esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 BCNBLMATT Model Building
### BiLSTM-Multi-Heads Attention Layer  
The Bidirectional Long Short Term Memory neural network model (BiLSTM) is a combination of forward LSTM and backward LSTM. Although the LSTM mode [29] can capture long-distance dependencies, it cannot encode information from back to front. Therefore, the BiLSTM model is proposed to perform bidirectional processing on sequence data to capture the bidirectional semantic dependencies of sentences, and it can be used to extract the contextual information from text sequences. In order to capture the more important features in sentences, this article adopts a multi-head attention mechanism for key information extraction. The multi-head attention mechanism divides the model into multiple heads, performs multiple independent attention calculations [28], focuses on different aspects of information, and learns more key feature information. It can focus on more positional information and improve the model's expressive ability. As shown in Figure 4, This article inputs the text vector Tn obtained after the Bert-base-chinese training into BiLSTM for the forward and reverse processing of the input sequence. The two processed feature vectors are concatenated as the output sentence feature vector hn, which contains all the information of the sentence in both forward and backward directions. Then, hn is input into the multi-head attention mechanism model for multiple sets of attention processing to obtain the key feature information from different angles. Finally, the features obtained from each group are concatenated and a linear transformation is performed to obtain the final high-level feature vector F2 of the text.  
Figure 3: TextCNN model.  
The formula is as follows:  
\[\begin{cases}\overset{\rightarrow}{h_{i}}=\overrightarrow{LSTM(T_{i})}\\ \overset{\leftarrow}{h_{i}}=\overleftarrow{LSTM(T_{i})}\\ \overset{\leftarrow}{h_{i}}=\left[\overset{\rightarrow}{h_{i}},\overset{ \leftarrow}{h_{i}}\right]\end{cases} \tag{3}\]  
Among them, \(Ti\) is the vector of the \(i\)-th word, \(\overset{\rightarrow}{h_{i}}\) represents the forward output of the \(i\)-th word \(LSTM\) and \(\overset{\leftarrow}{h_{i}}\) represents the reverse output of the \(i\)-th word \(LSTM\). The two are concatenated as the output value of the \(i\)-th word BiLSTM.  
\[\begin{cases}Q_{i}=QW_{i}^{Q},W_{i}^{Q}\in R^{l\times dk}\\ K_{i}=KW_{i}^{K},W_{i}^{K}\in R^{l\times dk}\\ V_{i}=VW_{i}^{V},W_{i}^{V}\in R^{l\times dk}\end{cases} \tag{4}\]  
\[head_{i}=Attention(Q_{i},K_{i},V_{i})=softmax\left(\frac{Q_{i}K_{i}}{\sqrt{dk}} \right)V_{i} \tag{5}\]  
\[F_{2}=Multihead=concat(head_{1},head_{2},\ldots,head_{n})W^{o},W^{o}\in R^{l \times l} \tag{6}\]  
where \(i\) represents the \(i\)-th attention, with \(n\) attention heads, \(i\) = 1,..., \(n\), and \(l\) is the word vector dimension, \(dk\) = \(l\)/\(n\). Its existence is to shrink the value to avoid an excessive inner product value, \(W_{i}^{Q}\), \(W_{i}^{K}\), \(W_{i}^{V}\), and \(W^{o}\) are the weight matrices, and \(concat\) represents the concatenation operation.
# Detecting IoT Attacks Using an Ensemble Machine Learning Model
## 5 Results and Analysis
### Fog Layer Result Analysis
#### 5.2.1 Performance Measures  
Performance measures such as kappa, F-measure, and ROC indicate how well the model performs in the fog layer. Figure 9 illustrates that all performance indicators in the selected model are almost equal and at the top. The values are 96.39, 98.20, 99.60, and 96.40 for kappa, F-measure, ROC, and MCC, respectively.
## 4 Jasmonates: Biosynthesis, Signaling, and Functions
### Deeper Mechanistic Insights: How MeJA Modulates Trichome Development and Cannabinoid Biosynthesis
#### 4.2.3 Role of Epigenetics and Post-Translational Modifications  
Emerging evidence from other plant systems indicates that JA-related transcription factors may recruit or be influenced by histone modifiers, such as H3K9 acetylation or methylation, thereby leaving a transcriptional \"memory\" of MeJA treatment [52]. In _Arabidopsis thaliana_, MeJA treatment has been shown to induce genome-wide changes in histone acetylation and methylation, particularly H4 Ac and H3K27 me3, leading to a transcriptional memory that primes plants for enhanced stress responses [53]. Similarly, in _Nicotiana benthamiana_, MAPK-mediated phosphorylation of WRKY transcription factors increases their DNA-binding affinity, activating defense-related genes that are critical for the plant's immune response [54]. If a similar mechanism applies to cannabis, certain genes could remain primed for rapid activation during subsequent stress or hormone exposure, although data for cannabis remains scarce. Additionally, phosphorylation of MYC factors by MAP kinases could alter their stability or DNA-binding affinity, adding yet another layer of regulation to the MeJA-trichome developmental cascade [55]. These epigenetic and post-translational controls underscore the complexity of hormonal crosstalk and illustrate why different cannabis cultivars may exhibit varying responses to MeJA.
# Research on Phase Stabilization Algorithm of Femtosecond Timing System
## 4 System Algorithm Design
### Automatic Control Algorithms  
We employ a PID algorithm as the core automatic control mechanism in our system, leveraging its longstanding reputation in control engineering for robustness, reliability, and straightforward implementation. Given the demands for high precision in timing systems, particularly in applications like particle accelerators, the PID controller is particularly advantageous due to its ability to automatically fine-tune control parameters. This adaptability ensures stable system outputs, rapid responses, and precise regulation, thereby enhancing overall performance. The design of the PID controller integrates proportional, integral, and derivative strategies to optimize output adjustments.  
Figure 6: Function test.
## 3 Materials and Methods  
### Plant Material and EO Extraction  
In this study, A 10-year-old cultivated field with Lavender plants (genotype, _Lavandula angustifolia_ cv. Etherio) was used and analyzed. The genotype described by [37] is located at Kato Scholari, Greece and is owned by the company Etherio (Eratea, Kozani, Greece). The EO chemical composition was evaluated on 22 July 2021 when fully blooming plants were harvested. The samplings were made at 9:00, 12:00, 15:00 and 18:00, as described by [23]. Three random samples were taken per treatment. The ambient temperature during the day was 24 \({}^{\circ}\)C in the morning, 29 \({}^{\circ}\)C at noon, 35 \({}^{\circ}\)C at 15:00 and 32 \({}^{\circ}\)C at 18:00. A Clevenger water steam distillation apparatus was used to extract lavelnder EO. Approximately 500 g of freshly collected flowers without inflorescence stalks (only the blooming heads, approximately 4 cm long) were weighed and put into the top flask. The distillations protocol was: duration: 1.50 h, steam flow: 0.8 L min\({}^{-1}\), distillatory pressure: free flow, steam temperature: 110 \({}^{\circ}\)C.
# Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods
## 3 Materials and Methods
### Tweet Pre-Processing Phase  
Before using tweets as an input in our Neural Network models, the tweets needed pre-processing. Tweet pre-processing included:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Neural Network Type** & **Number of Hidden Layers** & **Layer Sizes** & **Activation Functions** \\ \hline Simple Neural Network (Binary Classification) & Typically 1–2 & Varies, depends on problem complexity & Hidden: ReLU, Output: Sigmoid \\ \hline Convolutional Neural Network (CNN) & Multiple convolutional and pooling layers, followed by fully connected layers & Convolutional layers: filter size determines neuron count, fully connected layers: variable & Convolutional: ReLU, Output: Sigmoid or Softmax (depending on task) \\ \hline Recurrent Neural Networks (RNN) & One or more recurrent layers & Number of recurrent units (neurons) per layer & tanh or ReLU \\ \hline Long Short-Term Memory (LSTM) & Multiple layers of memory cells & Number of memory cells (neurons) per layer & Specialized within memory cells (sigmoid, tanh) \\ \hline Gated Recurrent Unit (GRU) & Multiple layers possible & Number of gated units (neurons) per layer & Specialized gating mechanisms (sigmoid, tanh) \\ \hline \hline \end{tabular}
\end{table}
Table 4: A brief overview of the specific architectural features of each type of Neural Network.  
Figure 2: Binary and multi-class datasets.  
* Removing unnecessary sections of tweets (external links and usernames (signified with @sign), URL (http://...), stop words, #tags, retweets (starts with \"RT\"), punctuations, unnecessary whitespaces, etc.);
* Transforming characters to lowercase;
* Removing numbers;
* Correcting spelling/writing errors (normalization) and restoring popular abbreviations to their full forms (e.g., mrb to merhaba). ITU Turkish NLP Web Service API [7] was used for the normalization process.  
We developed a tweet pre-processing program with Python, which processed the tweets as shown in Figure 3.
## 8 Conclusions (with Elements of Results Analysis)
A particular case at \(\lambda=0\) was examined in [1; 3]. The analytical expressions for the complex dielectric permittivity (CDP) were obtained with accuracy \(\lambda=1\) on the set \(\Omega_{3}=\{\omega;2\omega;3\omega\}\) in a quadratic field approximation. They reflect the non-linear polarization effects of the third order in field frequency, which can serve as a theoretical basis for creating microwave generators by tripling the frequencies of the radio range. 5. Based on the expression for dielectric polarization at the fundamental frequency of the alternating polarizing (external) field \(\rm P^{(\omega)}(t)\) (see formula (8)) the generalized nonlinear analytic expressions are constructed (38). They allow us to examine and numerically calculate theoretical frequency and temperature spectra of the complex dielectric constant of the dielectric at the main frequency of the polarizing field, taking into account the nonlinear polarization effects caused by the effects on polarization of the spatially heterogeneous electric field induced in the crystal during volume-charge polarization (in the region of high temperatures) or due to the tunnel movement of ions (primarily protons), in low-temperature quantum polarization (in the low-temperature region) in the hydrogen-bonded crystals (HBC). Formally, according to the spectral expressions (38), the nonlinear polarization effects in the dielectric appear in the form of additional terms reflecting the interactions the of relaxation modes with different (by the Fourier order number of the harmonic of the bulk charge density) relaxation times \(T_{\rm n}\) at the fundamental frequency of the external field (\(\omega\)). In this paper, the relaxation \(\Gamma_{1}^{(\omega)}(\Gamma)\), \(\Gamma_{2}^{(\omega)}(\Gamma)\) parameters are introduced for the first time. These parameters are dimensionless values written in the form of converging number series in the functions of the frequency of the alternating polarizing field and temperature (33a) and, further, converted to analytical expressions (48) calculated together with (50), (51) in the functions of dimensionless parameters \(\alpha_{1}=\frac{T_{\rm D}}{T_{\rm M}}\), \(\alpha_{2}=\omega T_{\rm D}\). Further studies of the expressions (50), (51), for particular cases of diffusion relaxation over the wide range of field frequencies (\(\alpha_{1}=\frac{T_{\rm D}}{T_{\rm M}}<<1\), \(\alpha_{2}=\omega T_{\rm D}>0\)) in the form of (52), (53), and for the case of mixed type relaxation in the low frequency region of the field (\(\alpha_{2}=\omega T_{\rm D}<<1\)) in the form of (56), (57) confirms both a high degree of mathematical accuracy in justifying the expressions (51), (52), and the physical meaning laid down in the preceding equalities (33a). 6. In parallel with the results indicated in paragraph 5 of the conclusions, based on the expression \(\frac{T_{\rm D}}{T_{\rm M}}=\left(\frac{\rm d}{\Gamma_{\rm D}}\right)^{2}\), we introduce the Debye screening length \(r_{\rm D}=\rm d\sqrt{\frac{T_{\rm M}}{T_{\rm D}}}\).
# Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link
## 5 Physical Layer Proposal
### Frame Structure  
Four different frame structures are defined to satisfy the needs of the physical layer definition of the remote sensing sounder: (i) a frame for narrowband sounding (Figure 12a); (ii) a frame for wideband sounding (Figure 12b); (iii) a frame for High Robustness Mode (HRM) data transmission (Figure 12c) and (iv) a frame for High Throughput Mode (HTM) data transmission (Figure 12d).
###### Abstract  
A high damping rubber bearing (HDRB) is widely utilized in base-isolation structures due to its good energy dissipation capacity and environmentally friendly properties; however, it is incapable of isolating the vertical vibration caused by earthquakes and subways effectively. Thick rubber bearings with a low shape factor have become one of the important vertical isolation forms. This paper provides an experimental comparative study on high damping rubber bearings with low shape factor (HDRB-LSF), thick lead-rubber bearings (TLRB), and lead-rubber bearings (LRB). The abilities of the bearing and energy dissipation of the above bearings are analyzed contrastively considering the influence of vertical pressure, loading frequency, shear strain, and pre-pressure. Firstly, the HDRB-LSF, TLRB, and LRB are designed according to the Chinese Code for seismic design of buildings. Secondly, cyclic vertical compression tests and horizontal shear tests, as well as their correlation tests, are conducted, respectively. The vibrational characteristics and hysteresis feature of these three bearings are critically compared. Thirdly, a corrected calculation of vertical stiffness for the thick rubber bearings is proposed based on the experimental data to provide a more accurate and realistic tool measuring the vertical mechanical properties of rubber bearings. The test results proved that the HDRB-LSF has the most advanced performance of the three bearings. For the fatigue property, the hysteresis curves of the HDRB-LSF along with TLRB are plump both horizontally and vertically, thus providing a good energy dissipation effect. Regarding vertical stiffness, results from different loading cases show that the designed HDRB-LSF possesses a better vertical isolation effect and preferable environmental protection than LRB, a larger bearing capacity, and, similarly, a more environmentally friendly property than TLRB. Hence, it can avoid the unfavorable resonance effect caused by vertical periodic coupling within the structure. All the experimental data find that the proposed corrected equation can calculate the vertical stiffness of bearings with a higher accuracy. This paper presents the results of an analytical, parametric study that aimed to further explore the low shape factor concepts of rubber bearings applied in three-dimensional isolation for building structures.  
isolation; high damping rubber bearing; low shape factor; performance test; experiment violation. +
Footnote †: journal: Applied Sciences
## 3 Methods
### InSAR Processing  
In this study, the PS-InSAR method in the ENVI SARScope software (Version: 5.6) was used to retrieve temporal deformation of the study area based on Sentinel-1A images. The PS-InSAR method was first proposed by [PERSON] et al. [PERSON] et al. (2019). Although various algorithms for PS-InSAR techniques have been developed in recent years, all of them aim at retrieving localized deformations on highly coherent scatterers (i.e., persistent scattering (PS) pixels, e.g., buildings or bare rocky objects) from the contained differential interference phase.  
In the PS-InSAR interferometric model, the phase at any point can be expressed as:  
\[\phi=\phi_{d\phi_{0}}+\phi_{atmo}+\phi_{\text{topo}}+\phi_{\text{geo}}+\phi_{ noise} \tag{1}\]  
where \(\phi\) represents the interferometric phase, \(\phi_{d\phi_{0}}\) represents the deformation phase of the surface line of sight, \(\phi_{atmo}\) represents the atmospheric disturbance phase, \(\phi_{\text{topo}}\) represents the terrain phase, \(\phi_{\text{geo}}\) represents the flat phase, and \(\phi_{noise}\) represents the thermal noise phase of the system.  
By using the PS-InSAR technique in the ENVI SARScape software, the SAR image acquired on 4 January 2021 was selected as the master image, and the other images were co-aligned with the master image. All images were aligned to the master image coordinate system using a DEM to remove the terrain leveling effect. Then, we used a linear model to estimate the deformation rate and residual elevation information in all differential interferograms and calculated the pixels with high coherence. Then, the estimated target elevation was estimated, and the deformation rate in the LOS direction and the atmospheric compensation was generated again with the deformation results. Finally, all the steady point scattering points (PS points) were geocoded and transformed to the corresponding positions. PS-InSAR technique is quite mature in urban surface monitoring, and its data confidence meets the experimental requirements.
# The Influence of Interlock Loss between Rebar and Concrete on Bond Performance of RC Member
## 2 Outline of the Loading Experiment for Beams with Different Interlock Conditions
### Specimens  
Table 1 shows the list for the specimens of six RC beams. In this test, the authors focused on the interlock effects on the load-carrying mechanism in the bond components. For this purpose, the interlock area was controlled, although chemical adhesion and friction resistance were kept intact in all cases.  
The specimen with 50% interlock area had no cover concrete in the loading span, and the concrete was in contact with only the top half surface of the main rebars, and the bottom half side was exposed. The specimen with interlock area of 0% used a round PC rod. Two series of specimens, the ST series with stirrup and the PL series without stirrup, were set for beams with three different interlock areas. The geometry and the rebar arrangement of the specimens are shown in Figure 1. For the main rebars of the specimens, high strength threaded rebars were used in the cases of 100% and 50% interlock area cases, and smooth  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Name** & **Area with Interlock** & **Stirrup** & **Outline of Experimental Specimens** & **Remark** \\ \hline ST-100 & 100\% & Placing & & \\ \hline PL-100 & 100\% & Not placing & & \\ \hline ST-50 & 50\% & Placing & & \\ \hline PL50 & 50\% & Not placing & & \\ \hline ST-0 & 0\% & Placing & & \\ \hline PL-0 & 0\% & Not placing & & \\ \hline \end{tabular}
\end{table}
Table 1: List of specimens.  
round PC rod was used in the case of the 0% interlock area. In the all cases, a D10 bar whose yield strength was 305 MPa was used for the stirrup and compression bars. Beam specimens were designed for failing in shear with tensile rebar ratio of 1.95%, effective height of 230 mm, shear span of 600 mm, beam width of 220 mm, and anchorage length of 500 mm. To secure solid anchorage at the beam extremities, the main rebars were fixed by attaching nuts at the beam ends.  
The six beam specimens were casted at the same time using ready-mixed concrete with the mix proportions given in Table 2. The specimens were cured under sealed condition for 10 to 12 days until the loading test. Table 3 lists the mechanical properties of the concrete obtained from compression tests on cylinder specimens, with 39.7 MPa of compressive strength. Table 3 also shows the main rebar property: 710 MPa for threaded bar and 1189 MPa for PC rod of yielding strength.
# Egyptian Hieroglyphs Segmentation with Convolutional Neural Networks
## 6 Conclusions  
In this work, we analyze the properties of the Detectron2 framework as a tool to face the problem of ancient Egyptian hieroglyphs segmentation. A pre-trained model was employed and finely tuned by using an ad-hoc dataset constructed on images taken from different sources. Hieroglyph segmentation is a difficult task due also to the variety of the signs and the possible corruption of symbols caused by millennial ageing. Notwithstanding these adversities, in our experimental tests, we have shown that a CNN-based approach may be a good candidate to solve the segmentation problem, with promising quantitative results on the validation set and qualitatively fine results on images taken \"from the wild\". Segmentation performance is limited by several issues inherent to the quality and amount of the raw available data, for which possible countermeasures--such as using data augmentation or image enhancement techniques--still need to be investigated.  
Conceptualization, A.B. and T.G.; methodology, F.A. and T.G.; software, T.G., M.F. ([PERSON]) and L.P.; validation, F.A., A.B. and C.C.; formal analysis, F.A., A.B., T.G., M.F. ([PERSON]) and L.P.; investigation, C.C. and M.F. ([PERSON]); resources, A.B. and F.A.; data curation, C.C., M.F. ([PERSON]), T.G., M.F. ([PERSON]) and L.P.; writing--original draft preparation, T.G., M.F. ([PERSON]) and L.P.; writing--review and editing, F.A., A.B. and T.G; visualization, C.C.; supervision, F.A. and A.B.; project administration, A.B.; funding acquisition, F.A. and C.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Part of the images composing the dataset used in this study are available here: [[http://iamai.nl/downloads/GlyphDataset.zip](http://iamai.nl/downloads/GlyphDataset.zip)]([http://iamai.nl/downloads/GlyphDataset.zip](http://iamai.nl/downloads/GlyphDataset.zip)) (accessed on 14 December 2022) (for data from the Pyramid of Unas), here: [[https://www.metmuseum.org](https://www.metmuseum.org)]([https://www.metmuseum.org](https://www.metmuseum.org)) (accessed on 14 December 2022) (for the MET), and here: [[https://www.museoegizio.it](https://www.museoegizio.it)]([https://www.museoegizio.it](https://www.museoegizio.it)) (accessed on 14 December 2022) for the Egyptian Museum of Turin. Some of the images used are under copyrights of the authors and can not be shared.  
**Acknowledgments:** The authors are grateful to the Museo Entologico Missionario di Fiesole (Florence, Italy), Dott. [PERSON], to the Egyptian Museum of Turin and to the MET of New York for providing access to their Egyptian collection of artifacts.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 10: Example of critical segmentation results (on Cyperus papyrus, from the Egyptian Museum of Turin online collection, _Book of the Dead of Nehhepet, scribe of the necroolis,_ cat. number 1768): on the left the original document, on the right the segmented one.