esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating Visual Cryptography for Efficient and Secure Image Sharing on Social Networks
## 5 Conclusions  
With the rapid development of big data and IoT, image sharing has become an important link on social networks. However, traditional data distribution methods often have difficulty meeting the requirements of real time, high efficiency, and security. Image encryption needs to balance privacy protection with data availability. While protecting user privacy, it is also necessary to maintain sufficient quality and availability of data. This strategy facilitates the secure distribution of access credentials among diverse nodes or devices, thereby enabling nuanced and adaptable access control measures while mitigating the susceptibility to compromises. Although conventional encryption methods serve to uphold data security, they encounter challenges associated with elevated computational complexity and diminished transmission efficiency. In this paper, we combine the VC and Siamese networks to securely transmit massive sensitive data in energy-sensitive devices. The color and gray spaces provide more room for quality improvement, and the Siamese network facilitates identification performance by comparing the similarity between the secret and lossy decrypted images.  
In future work, we will explore adversarial defense based on VC, since halftoning and bit-reduction have been exploited for the detection of adversarial examples. We propose taking the halftone algorithm to bridge the adversarial robustness with the privacy protection of images. This approach not only enhances the model's robustness against adversarial samples but also protects the privacy of the images. In high-computation environments such as cloud servers, we are exploring deep learning models like diffusion models to restore the original image from the decrypted image. Last but not least, social networks face challenges in managing and protecting sensitive content. The secret-sharing mechanism provides a way to divide sensitive content into multiple shares and distribute them to different nodes, thus improving security and adaptability.  
Conceptualization, D.Z.; methodology, software, validation, and data curation, L.R.; writing--original draft preparation, D.Z.; writing--review and editing and supervision, D.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by the Key Research Platforms and Projects of Universities in Guangdong Province (Science and Technology) (XJ2023002702), and the Youth Program of Humanities and Social Sciences of the MoE (23 YJCZH291).  
Not applicable.  
The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors declare that they have no known competing financial interests or personal relationships that could appear to influence the work reported in this paper.
###### Abstract  
Green vegetation plays a vital role in energy flows and matter cycles in terrestrial ecosystems, and vegetation phenology may not only be influenced by, but also impose active feedback on, climate changes. The phenological events of vegetation such as the start of season (SOS), end of season (EOS), and length of season (LOS) can respond to climate changes and affect gross primary productivity (GPP). Here, we coupled satellite remote sensing imagery with FLUXNET observations to systematically map the shift of SOS, EOS, and LOS in global vegetated area, and explored their response to climate fluctuations and feedback on GPP during the last two decades. The results indicated that 11.5% of the global vegetated area showed a significantly advanced trend in SOS, and that only 5.2% of the area presented significantly delayed EOS during the past two decades, resulting in significantly prolonged LOS in 12.6% of the vegetated area. The climate factors, including seasonal temperature and precipitation, attributed to the shifts in vegetation phenology, but with high spatial and temporal difference. LOS was positively and significantly correlated with GPP in 20.2% of the total area, highlighting that longer LOS is likely to promote vegetation productivity. The feedback on GPP from the shifted vegetation phenology may serve as an adaptation mechanism for terrestrial ecosystems to mitigate global warming through improved carbon uptake from the atmosphere.  
vegetation phenology; climate change; gross primary productivity; carbon sequestration 2023152888. Article
## 7 Experiments and Results
### Data  
For the translation tasks (see Supplementary Materials), the study used the same parallel corpus used by [PERSON] et al. [11]. We had concatenated the Maghrebi Dialects (Moroccan Dialect, Algerian Dialect and Tunisian Dialect) together from the PADIC Corpus and MPCA Corpus [33] as well the Levantine Dialects (Jordanian Dialect, Syrian Dialect and Palestinian Dialect) are concatenated together from the same corpora. The Multitask NMT model is trained on 13,805 sentence pairs for Levantine Dialect (LD) and 17,736 sentence pairs for Maghrebi Dialect (MD). The text data was collected from social media, movies, TV shows. For the test set, the Multitask NMT model on 2000 sentence pairs for Levantine Dialect and 2000 sentence pair for Maghrebi Dialect were tested. For the POS tagging task, the dataset described by [PERSON] et al. [31]. were adopted. The adopted dataset is in-of-domain data for machine translation task and the test data used is also from the mentioned dataset. The POS dataset includes a set of 350 tweets for four major Arabic dialects (Egyptian, Levantine, Gulf and Maghrebi).  
Three cross-validation methods, that is, independent dataset test, sub-sampling (or K-fold cross-validation) test and jackknife test, are often used to evaluate the anticipated success rate of a predictor. Among the three methods, however, the jackknife test is deemed the least arbitrary and most objective [34] and hence has been widely recognized and increasingly adopted by investigators to examine the quality of various predictors [35; 36; 37; 38; 39]. However, this procedure is time- and source-consuming. Therefore, in this paper, we used K-fold cross-validation, where K set to 1 such that a single train/test split is created to evaluate our proposed model.  
In the Bi-LSTM-CRF POS tagging model, the study used the Levantine Dialect that includes 7221 words and Maghrebi Dialect that consists of 6400 words. The dataset has unstructured information that might affect the performance of the model. Therefore, a pre-processing stage has been employed for Arabic dialect and Modern Standard Arabic sentences. Punctuations, diacritics, hashtags and non-Arabic characters were removed for both AD and MSA. Also, an orthographic normalization was performed. For example, the transformation of all \(\overrightarrow{\text{\text{\textlbracki}}}\) characters to the \({}^{1}\) character was applied. Stemming or stop word removal has not been employed. The sequence length is set to 55 for the translation tasks and 25 to POS tagging task. We tokenize both MSA and AD using Python tokenizer. The modern standard Arabic contains a variety of tokens than English and Arabic Dialects and its sentences are shorter than English and Arabic Dialects. Modern Standard Arabic has more probability in its infrequent words than English and the words in the long tail tend to be morphologically complex, alternately beginning with affixes like \"AL\" \"\(\cup\)\" (the) or \"wa\" \"\(\cdot\)\" (and).
## 2 Rate Equations Model  
This section includes a derivation of the dynamic equations of a laser cavity with EOF coupled into the laser cavity from both sides. Starting from the Lang-Kobayashi model [39], the lasing frequency and threshold gain shifts due to feedback are obtained, as well as an analytical expression for the frequency noise power spectral density from which the change of intrinsic linewidth can be computed.  
This work proposes a revised laser system, as shown in Figure 1. The laser cavity of length \(L\) is delimited by two mirrors with complex reflection coefficients \(\rho_{1}\) and \(\rho_{2}\), respectively. Assuming two interfaces at each side of the main cavity, two additional back-reflections (\(\rho_{1,\text{ext}}\) and \(\rho_{2,\text{ext}}\)) are considered and accounted for by computing effective reflection coefficients. The case of weak feedback is discussed here, for which:  
\[|\rho_{j,\text{ext}}|\ll 1. \tag{1}\]  
The following parameters are introduced:  
\[\kappa_{j}\equiv\frac{1-\rho_{j}^{2}}{\rho_{j}}\frac{\left|\rho_{j,\text{ext} }\right|}{t_{\text{cav}}} \tag{2a}\] \[\phi_{j}\equiv\omega_{\text{FB}}t_{j}+\phi_{\text{m}_{j}}\] (2b) \[\delta\omega\equiv\omega_{\text{FB}}-\omega_{\text{ref}}\] (2c) \[t_{\text{cav}}=2L/v_{\text{g}}, \tag{2d}\]  
with \(j=1,2\), where \(\kappa_{j}\) is the coupling coefficient; \(t_{\text{cav}}\) is the cavity round-trip time, with the group velocity \(v_{\text{g}}\); \(\phi_{j}\) is the phase delay due to the external cavities determined by the  
which vanishes if:  
\[\cos(\omega_{\text{ref}}t_{\text{ext}}+\phi_{\text{m}_{1}}) =-\cos(\omega_{\text{ref}}t_{\text{ext}}+\phi_{\text{m}_{2}})\] \[\cos(\omega_{\text{ref}}t_{\text{ext}}+\phi_{\text{m}_{1}}) =\cos(\omega_{\text{ref}}t_{\text{ext}}+\phi_{\text{m}_{2}}-\pi) \tag{14}\] \[\Rightarrow\omega_{\text{ref}}t_{\text{ext}}+\phi_{\text{m}_{1}} =\omega_{\text{ref}}t_{\text{ext}}+\phi_{\text{m}_{2}}-\pi+2m\pi \quad m\in\mathbb{Z}\] \[\stackrel{{ m\
eq 0}}{{\Rightarrow}}\Delta\phi_{\text{m}}=\pi.\]  
This condition, while different than condition (12), also yields stability regardless of lasing frequency in this case for the threshold gain. Both cases are studied numerically in Section 3.
## 2 Materials and Methods
### Galvanotaxis Chamber for 2D Culture  
The galvanotaxis system (Figure 2A) was constructed following a flow chamber assembly [25]. Silver-silver chloride electrodes were fabricated from silver wire and soaked in a hypochlorite solution (Clorox Bleach) for 1 h. Electrodes were connected to ports on both sides of the galvanotaxis channel using a pair of 2% agarose-PBS bridges. These bridges prevented cell contamination from electrolysis products generated during EF stimulation using a power supply (Keithley Instruments, Cleveland, OH, USA), which delivered a current of 3.3 mA (6 V/cm EF strength) [25]. The conductive media that flowed through the galvanotaxis chamber system was Dulbecco's Modified Eagle's Medium (DMEM; Cat. No. 12100046; Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 5% fetal bovine serum (FBS; Cat. No. S11550; R&D Systems, Minneapolis, MN, USA), which had adequate protein concentration for cellular adherence as well as ion concentrations for current flow. Galvanotaxis DC EF stimulation was applied for 3 h at room temperature. The chamber also consisted of a glass panel, allowing for microscopic analysis of cellular migration.  
Figure 1: In vitro PEMF chamber consisting of (**A**) two electromagnetic Helmholtz coils (green) that create a (**B**) uniform magnetic field in the culture region.
## 2 Materials and Methods
### Numerical Models
#### 2.3.2 Non-Smooth Contact Dynamics Model (NSCD)  
The NSCD method implemented in the LMGC90c version 2021 [31; 32] uses an implicit integration scheme and integrates non-smooth contact laws. Considering two bodies in a plane, one candidate \(A_{i}\) and one antagonist \(A_{j}\), and their possible points of contact \(C_{i}\) and \(C_{j}\), with \(n\) the vector orthogonal to the point of contact \(C_{i}\) (Figure 5a), the distance between the two points of contact will be:  
\[g=\left(C_{j}-C_{i}\right)n. \tag{17}\]  
Defining \(\dot{u}_{n}\) and \(\dot{u}_{t}\), the normal and tangential velocity of \(C_{j}\) with respect to \(C_{i}\), and \(r_{n}\) and \(r_{t}\), the normal and tangential reaction forces of \(A_{i}\) on \(A_{j}\), two contact laws were used:  
1. [PERSON]'s law of impenetrability (Figure 5b): \[g\geq 0,r_{n}\geq 0,gr_{n}=0,\] (18) \[\text{if }g=0\to\dot{u}_{n}\geq 0\to r_{n}\geq 0\to\dot{u}_{n}r_{n}=0.\] (19)  
This law specifies a perfectly plastic impact, i.e., [PERSON]'s law returns a restitution coefficient equal to zero. As a result of the impact, there are no bounces and, in the case of stones and bricks, there is a low coefficient of restitution and, for this reason, it can be neglected.
2. The dry-friction [PERSON]'s law (Figure 5c): \[|r_{t}|\leq\mu r_{n}:\left\{\begin{array}{l}|r_{t}|<\mu r_{n}\to\dot{u}_{t}=0,\\ |r_{t}|=\mu r_{n}\to\dot{u}_{t}=-\lambda\frac{r_{t}}{|r_{t}|},\end{array}\right.\] (20) where \(\mu\) is the friction coefficient and \(\lambda\) is a positive real arbitrary number. The equation of motion can be written in this way: \[M\ddot{q}=f\big{(}q,\dot{q},t\big{)}+l,\] (21) where \(M\) is the mass matrix, \(\ddot{q}\) is the acceleration, and \(f\big{(}q,\dot{q},t\big{)}\) is the vector of the internal and external discretized forces acting on the system; meanwhile, \(l\) is a measure of the impulse of contact resultant.  
The couples that characterize each contact, \((\dot{u}_{n},\dot{u}_{t})\) and \((r_{n},r_{t})\), are related by linear maps that depend, respectively, on the global vector \(\dot{q}\) and \(l\).  
Figure 4: Inelastic mono axial curve in compression (**a**) and tension (**b**), and yield surface in the Westergaard space (**c**).  
The \(l\) and the velocity \(\dot{q}\) are a discontinuous function of time given that Equations (18) and (19) are non-smooth. The velocities are discontinuous and the equation of motion is integrated into time intervals \([t_{i},t_{i+1}]\) in this way:  
\[M\big{(}\dot{q}_{i+1}-\dot{q}_{i}\big{)}=\int_{t_{i}}^{t_{i+1}}f\big{(}q,\dot{q },t\big{)}dt+\underline{l}_{i+1}, \tag{22}\]  
where \(\underline{l}_{i+1}\) is the impulse in the interval time and \(\dot{q}_{i+1}\) is the variable that approximates the right limit of the speed in the time \([t_{i+1}]\).  
The reactions are approximated in the average of the impulse in \([t_{i},t_{i+1}]\) in the global (22) and local contacts (18)-(19). The deformability of the blocks is neglected, obtaining the dynamic iteration between the elements exclusively by sliding and rotation of the blocks.
# A Real-Time Dynamic Gesture Variability Recognition Method Based on Convolutional Neural Networks
## 2 Purpose and Summary of the Work  
This work's primary focus is the development of multimodal systems [28] for automatic sign language translation.  
Firstly, the authors strive to overcome the problems of recognizing sign language by integrating several modalities, such as hand movement, hand configuration, and body and head movements. Secondly, the authors try to cover various variations in the demonstration of gestures and group them into four separate categories (described further) for a more accurate and reliable interpretation of sign language. Thirdly, the authors develop a preprocessing module that allows one to collect gesture properties from video files or in real-time automatically. This makes the technique applicable to any sign language and allows for further experiments. Fourth, the authors create their own extensive Kazakh Sign Language (KSL) dataset consisting of 2400 training, test, and validation samples, which helps solve the problem of limited datasets in gesture recognition research. Finally, the authors test their architecture on various public data sets, contributing to ongoing efforts to improve gesture recognition technologies.  
The presented work makes it possible to answer the following formulated research questions:  
1. Is it possible in practice to use technologies that integrate hand, body, and head movements to improve sign language recognition?
2. Is it possible to group gestures for a more accurate and reliable interpretation of sign language?
3. Is it possible to create preprocessing software that allows for gesture properties to be automatically collected from video files or in real time from a video camera system?
4. Is the created architecture of the real-time dynamic gesture variability recognition system based on CNNs effective for different data sets?
## 2 Related Work
Another router in this category is KNNR, a router based on the KNN classification algorithm, proposed in [25]. They use six parameters (available buffer space, time-out ratio, hop count, neighbor node distance from destination, interaction probability, and neighbor speed) to decide on the final label. The class used during the training stage (which is done offline) is based on the interaction probability, which is the same used in PRoPHET. Like SeeR, this router addresses the routing problem under the best next message perspective. Their results show better average delivery ratio and acceptable delay with respect to Epidemic and PRoPHET routers. Also, the authors in [26]propose MLProph, a machine learning model as a routing protocol. They use the PRoPHET router as the base and expand its capabilities by adding some other features to the equation, and the result is an improved router with respect to the base. Although they use a neural network model as well, they use a different algorithm than the one proposed here, Furthermore, their router makes calculations for each connected router, which increases computational resources such as time and CPU usage, and transfers sensible information from the connected nodes, increasing the risk of security leaks. In [27], the authors presented CRPO (cognitive routing protocol for opportunistic networks), which also uses a neural network as the core, although the decision parameter is the probability of encounter defined in PRoPHET; hence, CRPO is similar in nature to MLProph, since both of them use PRoPHET's probability as their main decision parameter. Although the authors claim that the training stage is run for X units of time each Y units of time, they do not provide further detail on this. Finally, in [28], the authors explore the possibility of removing the routing protocol from a wireless network using deep learning techniques. The problem statement, however, is formulated as a classical optimization problem to find the shortest path in a connected graph. That is, the scenario is different to that of a vehicular network, since one of the main characteristics in VDTN is precisely the lack of a fixed topology with pre-defined paths.
# A High Compressibility Pressure--Sensitive Structure Based on CB@PU Yarn Network
## 3 Results and Discussion  
### Structure and Preparation of CB@PU 3D Network Structure  
Figure 1a illustrates the fabrication process of the CB@PU yarn network. Figure 1b,c show the spatial structure of the sensor. The holes on the ring were evenly arranged. PU yarn was wound segment by segment in order to ensure equal length in the unit space, and the yarn was wound according to certain rule. A cross section of Z-shaped arrangement yarn is shown in Figure 1d,e, which reveals the longitudinal interweaved layout of the yarn. In a word, the uniform distribution of the network structure could be controlled. Positively charged chitosan (CS) and negatively charged cellulose nanocrystals (CNC) were used as mediators. The PU elastic network was immersed into a positively charged CB@CS solution and a negatively charged CB@CNC solution successively. During electrostatic deposition, CB particles were gradually coated on the surface of the PU yarn network. After drying, the final CB@PU conductive network was fabricated.  
Figure 1: (**a**) Schematic illustration for preparation of the CB@PU yarn network assembly process; (**b**,**c**) Schematic illustrations of the PU yarn network. Schematic diagram of the horizontal interlacing method (**d**) and the longitudinal interlacing method of the yarn network (**e**).  
Compared with the original white PU yarn network (Figure 2a), the CB@PU network was darker after loading (Figure 2b). The dark visual appearance indicated that CB achieved a good load. SEM was used to observe the surface characteristics of PU yarn and of CB@PU yarn. As addressed in Figure 2c,d, PU yarn (34 tex) had a diameter of 80 \(\upmu\)m and consisted of a bunch of PU microfibers. The surface of PU yarn was very clean and smooth before deposition of CB. In Figure 2e,f, it can be seen that the surface of the PU yarn became rugged after CB deposition. It can be clearly seen that the CB nanoparticles were well distributed. These indicated that the CB layer can be coated on the PU yarn via an electrostatic deposition method.  
The CB@PU network was tested for compression. Figure 3a,b show images of the experimental sample before and after compression. The resulting CB@PU fiber network exhibited excellent flexibility in the compression process and achieved a compression deformation of 97.4%. This was attributed to the combination of the elastic PU yarn material and the hollow structure of the 3D yarn network.  
PU yarn possesses excellent flexibility and elastic elongation. Compared to structures of other sensors fabricated or assembled with entity materials, the hollow yarn mesh skeleton is not limited by the volume of the material, which can be almost completely compressed. Such properties have great advantages in testing large strains.  
Figure 3: (a,b) Photographs show the compressibility of CB@PU network.  
Figure 2: (a) Photographs of neat PU yarn network and (b) CB@PU yarn network.SEM images of a neat PU yarn (c,d) and CB@PU yarn (e,f) under different magnifications.
# Flexible Fashion Product Retrieval Using Multimodality-Based Deep Learning
## 2 Related Work
### Multimodality-Based Deep Learning  
The problem to be addressed in this study is multimodal, because it involves both query data and image data. Multimodal learning is a learning method for simultaneously handling data with several different modalities. Thus, many multimodal-learning methods have been employed for audio-visual speech recognition, human-computer interaction, and action recognition [21; 22; 23].  
Recent advances in deep learning have led to significant progress in multimodal learning. One such example is visual question answering (VQA) [24; 25; 26]. One of the basic VQA methods is iBOWING [24]. [PERSON] et al. [25] proposed the VIS + LSTM model. The images were converted into vectors using a CNN, and each query word was also converted into a vector via a word embedding technique. Both vectors were used as inputs of the LSTM model, and the final output of the LSTM model was transferred to the softmax layer to generate an appropriate answer. [PERSON] et al. [26] proposed a dynamic parameter prediction (DPP) network, called DPPNet, in which DPP is employed to determine parameters dynamically based on questions, and parameters can be shared randomly using a hash function. [PERSON] et al. [27] proposed a structure called multimodal compact bilinear (MCB) pooling. This method can be efficiently process data with different modalities and is based on the assumption that the cross product of the two vectors is efficient.  
The purpose of VQA is to analyze an image and answer questions from the image. Therefore, VQA requires natural language-level understanding of the image scene. On the other hand, the proposed approach is based on the formal, faceted queries consisting of Boolean-, categorical-, or pseudo-SQL forms, which can retrieve fashion products efficiently and flexibly. Although there is a significant difference in the output, the proposed approach and VQA have to deal with images and text or queries simultaneously. It is common to use a CNN to process image data and an RNN to process text data, although there are various ways to employ different networks together.  
Both the keyword-based query search and the image-based product search are essential for sustainable online shopping malls. However, there is considerable room for the improvement with regard to enhancing the scalability of the query and simultaneously exploiting the image-based search.