esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 7 Discussion
### Post-OCR-Correction Works [61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71]
[69] proposed an approach that faces some challenges when the Shadda diacritic is to be corrected, since it should be accompanied by other diacritics, while the model uses it alone in some locations because the training set has the same problem. [PERSON] et al. [70] ignored the letters with diacritics of a ratio less than half; therefore, it is not guaranteed that their model can perform well when tested in such scenarios. [PERSON] et al. [71] used a DBN that suffers from poor results if the batch size is small; it therefore takes longer to train such batches. The analysis, performance, and drawbacks of deep-learning text-correction works are summarized in Table 3. The conclusions drawn from the works on text correction discussed and analyzed above are as follows:  
* Uneven distribution and a lack of error types are common issues in text-correction datasets;
* Text-correction models suffer when faced with challenging examples, such as the presence of dialect words and the correction of punctuation errors;
* The problem of the ineffective testing of models is further extended to text-correction works, as this problem also exists in OCR works;
* More complex models, such as DBNs, take longer to train;
* There is a need for more text-correction datasets that are context-aware;
* The effectiveness of the application of embedding techniques to correct text, such as FastText and BERT, was shown; however, this approach is still in its infancy. Moreover, it has potential and should be explored further in Arabic. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Work** & **Year** & **Technique** & **Dataset** & **CER** & **WER** & **Other Results** & **Description** & **Limitation(s)** \\ \hline S. B. & & & & & & Best recall: & Generated eight & Uneven \\ Aichaoui & 2022 & AraBART & SPIRAL & N/A & N/A & 0.863 for & kinds of error on & distribution \\ et al. & 2022 & AraBART & SPIRAL & N/A & N/A & space-issue & which to train and & between the types \\ [61] & & & & & & & errors & test & and categories of \\ \hline M. & & & & & & & & error \\ Alkhatib & 2020 & AraVec, Bi-LSTM, attention mechanism, & & & & & & & \\  & mechanism, & & & & & & & \\  & polynomial & & & & & & & \\  & classifier & & & & & & & \\  & classifier & & & & & & & \\  & & & & & & & & \\ \hline A. & & & & & &
# The Use of Volcanic Powder as a Cement Replacement for the Development of Sustainable Mortars
## 2 Materials and Methods  
### Volcanic Powder Characterization  
The VP used in this work was obtained from the last eruption of the Calbuco volcano. Calbuco (41\({}^{\circ}\)20\({}^{\prime}\) S, 72\({}^{\circ}\)37\({}^{\prime}\) W, 2003 m.a.s.l.) is an active stratovolcano which belongs to the southern Andes, sited among Ensenada and Puerto Montt, in Chile. The last subplinian eruption happened on 22 and 23 April 2015, with a total volume of tephra fall deposits calculated to be 0.27 km\({}^{3}\). Analyses of the erupted debris revealed the presence of porphyritic basaltic andesite (~55% by weight of SiO\({}_{2}\)) [28]. On the one hand, the fallout mostly affected the northeast area of the volcano. On the other hand, the finest ashes were deposited mainly in southern Chile and Argentinean Patagonia. The analysis of the size distribution of particles showed fractions between 3 and 350 \(\upmu\)m, variably distributed as a function of the distance from the origin [29].  
The material used in this study was previously sifted and the VP particles used were less than 75 \(\upmu\)m.  
The chemical analysis, determined with X-ray fluorescence (XRF), and the VP physical properties are listed in Table 1. According to the table, the main components of VP are silica, aluminum, and iron. The particle size distribution is depicted in Figure 1.  
The scanning electron microscopic (SEM) analysis of the VP appears in Figure 2, and it was performed using a scanning electron microscope model Hitachi S3000N (Hitachi, Krefeld, Germany). The analysis showed that the particles of VP are irregular showing very smooth surfaces, which is consistent with the low surface area shown in Table 1. In addition, Figure 3 represents the energy-dispersive X-ray (EXD) spectroscopy for the VP obtained by SEM, which is in agreement with the results of the chemical analysis noted in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Composition** & **VP** \\ \hline SiO\({}_{2}\) & 57.76\% \\ Al\({}_{2}\)O\({}_{3}\) & 14.54\% \\ CaO & 8.27\% \\ Fe\({}_{2}\)O\({}_{3}\) & 11.00\% \\ SO\({}_{3}\) & - \\ MgO & 2.44\% \\ Na\({}_{2}\)O & 2.41\% \\ K\({}_{2}\)O & 2.14\% \\ TiO\({}_{2}\) & 1.42\% \\ Density & 2450 kg/m\({}^{3}\) \\ Blaine surface area & 285 m\({}^{2}\)/kg \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and chemical properties of VP.  
Figure 1: Grain size distribution of volcanic powder (VP).
# Non-Autoregressive Sparse Transformer Networks for Pedestrian Trajectory Prediction
## 2 Related Works
### Non-Autoregressive Inference  
For sequence modelling tasks, most deep neural networks generate predictions one by one in an autoregressive way, such as in RNN-based models. This indicates that the prediction at each time step is achieved based on the prediction results of the previous steps. These methods are not parallelizable and increase computational complexity in the case of works in machine translation using transformers [22; 57]. In addition, the autoregressive pattern inevitably accumulates errors. The original design of self-attention in transformers is parallelizable in principle, while the autoregressive decoding at the inference stage makes it hard to be parallel. In light of this issue, some works have tried to attempt parallel transformer decoding in non-autoregressive pattern [24; 28; 58]. Study [28] adopted fertilities to parallelize decoding with transformers in machine translation. DETR [24] employed transformers with parallel decoding in end-to-end object detection. Study [58] explored non-autoregressive inference in human motion prediction. Non-autoregressive inference is also applied in pedestrian trajectory prediction tasks. NAP [59] designed a time-agnostic context generator and a time-specific context generator for non-autoregressive prediction. STPOTR [60] predicted human poses and trajectories by non-autoregressive transformer architecture. PReTR [61] extracted features from multi-agent scenes by employing a factorized spatio-temporal attention module and solved the trajectory prediction problem as a non-autoregressive task. We were inspired by this idea and constructed transformers in a non-autoregressive pattern for pedestrian trajectory predictions.
# Current Trends in Fluid Viscous Dampers with Semi-Active and Adaptive Behavior
## 2 Passive Fluid Viscous Damper
### Modeling of Passive Fluid Viscous Dampers  
The mechanical behavior of an FVD can be ideally described through the rheological model of a viscous dashpot element [70, 71], as shown in Figure 4.  
Figure 3: Force–displacement curves (**a**) and force–velocity curves (**b**) for passive FVDs with different \(\alpha\) exponents [57].  
The purely viscous model is valid only under a certain cut-off frequency [17; 54] and, in particular, for frequencies lower than 4 Hz, for which no additional stiffness can be included [1]. However, an accurate prediction of the structural response is obtained by a more complex mathematical model that also considers the small stiffness generated by the device under compression [71; 72; 73]. In many applications, FVDs are encased in bracing systems, and a reliable mathematical model should also include the stiffness of the brace [74; 75]. To this scope, different models were developed [31; 71; 76; 77], and the Kelvin-Voigt model [78] as well as the Maxwell model [44; 79] are the ones most largely adopted and implemented in the principal Finite Element Method (FEM) software programs, such as MidasGen 2023 [80] and SAP2000 v25.0.0 [81]. Between them, the Maxwell model is more common because it is more accurate when representing the viscous damping force [74; 78; 82; 83], as it is able to capture the relaxation time of FVDs [74; 83; 84]. On the other side, the Kelvin-Voigt model is not able to exactly reproduce the dependency of the FVD's behavior upon the frequency of the external excitation, tending to underestimate the FVD's force, especially at low frequencies [78].  
In particular, the Maxwell model consists of a spring and a dashpot connected in series (see Figure 5a). This model satisfies the kinematic conditions expressed in Equation (2) and the force condition reported in Equation (3):  
\[\begin{cases}x_{1}(t)+x_{2}(t)=x(t)\\ \dot{x}_{1}(t)+\dot{x}_{2}(t)=\dot{x}(t)\end{cases} \tag{2}\]  
\[F_{D}(t)=Kx(t)=C_{D}\dot{x}(t) \tag{3}\]  
where \(x_{1}\), \(x_{2}\), \(x\) and \(\dot{x}_{1}\), \(\dot{x}_{2}\), \(\dot{x}\) are, respectively, the displacement and the velocity of the dashpot (\(x_{1}\), \(\dot{x}_{1}\)), of the spring (\(x_{2}\), \(\dot{x}_{2}\)), and their combination (\(x\), \(\dot{x}\)) [44]. \(K\) in Equation (3) is the stiffness of an equivalent spring that includes both the stiffness of the bracing system and the inherent stiffness of the FVD [75].
## 4 Experimental Setup and Results Analysis
### Experimental Data  
The experimental data used in this study were obtained from VLF waveform data collected by the CSES EFD from 2019 to 2020. They include complete revisited track cycle waveform data and a random set of waveform data. A total of 8558 spectrograms were produced.
## 6 Authorization Mechanisms for Secure IoT Services
The authorization server authenticates the client, validates the authorization grant, and if the grant is valid, issues an access token. 5. The client requests the protected resource from the resource server and authenticates by presenting the access token. 6. The SP validates the access token and, if valid, serves the request. The OAuth authorization framework (currently in version 2.0 [77]) enables a third-party application to obtain limited access to an HTTP service, either on behalf of a resource owner by orchestrating an approval interaction between the resource owner and the HTTP service, or by allowing the third-party application to obtain access on its own behalf. Figure 5: Interaction between the four roles of the OAuth protocol flow. For IoT scenarios, when using CoAP as an application-layer protocol instead of HTTP, a modified version of OAuth should be defined in order to ensure an authorization layer for restricting access to smart objects services. Since OAuth is an open protocol to allow secure authorization in a simple and standard method from web, mobile and desktop applications, it has to be adapted in order to fit into IoT application scenarios and to be compatible with constrained environments. For instance, header compression should be used to minimize the amount of information sent along. HTTP/CoAP proxies should be able to perform OAuth proxying as well, in order to allow interoperability between conventional and constrained OAuth clients and servers. This raises particular challenges since the OAuth specification recommends the usage of HTTPS as a means to avoid man-in-the-middle (MITM) attacks, thus preventing the access tokens to be stolen and used by malicious nodes. This means that CoAPs should be used in order to comply with the specification. The HTTP/CoAP proxy should then be able to perform a TLS-to-DTLS mapping in order to ensure end-to-end security. However, the use of HTTPS (and CoAPs inherently) can be avoided: it is possible to use OAuth over an insecure communication channel by adopting digital signature schemes with HMAC-SHA1 and RSA-SHA1.
# Flight Level Assignment Using Graph Coloring
## 2 Flight-Level Assignment as Spectrum Coloring Problem with Hard Constraints
### Individual and Social Utility Models
#### 2.1.1 Individual Utility Model: Fuel Consumption
\begin{table}
\begin{tabular}{c c c} \hline \hline
**FL (100 s ft)** & **Fuel (kg)** & \(\frac{\text{A}Fuel}{\text{A}FL}\) \\ \hline
200 & 600 & n/a \\
240 & 710 & 2.75 \\
280 & 820 & 2.75 \\
300 & 875 & 2.75 \\
320 & 940 & 3.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Climb fuel cost (extracted from [24]). \begin{table}
\begin{tabular}{c c c} \hline \hline
**FL (100 s ft)** & **\% Off from Optimum SAR** & \(\frac{\text{\Delta}Percentage}{\text{\Delta}FL}\) \\ \hline
300 & \(-12\) & n/a \\
320 & \(-8\) & 0.2 \\
340 & \(-4\) & 0.2 \\
360 & \(-1.5\) & 0.125 \\
380 & 0 & 0.075 \\
400 & \(-3\) & 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cruise fuel cost (extracted from [25]). As stated above, for the purpose of this paper the aircraft considered will be an Airbus A320. Based on the slopes observed in the tables above, both the cruise and climb utility functions are linear approximations. The tables provide the operating points and ranges from literature, but given that we may use certain operating points below or above the limits, we make an extrapolation for these points using a linear model. This is viable because, in the case of the climb, the fuel expenditure is almost perfectly linear, with a rate of change around 3 (observed at \(3\pm 0.25\)). Then, for the cruise phase, we need to extrapolate FLs in the range below FL300, and the percentage off from optimum SAR starts to behave linearly. Finally, we obtain the values used for \(\varepsilon\) and \(\varphi\) based on the literature. From [26], we get that at Cost Index equal to zero (CI = 0, maximum fuel efficiency), 150 NM (278 km) are traveled when climbing to FL330. Assuming a constant rate of climb, we have, for a given flight level \(x\):  
\[\varepsilon(x)=0.84x. \tag{7}\]  
For the parameter \(\varphi\), we use the data in [27], which shows that as the length of a flight increases (which means cruise phase fuel expenditure becomes a larger percentage of the total used), the average fuel spent per kilometer tends to 3.3 kg/km. This means that each kg of fuel yields approximately 0.303 km, so we can set \(\varphi=0.303\). Based on the proposed model, Figure 1 shows the total fuel consumption of the Airbus A320 for different values of distance (\(d\)) and FL (\(x\)) according to our model.
# [PERSON]: Extreme Plastic Pollution Pathways in Riparian Waterways
## 3 Materials and Methods
### Phase Three: Field Testing  
A significant component of this research project was the reliability and resilience of the technology. The robustness and water tightness of the bottle tracker were tested in a controlled environment and then introduced into the environment by testing in canals, connected to the Saigon River, for set periods of time. Telemetry, data, and the digital pipeline were also tested by dry land field trips around Ho Chi Minh City to establish and map the data linkage between the bottle tracker and the cellular network it was connected to. This process also served as scouting for possible recovery sites. An action research methodology of planning, acting, observing, and reflecting was used to make any adjustments or adaptations to the bottle tracker as the field testing unfolded.  
An important finding from this process was the need to maintain the correct orientation of the GPS and communication antennas for uninterrupted data transmission. The optimized arrangement relied on positive buoyancy with self-righting capability, which ensured that the tracker remained three-quarters submerged while keeping its upper components above water. After multiple prototypes, this design proved to be the most effective, allowing the tracker to float while maintaining stability even in turbulent conditions.
# Resource- and Neighbor-Aware Observation Transmission Scheme in Satellite Networks
## 5 Evaluation Results
### Convergence to Nash Equilibrium  
The process by which the policies of observation satellites are converged to the Nash equilibrium (i.e., best-response policy) is illustrated in Figure 3. From Figure 3, it can be seen that the proposed algorithm can find the Nash equilibrium within few iterations. (Note that each observation satellite randomly set its initial policy.) This indicates that our algorithm may be implemented in satellite networks without incurring significant signaling overhead.  
Meanwhile, from Figure 3, it can be seen that each observation satellite decides its action with the consideration of the other observation satellites' actions at the best response to reduce the queuing delay at the relay satellite. Specifically, in this result, after the convergence, the observation satellites 1, 2, and 3 transmit their observations to the relay satellite with relatively high probability. In this situation, if other observation satellites (i.e., satellites 4 and 5) transmit their observations to the relay satellite, the queuing delay at the relay satellite can increase significantly. To realize this fact, the observation satellites 4 and 5 maintain lower transmission probability to the relay satellite.
## 5 Conclusions  
In this paper, we propose an inner-frame time division multiplexing waveform design method of ISAC in the 5G NR system, which achieves ISAC by reusing the hardware of existing 5G BSs. The proposed waveform design strategy describes the inner-frame time division multiplexing waveform design as an optimization problem of a constrained combination of PSLR and ISLR. Specifically, the constraints are the 5G communication protocol and 5G NR frame structure. This optimization problem is solved by the simulated annealing algorithm, and the corresponding signal detection and estimation methods based on sparse recovery techniques are developed to obtain the target information. The experiments show that the designed waveform satisfies the downlink and common channel constraints and has a 40% reduction in sensing costs compared to the uniform waveform. In terms of theoretical performance, the PSLR of the velocity ambiguity function, the velocity resolution, and the ambiguity-free velocity range are similar to those of the uniform waveform. Additionally, the designed waveform using the IAA can achieve a detection and estimation performance close to that of the uniform waveform in the case of high SCNR.  
Conceptualization, J.Z., P.C., X.W. and Z.Y.; methodology, J.Z., P.C., X.W. and Z.Y.; software, J.Z.; validation, J.Z., P.C. and Z.Y.; formal analysis, J.Z., P.C. and Z.Y.; investigation, J.Z., P.C. and Z.Y.; resources, Z.Y.; data curation, J.Z.; writing--original draft preparation, J.Z.; writing--review and editing, J.Z., P.C. and Z.Y.; visualization, J.Z.; supervision, P.C. and Z.Y.; project administration, P.C. and Z.Y.; funding acquisition, P.C. and Z.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Guangdong Basic and Applied Basic Research Foundation under grant 2022A1515140014, in part by the Science and Technology Project of Shenzhen under grant JCYJ20190808142803565, and in part by the National Natural Science Foundation of China under grant 62101207.  
**Institutional Review Board Statement:** Not applicable.  
Figure 12: RMSEs vs. SCNR. (**a**) RMSEs of velocity estimations vs. SCNR for different waveforms and algorithms. (**b**) RMSEs of range estimations vs. SCNR for different waveforms and algorithms.
# A Wideband Eight-Port MIMO Antenna with Reduced Mutual Coupling for Future 5G mm-Wave Applications
## 1 Introduction
The mm-wave MIMO antenna has a good diversity performance of ECC \(<\) 0.0001 and DG \(>\) 9.999 dB. To validate and examine the antenna further, the 44 \(\times\) 70 \(\times\) 0.8 mm\({}^{3}\) miniaturized antenna is printed to confirm the simulated outcomes.
# Satellite-Based Localization of IoT Devices Using Joint Doppler and Angle-of-Arrival Estimation
## 5 Localization Framework  
In this framework, we aim to estimate the location of the ground IoT device \(\mathbf{x}\) from the combination of Doppler and AoA measurements \(\mathbf{Z}^{(1:\mathrm{K})}\) using the joint likelihood function. We utilize the stochastic optimization method to estimate the location.  
In our problem, we aim to maximize the cost function that is represented by the likelihood function:  
\[\tilde{\mathbf{x}}=\underset{\mathbf{x}}{\mathrm{argmax}}\Big{[}p\Big{(} \mathbf{Z}^{(1:\mathrm{K})}|\mathbf{x}\Big{)}\Big{]}, \tag{13}\]  
where \(\tilde{\mathbf{x}}\) represents the estimated state. However, since the likelihood functions are Gaussian, maximizing the Gaussian likelihood functions is equivalent to minimizing the mean squared error, as discussed in Section 4.3. The optimization of the mean squared error can then be expressed as follows,  
\[\tilde{\mathbf{x}}=\underset{\mathbf{x}}{\mathrm{argmin}} \Bigg{[}\sum_{(k)=1}^{(K)}\frac{\Big{(}f_{\mathrm{d}}^{(k)}-\mu_{ \mathrm{d}}^{(k)}(\mathbf{x})\Big{)}^{2}}{\sigma_{\mathrm{d}}^{2}}+ \tag{14}\] \[\sum_{(k)=1}^{(K)}\frac{\Big{(}\Phi^{(k)}-\mu_{\mathrm{\Theta}}^ {(k)}(\mathbf{x})\Big{)}^{2}}{\sigma_{\mathrm{\Theta}}^{2}}+\] \[\sum_{(k)=1}^{(K)}\frac{\Big{(}\Theta^{(k)}-\mu_{\mathrm{\Theta}} ^{(k)}(\mathbf{x})\Big{)}^{2}}{\sigma_{\mathrm{\Theta}}^{2}}\Bigg{]}.\]  
The \(C\) constant is ignored in the minimizing computation as the constant does not change the point where the minimum error lies.
# The Effect of Rainfall on Economic Growth in Thailand: A Blessing for Poor Provinces
## 3 Methodology
### Data Type and Sources  
This section describes the data sources and procedures used to aggregate the data at the provincial level and provides some descriptive statistics for the main variables (refer to Table 1). The empirical study relied on panel datasets collected from different Thai government organisations between 1995 and 2015 (a period of 21 years) across 76 provinces nationwide.  
* Weather station data included ground station data collected by the Thai Meteorological Department (2016). The dataset incorporated daily data taken from 129 Thai weather stations between 1995 and 2015. The average annual rainfall (millimetres) and annual temperature (Celsius) were used and calculated using average monthly data. Temperature was included here as a robustness check.
* Provincial level GDP data was taken from real GDP by province (also known as gross provincial product [GPP] in Thailand, using chain volume measures against the reference year 2002, which was collected by the Office of the National Economic and Social Development Board of Thailand (2017). This dataset was reported in an annual series in Thai baht. This research studied the economic sector level in depth by classifying 16 economic activities into three sectors, which were agriculture, industry and service (refer to Table 11). The research used real GPP per capita growth for all economic activities.
* Population data was collected from the Department of Provincial Administration (2018). The dataset was reported in an annual series. The research used the population growth rate in an annual series.
# Reliability-Aware Cooperative Node Sleeping and Clustering in Duty-Cycled Sensors Networks
## 3 Network Model
### Selection of the Reference Node for the Next Hop  
Among the RCR, the cooperative node with the shortest distance to the strategic location will be selected as the reference node of the next hop.