esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT-Enabled Gas Sensors: Technologies, Applications, and Opportunities
## 7 Discussion and Open Issues
### Wireless Gas Sensors  
Table 2 summarizes the main aspects of the most promising systems, in terms of sensing technologies, wireless protocols, and their focus. It includes a brief description of each proposal highlighting their strengths and weakness.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Description** & **Gas** &
\begin{tabular}{c} **Sensing** \\ **Tech.** \\ \end{tabular} & **Wireless** & **Strengths** & **Weaknesses** \\ \hline \hline
[34] & Remote gas monitoring system. Data collected are transferred to a database and displayed on a webpage. & \begin{tabular}{c} \\ \end{tabular} & \begin{tabular}{c} \\ \end{tabular} & \begin{tabular}{c} \end{tabular} & \begin{tabular}{c} Measurement of other environmental parameters \\ \end{tabular} & \
\begin{tabular}{c} \end{tabular} &
\begin{tabular}{c} Only one target gas \\ Periodic \\ data transfer \\ \end{tabular} \\ \hline
[93] & \begin{tabular}{c} Energy efficient gas sensor for indoor air quality. Nodes exchange information in order to save energy \\ \end{tabular} & \begin{tabular}{c} VOC \\ CO \\ \end{tabular} & \
\begin{tabular}{c} MOS \\ \end{tabular} &
\begin{tabular}{c} ZigBee \\ \end{tabular} & \begin{In terms of wireless transmission protocols, in short-range communication the ZigBee protocol is the most applied to the studied proposals. This protocol uses the IEEE 802.15.4 physical and MAC layers, although it does not allow the use of IPv6 on the final devices, as the pure IEEE 802.15.4 deployed with the 6 LoWPAN adaptation layer. As this protocol can be used in mesh networks, the energy consumption and communication range on the network can be greater than Wi-Fi applications with one single access point, which is more suitable for IoT applications.  
Regarding long-range communications, cellular technologies were more explored in wireless gas sensors. The second generation of mobile communications was applied in four solutions, as these networks are still operating and the cost to use these networks is small, compared to the costs of deploying a new network to operate wireless devices, as the LoRaWAN. The monitoring process of remote areas, such as big farms, where the coverage of legacy mobile networks is poor, protocols as LoRaWAN may be more suitable for these scenarios.  
Most of the studied solutions do not support M2M communications and were not integrated though an IoT platform or middleware solution, which are important features on smart environments and IoT-ready solutions. These aspects allow devices to act in favor of users in determined situations, such as stopping gas leakages, opening windows to help with the ventilation, or even warning users with alarms. The M2M communication feature can be achieved through IoT platforms that store and forward data to other devices with the same context of the sensors. Moreover, these platforms can also exhibit the collected information for the final users.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
# Deep Learning-Enhanced Autoencoder for Multi-Carrier Wireless Systems
## 1 Introduction
* We evaluate the performance of the proposed model in terms of BER and BLER under both perfect and imperfect channel conditions. We compare it with traditional and DL-based decoders, and the results show that the GRU-MC-AE model exceeds the performance of both conventional and DL-based models. To further assess the efficiency of the proposed model, we evaluate its SE and EE. The remainder of this paper is organized as follows: The system modeling is introduced in Section 2. A thorough discussion of the suggested model, including details on offline training and online testing protocols, is given in Section 3. The simulation results are presented in Section 4, while the complexity analysis and detailed discussion are provided in Sections 5 and 6, respectively. Finally, the conclusions are drawn in Section 7.
## 5 Conclusions  
A mathematical model of isolated buildings based on DFP bearings is presented in this paper. The bearings are simulated through a series model considering a velocity- and pressure-dependent friction model and the vertical component of ground motions. The analysis results are obtained for a 9-storey steel building subjected to near-source and far-field earthquakes. Comparative results between non-isolated and isolated cases of the building demonstrate the high effectiveness of the DFP bearings in the reduction of the seismic response of high-rise buildings. As a result, the seismic responses of the building are significantly reduced. In most cases, the roof acceleration decreases about 60% and the first-floor shear force decreases about 80% as compared to the fixed base condition.  
The findings of the study also show the significant effect of the vertical component of the earthquake on the overall response of the building, especially in the case of the near-source earthquake. In detail, the response of the building considerably increases in the tri-directional case as compared with the bi-directional one. For example, the difference in the percentage of reduction between the two cases is about 15% for the X-response of the acceleration and 24% for the Y-response.  
Conceptualization, P.H.H. and V.N.N.; methodology, P.H.H. and V.N.N.; software, H.N.P. and V.N.N.; formal analysis, H.N.P. and V.N.N.; investigation, H.N.P. and V.N.N.; writing--original draft preparation, P.H.H., H.N.P. and V.N.N.; writing--review and editing, P.H.H., H.N.P. and V.N.N. All authors have read and agreed to the published version of the manuscript.  
This research is funded by Funds for Science and Technology Development of the University of Danang under project number B2019-DN02-55.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.  
Figure 12: The movements of the pendulum on the sliding surfaces: (**a**) the near-source earthquake and (**b**) the far-field earthquake.
## 2 Open Challenges When SS Meets ISAC  
ISAC is a cutting-edge research area that combines wireless communication and radar-like sensing into a unified framework. SS plays a critical role in ISAC, particularly for dynamic spectrum access, interference mitigation, and environment-aware communication. However, there are several open challenges in integrating SS with ISAC.
## 4 Experiments
### Noise Experiment  
Images sometimes generate noise during transmission or decoding. In order to evaluate the performance stability of localization algorithms in noisy environments, 3% salt and pepper noise was added to the samples, and the center of the spike was located. The experimental results are shown in Figure 16. The red circles and green dots in the figure represent the visualization of the spike circle screening results and the visualization of the spike center positioning results, respectively. From the positioning results, it can be concluded that the algorithm proposed in this paper still achieves accurate positioning even in the presence of noise interference and has strong robustness.
# Response of NDVI and SIF to Meteorological Drought in the Yellow River Basin from 2001 to 2020
## 1 Introduction
It is the fifth largest river in the world and the second largest river in China [27]. YRB has a vast territory and significant differences in climate exist in the different regions, and it is an area significantly affected by climate change [28]. YRB is an important ecological barrier and important economic zone in China, and plays a decisive strategic role in the ecological protection and high-quality development of the Yellow River Basin. In addition, YRB has diverse landforms, climates, and vegetation types. Vegetation plays a vital role in maintaining the stability of regional and global ecosystems [29; 30]. YRB drought has seriously increased in frequency and detriment. Evaluating the impact of drought on vegetation dynamics is necessary, which will provide reference suggestions for ecological protection and biodiversity construction. In recent years, the response of YRB vegetation dynamics to drought has great significance. However, most studies only use the traditional vegetation index to analyze vegetation growth [31; 32; 33], and SIF data that are more sensitive to climate are rarely used. It is unclear whether the trend of vegetation change monitored by NDVI and SIF in the Yellow River Basin is consistent, and the sensitivity of NDVI and SIF to drought is uncertain. Therefore, analyzing the response of NDVI and SIF to drought in the Yellow River Basin has become absolutely imperative for drought assessment and management.
## 3 UWB Characteristics  
### UWB Definition  
Developed in the 1970s, UWB is a wireless radio technology developed initially for classified applications by the US military, but the focus has shifted to impulse radio UWB (IR-UWB) with ongoing research, according to [64]. To distinguish UWB from narrow-band signals, the Federal Communications Commission (FCC) has defined that UWB can be treated as a radio frequency (RF) signal: firstly, when the occupying bandwidth is greater than or equal to 500 MHz, and secondly, the fractional bandwidth of UWB has to be greater than 20% of the center frequency [65] and expressed as  
\[B_{f}=2\times\left(\frac{f_{H}-f_{L}}{f_{H}+f_{L}}\right), \tag{1}\]  
where \(B_{f}\) is a dimensionless frequency-independent indicator, and \(f_{H}\) and \(f_{L}\) mean the higher and lower cut-off frequencies at \(-10\) dB of the UWB pulse spectrum. \(B_{f}\) and band ratio \(B_{r}\) help determine the types of communication. Table 1 summarizes the communication band usage scenario by classifying the communication system as a narrow-band, wide-band or ultra-wideband system.
## 4 Results
### Sequence-Specific Clustering Properties
#### 4.4.3 North Aegean Sea  
In NAS area cluster \(N1\), which consists of two moderate events (\(M=5.2\) and \(M=5.3\)) within a time period of 3 weeks, exhibits the lowest \(a\) value (\(a=1.10\)) among the main detected clusters, which could be an indicator of fluid diffusion in the area (Table 8). Another case worth mentioning is the 24 May 2014, \(M=6.9\), Samothraki seismic sequence which is divided into three major clusters (\(N4,\ N5,\ N6\), in Figure 7). The estimated background rates of the three major clusters are relatively small (\(\mu=0.16,\ 0.60,\ 0.29\)), whereas the opposite holds for the scaling parameter, \(a\), for the first two clusters (\(a=1.82,\ 1.76\)). Concerning the seismic excitation that consists of clusters \(N10\), \(N11\) and \(N14\), the relatively low productivity rates of the ETAS model (\(a=1.31,\ 1.29,\ 1.13\)) and, conversely, the relatively high background rates for the first two, \(N10\) and \(N11\), clusters (\(\mu=1.00,\ 0.91\)) may indicate fluid intrusion. This observation is in accordance with the study of [PERSON] et al. [67] who derived high background rates after the estimation of the ETAS model to the empirically divided 5 sub-clusters of the primary seismic activity (January-March 2017). A fast-dimishing aftershock activity is observed for the main shock (\(M=6.4\)) that is located SE of Lesvos Island (\(N12\)), which is translated into a high Omori exponent, \(p=1.48\). Additionally, low background rates characterize the three main clusters, \(N12\), \(N16\) and \(N17\), indicating that they are probably related to tectonic and coseismic stress transfer from previous seismicity [62]. Worth mentioning are the remarkable high background rates for clusters \(N8\) (\(\mu=1.76\)) and \(N9\) (\(\mu=2.78\)), which could be an indicator for seismic activity driven by transient forces, however, the number of events is rather small and could have led to significant biases in the inversion of the parameters.
# A Pipeline Approach to Context-Aware Handwritten Text Recognition
## 2 Literature Review
The proposed MLC-CRNN model, when integrated with two MLC modules, achieved the best performance by obtaining an accuracy of 91.4%. [PERSON] and [PERSON] presented a study that applied full-page handwriting document recognition using the Transformer model [4]. The authors aimed to recognize handwritten texts in a full-page manner. The model consists of a CNN network to extract the features from the document, followed by Transformer as an image-to-sequence model, which learns to map an image to a sequence. The model was trained on various datasets, including IAM, WIKITEXT, FREE FORM ANSWER, ANSWERS2. The model was then evaluated on the FREE FORM ANSWERS dataset and obtained a CER of 7.6%. Despite the promising performance, the method suffers from a biased multi-task problem. For example, if the model is trained using datasets that only contain one transcribed text region per sample (like the Free Form dataset), the model will have a tendency to transcribe only one main text region while skipping the others due to its full-page recognition nature. This is a challenge for text recognition. It is important to have a robust text recognition system that can deal with different text types, including printed texts, handwritten texts, and non-texts. Table 1 summarizes the different papers discussed in this section. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Subject of Study** & **Proposed Solution** & **Dataset** & **Experimental Results** \\ \hline [PERSON] (2016) [11] & Joint line segmentation and transcription & MDLSTM-RNN & RIMES and IAM database & \begin{tabular}{c} CER of 4.9 and 2.5 on IAM \\ and RIMES \\ data, respectively \\ \end{tabular} \\ \hline [PERSON] et al. (2018) [12] & Historical document processing & SFT & ICDAR 2017 competition dataset, IAM, RIMES & \
\begin{tabular}{c} BLEU score of 72.3 on \\ ICDAR dataset. CER and \\ WER of 2.1 and 9.3, 6.4 and \\ 23.2 on both IAM \\ and RIMES \\ datasets, respectively \\ \end{tabular} \\ \hline [PERSON] and [PERSON] (2018) [13] & Kannada Handwritten Document Recognition & CNN & Chars74K dataset & 99\% of accuracy \\ \hline [PERSON] et al. (2019) [3] & Line-level text style classification and recognition & GRCL &
\begin{tabular}{c} IAM online and offline \\ database, self-collected \\ handwritten \\ online samples \\ \end{tabular} \\ \hline [PERSON] et al. (2020) [14] & Recognition of handwritten text and text-line & CTPN to detect text lines, MLC-CRNN for text recognition & 3883 training images and 297 testing images \\ \hline [PERSON] and [PERSON] (2021) [4] & Full-page handwritten document recognition & Transformer & IAM, WIKITEXT, FREE FORM ANSWERS, ANSWERS2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of included studies.
# Quantum Modular Adder over \(GF(2^{n}-1)\) without Saving the Final Carry
###### Abstract  
Addition is the most basic operation of computing based on a bit system. There are various addition algorithms considering multiple number systems and hardware, and studies for a more efficient addition are still ongoing. Quantum computing based on qubits as the information unit asks for the design of a new addition because it is, physically, wholly different from the existing frequency-based computing in which the minimum information unit is a bit. In this paper, we propose an efficient quantum circuit of modular addition, which reduces the number of gates and the depth. The proposed modular addition is for the Galois Field \(GF(2^{n}-1)\), which is important as a finite field basis in various domains, such as cryptography. Its design principle was from the ripple carry addition (RCA) algorithm, which is the most widely used in existing computers. However, unlike conventional RCA, the storage of the final carry is not needed due to modifying existing diminished-1 modulo \(2^{n}-1\) adders. Our proposed adder can produce modulo sum within the range \(\{0,2^{n}-2\}\) by fewer qubits and less depth. For comparison, we analyzed the proposed quantum addition circuit over \(GF(2^{n}-1)\) and the previous quantum modular addition circuit for the performance of the number of qubits, the number of gates, and the depth, and simulated it with IBM's simulator ProjectQ.  
quantum modular adder; quantum ripple carry adder; Galois Field \((2^{n}-1)\); quantum circuit; quantum algorithm +
Footnote †: journal: Applied sciences
## 3 Results
### Results of the Biofilm Production
#### 3.2.2 Biofilm Production in Minimal ABTG Medium  
In the ABTG medium, the largest oscillations in the ability of _A. salmonicida_ subsp. _salmonicida_ to produce biofilms in relation to temperature changes occurred in the detection of viable cells (log CFU). At a temperature of 8 \({}^{\circ}\)C, all isolates produced relatively strong biofilms, with relatively high viable cells log CFU, ranging from 3.71 to 6.04 log CFU (Figure 4, Table S2). At 16 \({}^{\circ}\)C, in ABTG, three isolates (F461, F458 and F710) did not produce any detectable viable cells biofilm (100% reduction in comparison to 8 \({}^{\circ}\)C, \(p<0.001\)). The five clinical isolates (F410, F203, F352, F262, FN 1413) produced weaker biofilms (lower log CFU) at 16 \({}^{\circ}\)C than at 8 \({}^{\circ}\)C (reduction rate from 9.77% to 30.97%, \(p<0.05\)). Isolates F303 and ATCC 7965 produced a stronger biofilm at 16 \({}^{\circ}\)C in ABTG compared to 8 \({}^{\circ}\)C; the increase was 4.21% to 6.33% (\(p<0.05\)). Four strains (ATCC and three clinical isolates) produced biofilm at 25 \({}^{\circ}\)C, but all of them except ATCC produced weaker biofilms compared to 16 \({}^{\circ}\)C and 8 \({}^{\circ}\)C (decreasing rate 4.1% to 17.34% \(p<0.05\)). A total of seven isolates failed to produce any detectable biofilm at 25 \({}^{\circ}\)C in the ABTG medium (no viable cells detected). F187 successfully produced biofilm at all three temperatures--at 8 \({}^{\circ}\)C and 16 \({}^{\circ}\)C, with 5.24 log CFU, and at 25 \({}^{\circ}\)C, with 4.6 log CFU.  
ODc values of the crystal violet method were also detected in all isolates, and at all temperatures, but they did not correlate with the detected number of viable cells in biofilms at 25 \({}^{\circ}\)C, which is considered a non-specific reading of cellular detritus. Numerical values of the detected biofilms (log CFU and ODc) are given in Table S2.  
Figure 4: Biofilm production of clinical _A. salmonicida_ subsp. _salmonicida_ in minimal ABTG after seven days. Legend: ODc (columns)—measured optical density of formed biofilms; log CFU (lines)—number of viable bacteria in biofilms; F203, F262, F303, F352, F410, F458, F461, F710, FN1413, FN187—clinical isolates. The isolates are arranged in order from the strongest (left) to the weakest (right) biofilm producer based on log CFU obtained at 8 \({}^{\circ}\)C.
## 3 Estimation of Node Position Using Indirect Observations
### WLS Applied to the Method of Indirect Observations
We now skip all the next mathematical passages and we give the final solution. Let \(A_{\partial}\) represent the partial derivates of \(Ap\) in \(dx\) and \(dy\) computed at \(\tilde{p}=(\tilde{x},\tilde{y})\). After the linearization we obtain the bound problem  
\[A_{\partial}p+r=v \tag{12}\]  
So, by Equations (11) and (12), we can build the system that, once resolved, represents the final solution  
\[\left\{ \begin{array}{l}A_{\partial}p+r=v\\ \tilde{v}^{T}P\tilde{v}=min\end{array} \right. \tag{13}\]  
The final solution obtained after the resolution of system in Equation (13) is then the following  
\[\delta p=-\left(A_{\partial}^{T}PA_{\partial}\right)^{-1}A_{\partial}^{T}Pr \tag{14}\]  
Essentially, with this method, it is possible to spread the error of the measures, to minimize the residual generated by the solution, so that the method provides a more accurate solution with respect to the initial OLS estimation [37]. It is also possible to estimate the precision of the solution. Indeed, for the law of variance propagation, the variance of \(\delta p\) can be easily computed from the square matrix \(H=\left(A_{\partial}^{T}PA_{\partial}\right)^{-1}\): the values in the diagonal \(h_{ii}\) correspond to the variance of \(\delta p\) multiplied by a factor scale \(\sigma_{0}^{2}\). Note that at any iteration we need to estimate the new variances of residuals \(\sigma_{i}\), which are obtained from the vector of residuals of the previous iterations. The value \(\sigma_{0}^{2}\) at each step is instead computed by the maximum likelihood criterion  
\[\sigma_{0}^{2}=\frac{v^{t}Pv}{n-s} \tag{15}\]  
The described method can be iterated until vector \(\delta p\) is lower than a given threshold (which is set according to the desired target application).
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 6 Metaverse Engine
### Artificial Intelligence (AI) and Machine Learning (ML)
#### 6.3.3 Differences in Purpose and Autonomy  
The primary distinction between AI-driven avatars and NPCs lies in their relationship with users and their level of autonomy. AI-driven avatars are designed to represent and act on behalf of the user, making decisions based on user-defined parameters or learned behaviors [201]. They are personalized to the user's preferences and can be seen as digital extensions of the user's identity within the metaverse. These avatars help maintain a user's presence, even when they are not actively engaged in the virtual world, by taking over routine tasks or social interactions. On the other hand, NPCs exist independently of the user [202]. They are part of the virtual environment itself, managed by the system's AI to provide users with a more immersive and engaging experience. NPCs follow predefined rules or machine learning algorithms, interacting with users to fulfill specific roles--whether as educators, fellow learners, or virtual assistants. While they can simulate human-like behaviors, NPCs do not represent any specific user and are instead designed to enhance the user's experience by populating the world with interactive, responsive characters. NPCs facilitated design thinking by providing feedback, engaging in empathy-building exercises, and offering diverse perspectives that helped students reframe problems and develop solutions [203].  
Another key difference between AI-driven avatars and NPCs is the degree of personalization [201]. AI-driven avatars are highly personalized to their user. They adapt and evolve based on the user's behavior, preferences, and interactions, becoming more reflective of the user over time. This level of personalization ensures that the avatar represents the user's unique identity and can act in ways that align with the user's goals and needs. For example, in a corporate setting, an AI avatar could autonomously schedule meetings, manage tasks, or even negotiate with other AI avatars based on the user's work habits and objectives. NPCs, in contrast, are designed to serve broader, more generalized purposes within the virtual environment. They are not tied to any specific user and often follow universal rules or patterns set by the system. While NPCs can adapt their behavior based on interactions with multiple users, their core function is to enhance the immersive experience for all users rather than reflecting the behavior of any individual [204]. For example, an NPC in a classroom scenario might adapt to the learning pace of different students, offering tailored guidance and feedback, but its role remains fundamentally as a system-controlled character that enriches the educational experience for everyone, not just a single user.