; 58; 59; 60; 61].  
It is suggested by (9) and (10) that an electromagnetic transducer can be electrically equivalent to an emf \(V_{e}\), an inherent inductance \(L_{e}\) and a resistance \(R_{e}\). The electrical model is shown in Figure 3.
# Spreadsheets as Interoperability Solution for Business Process Representation
## 5 Mapping a Spreadsheet with a BPMN Model
### Requirements  
In order to make the solution widely applicable and simple to use, some assumptions were made. The following requirements need to be considered while providing a spreadsheet-based representation of a business process model:  
* Users should be able to create a process model using one of the widely available spreadsheet applications (e.g., MS Excel, Google Docs, OpenOffice Calc).
* A graphical model should be created based on a CSV file (an exported spreadsheet).
* Only one pool is considered and the term \"Who\" is used instead of swimlane to distinguish different task performers.
* Logical gates are eliminated from the model. A process should be described as a set of phases. If two or more tasks are executed in the same phase, it means that they are parallel or connected by an alternative (OR/XOR). The relationship between tasks is determined by a condition stored in a separate column (no condition--AND, different conditions--OR, conditions and \"else\" statement--XOR). Table 3 shows how logical gates can be represented.
* Loops are represented by \"goto\" rows, which link one phase to another.