esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Analysis of Various Tokenizers for Arabic Large Language Models
## 3 Tokenization
### Byte-Level BPE (BBPE)  
Byte-Pair Encoding (BPE) is a popular subword tokenization algorithm introduced by [PERSON] et al. [32]. The pretokenization stage of BPE includes separating the text into words and then creating a set of unique words, along with the frequency of each word in the training text corpus. In the tokenization stage, the algorithm starts by creating a base vocabulary list that contains all the characters and symbols that are in the unique word set. From there, the algorithm starts merging the starting tokens in the base vocabulary list, which are individual characters to form new tokens to be added to the vocabulary list. The algorithm starts by merging the most frequent tokens, and it keeps repeating the merging process until the number of tokens in the vocabulary list reaches the predetermined vocabulary size. The BPE tokenizer was used in the original OpenAI GPT model [39]. Both the BPE and WordPiece algorithms follow a bottom-up approach in building the vocabulary list.  
Byte-level BPE (BBPE): BBPE is a recent variation of the original BPE tokenization algorithm introduced by [PERSON] et al. [11]. The main difference between the two is that BBPE builds the base vocabulary list using a 256-byte set instead of actual characters. This approach allows the vocabulary to include all possible based characters while maintaining the overall vocabulary size within a reasonable limit, and it ensures that there is no need for an out-of-vocabulary token. In contrast to Sentencepiece and Wordpiece, BBPE is byte-level, which builds a vocabulary based on bytes encoding, not based on individual characters, in the training data. It also offers a universal vocabulary of bytes, which allows for better handling of many languages and for solving the issue of out-of-vocabulary. The BBPE tokenizer was used in many prominent language models such as GPT2 [39], RoBERTa [8], JABER and SABER [40], and ARAMUS [41].
# Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
## 4 Discussion
### Performance in Shortest Path Planning  
In single-floor environments, as illustrated in Figure 16, the navigation networks extracted from the S3 DIS dataset showed robust performance, consistently generating accurate and optimal paths that followed the expected routes. The computed paths moved smoothly through different spaces like hallways, doors, and offices, proving the reliability of the network for basic indoor navigation.  
For multi-floor environments, as demonstrated by the result from our self-collected dataset in Figure 17, the networks also performed well. Based on our extracted networks, the Dijkstra algorithm [37] successfully computed paths across different floors. Especially in our self-collected dataset, even after adding restrictions such as wheelchair accessibility or staircase-only, the extracted navigation network can still correctly plan the shortest paths that meet these conditions. This further validates the flexibility of our extracted navigation network in shortest path planning.  
Overall, the extracted indoor navigation networks performed a robust shortest path planning capability in both single-floor and multi-floor settings. The results highlight the accuracy and utility of the networks for real-world indoor navigation, including the wheelchair-accessible path planning.
# Coupled Numerical Method for Modeling Propped Fracture Behavior
## 3 Results
#### Sensitivity Analysis  
By application of the fore-mentioned one-way coupling method, a parameter sensitivity study was performed to analyze behaviors of proppant particles for a pressure drop of the medium flowing between particles. As previous research showed, the main parameters that affect propped fracture behavior concerning permeability and conductivity are the closure pressure, Young's modulus of the formation, and proppant diameter. Therefore, the effect of these factors was investigated in a predetermined relevant range according to chapter Section 2.1.2:  
* Closure pressure (P\({}_{\text{c}}\)) 2139-13,904 psi;
* Young's modulus of the formation (E\({}_{\text{f}}\)) 1-30 GPa;
* Proppant diameter (D\({}_{\text{p}}\)) 1-1.74 mm.  
In the first part of the sensitivity study, the effect of closure pressure was investigated. The Young's modulus of the proppant particles was 41,306 MPa, while the Young's modulus of the formation was 15,000 MPa. The diameter of proppants was 12, 12/18, and 16/20 mesh, as described in Section 2.1.2. The closure pressure was ranged from 2139 to 13,904 psi by 2000 psi steps, and the corresponding pressure drops were recorded at each simulation. Defining a mean value for the cross-section where the fluid flows through and using Darcy's law, apparent permeability could be calculated. Figures 16 and 17 demonstrate permeability and conductivity results as a function of closure pressure.  
Figure 15: Velocity and pressure distribution within the fracture.  
As expected, proppant pack permeability or other defined the permeability of the propped fracture decreases as closure pressure increases. Again, this phenomenon is anticipated since the formation pressure generates load on the propped fracture entailing a closing action which deforms and embeds proppants into the formation reducing the size of pore throats where fluid could flow.  
Another critical point is the impact of proppant particles on fracture permeability. The diameter of the proppant has a remarkable influence on fluid dynamics. As Petrophysics predicts exponentially higher permeability value for rocks formed by greater particles of sediment, crystalline or metamorphic components, the same, or even more significant effect--because no cementation considered this case--could be observed in case of propped fractures. Analyzing the sensitivity test results in Figure 16 one can also conclude that the greater the proppant size, the more permeability reduction takes place. By approximating the closure pressure value of 12,000 psi, different proppant diameters produce similar permeability. This phenomenon can be explained as follow: a proppant pack formed by  
Figure 16: Fracture permeability as a function of closure pressure.  
Figure 17: Fracture conductivity as a function of closure pressure.  
greater particles can deform to a greater extent because more pore space is available to be reduced. Consequently, pore space is getting smaller with closure pressure, and permeability approximates the case of prop
## 3 PDM and Parameter Identification
### RL-Based Parameter Identification Method
The random selection of \(p_{i}(k)\) to determine \(x_{ik}\) is the core of the CARLA algorithm, which can select the value with high probability density to improve the convergence rate. Through a series of actions, the parameter set is updated to \(X_{k}\). Then, the cost function \(J^{*}(X_{k})\) can derived from Equation (9). If the number of iterations does not reach the upper limit, the performance evaluation is implemented to evaluate \(X_{k}\) as follows:  
\[\beta(k)=\text{min}\bigg{\{}\max\bigg{\{}\,0,\frac{J_{med}-J(X_{k})}{J_{mal}- J_{\text{min}}}\bigg{\}},1\bigg{\}} \tag{13}\]  
where \(\beta(k)\) is the reinforcement signal and \(J_{mal}\) and \(J_{\text{min}}\) are the median and minimum values obtained in the previous iteration, respectively. After that, the PD function of each parameter can be updated as follows:  
\[f_{i,k+1}(x_{i})=\left\{ \begin{array}{l}\alpha(k)[f_{i,k}(x_{i})+\beta(k)H( x_{i},r)],x_{i}\in[x_{i\text{min}},x_{i\text{max}}]\\ 0,\text{ otherwise}\end{array} \right. \tag{14}\]  
where \(H(x_{i},r)\) is the Gaussian neighborhood function and \(r=x_{i}(k)\) is the axis of symmetry of \(H(x_{i},r)\) where  
\[H(x_{i},k)=\frac{g_{h}}{x_{i\text{max}}-x_{i\text{min}}}\exp(-\frac{\left(x_{ i}-r\right)^{2}}{2[g_{w}(x_{i\text{max}}-x_{i\text{min}})]^{2}}) \tag{15}\]  
where \(g_{h}\) and \(g_{w}\) are free parameters determining the speed and resolution of the learning process, which can be chosen as \(g_{h}=0.3\) and \(g_{w}=0.02\). \(\alpha(k)\) in Equation (14) is used to renormalized PD as follows:  
\[\alpha(k)=\frac{1}{\int_{x_{i\text{min}}}^{x_{i\text{max}}}\big{[}f_{i,k}(x_{ i})+\beta(k)H(x_{i},r)\big{]}dx_{i}} \tag{16}\]  
After multiple iterations, the PD distribution of each parameter in their learning space tends to be stable. The larger the PD, the corresponding parameter values are more proximate to the actual values. Therefore, the values with the highest PD are chosen as the tuned parameters.
# The Flavor Profiles of Highland Barley Fermented with Different Mushroom Mycelium
## 3 Results and Discussion
### Relative Odor Activity Value (ROAV) Analysis of Volatile Compounds  
The ROAV method is used to determine the contribution of each volatile compound to the primary odor. The greater the ROAV value, the more the contribution to the primary odor. Compounds with ROAV > 1 are key volatile compounds, and compounds with 0.1 \(\leq\) ROAV < 1 have important influences on the overall flavor [40]. Before fermentation, the odor of highland barley was mainly from decanal, hexanal, nonanal, heptanal, phenylacetaldehyde and 2-pentylfuran (Table 3). These compounds made the highland barley exhibit grassy, floral and leguminous odors. After fermentation, the contents of these components decreased. All the fermented samples showed a high level of 1-octen-3-ol, which has a mushroom odor.  
Figure 4: Thermogram analysis of volatile flavor compounds for original and fermented highland barley samples: HB (original highland barley), CM-HB (fermented highland barley with _C. millitaris_), SR-HB (fermented highland barley with _S. rugoso-annulata_), ME-HB (fermented highland barley with _M. esculenta_), SC-HB (fermented highland barley with _S. commune_), TS-HB (fermented highland barley with _T. sanguinea_).  
High levels of 2-phenylethanol were detected in SC-HB, TS-HB, ME-HB and CM-HB samples, which gave a floral aroma. In SC-HB, the presence of 3-(methylthio) propanal with low threshold and high content brought an unpleasant taste. Ethyl benzoate and ethyl acetate in TS-HB played an important role in the aroma with floral notes. However, the presence of methyl anthranilate also brought unacceptable spicy notes. The high content of phenylacetaldehyde in both samples produced a bitter almond flavor that was not liked by consumers. For the SR-HB, ME-HB and CM-HB samples, decanal, hexanal, nonanal, phenylethylaldehyde, trans-2-octen-1-aldehyde and 1-octen-3-ol played a key role in the overall flavor. Other minor aldehydes, esters and ketones (benzaldehyde, 1-hexanol, ethyl myristate, etc.) mostly had a minor contribution. For example, linalool and 2-undecanone were present in the CM-HB samples, which provided floral and citrus aromas to the sample. It can be seen that in highland barley fermented with _C. militaris_, _S. rugoso-annulata_ or _M. esculenta_, the flavor characteristics were improved.
## 4 Discussion
### SMAP/CYGNSS TB Spatial Coverage  
SMAP/CYGNSS TBs do not provide full spatial coverage every 2-3 days, due to the \(\sim\)8-14 day repeat period of CYGNSS observations at the 3 km scale. Low latitudes and areas where CYGNSS data have low signal-to-noise (i.e., forests and mountains) have worse spatial coverage. Higher latitudes and areas where CYGNSS data have high signal-to-noise (i.e., grasslands and croplands) have better spatial coverage.  
The CYGNSS spatial coverage for each SMAP observation ranges from 3.0 to 28.3% (5 th to 95 th percentile) with a median of 15.5%. We calculated percent coverage for each 9 km SMAP TB during 2020 within \(\pm 35^{\circ}\) latitude in non-mountainous and non-forested  
Figure 12: (**a**) RMSD between 3 km SMAP/Sentinel TBs and 9 km SMAP TBs. (**b**) RMSD between 3 km SMAP/CYGNSS TBs and 9 km SMAP TBs. Gray histogram TBs were calculated using uncorrected \(\beta\) values. Red histogram TBs were calculated using landcover class corrected \(\beta\) values. The remaining zero-value RMSD TBs in the \(\beta\)-corrected histogram are due to \(\Delta\Gamma\) values of zero, which occur when \(\Gamma_{C}\) is calculated using a single CYGNSS reflectivity value (Section 4.6.1).  
areas, using the associated 3 km SMAP/CYGNSS TBs. We then calculated the mean percent coverage for each 9 km grid cell.  
There are two options for improving SMAP/CYGNSS TB spatial coverage. (1) We could produce both 3 km and 9 km SMAP/CYGNSS TBs. The 3 km TBs would include greater spatial heterogeneity, but the 9 km TBs would have better spatial coverage. Since the native resolution of SMAP enhanced TBs is \(\sim\)33 km (gridded at 9 km), 9 km SMAP/CYGNSS TBs would still provide improved spatial heterogeneity. (2) We could use spatial interpolation techniques to fill the gaps. We could either (a) use pre-interpolated CYGNSS data, (b) interpolate 3 km SMAP/CYGNSS TBs, or (c) interpolate 3 km SMAP/CYGNSS NSSM. In each case, we could achieve full spatial coverage every 2-3 days, but precipitation patterns on the landscape would be less precise, which may lead to reduced NSSM accuracy. Future work will include analyzing the pros and cons of these options for improving SMAP/CYGNSS spatial coverage.  
A final note: GNSS-R spatial coverage will likely improve in future years, due to larger constellation sizes and a higher number of received signals [33; 34].
# A Method for 5G-ICN Seamless Mobility Support Based on Router Buffered Data
## 1 Introduction
LPBMMS [21] uses location prediction technology combined with data buffering to bring better handover performance. However, the existing ICN seamless mobility support solution based on data buffering lacks management of buffered data and cannot distinguish mobile business types, resulting in unreasonable allocation of buffering resources. Different types of businesses have different requirements for packet loss. In video communication and streaming media applications, especially real-time video conferencing or online live broadcast, business continuity and real-time are particularly important for mobile user performance experience. When buffering resources are limited, how to effectively allocate buffering resources to different types of mobile data flows is an urgent problem to be solved. The quality of buffer resource allocation will directly affect users' Quality of Experience (QoE). This paper integrates 5G with ICN, takes advantage of router data buffering capabilities, and designs a seamless mobility support method based on router buffered data suitable for real-time applications to improve the QoE of mobile users. The main contents of this article are as follows:  
* Referring to the 5G-ICN architecture in [7], we propose a seamless mobility support method based on router buffered data (BDMM), which fully utilizes the identity-based routing capabilities of ICN to solve the problem of UE cross-UPF handover affecting business continuity. BDMM also uses the ICN router data buffering capabilities to reduce packet loss during handovers. * We design a dynamic buffer resource allocation strategy (DBRAS). This strategy comprehensively considers factors such as the status of the mobile buffer, the transmission rate of the mobile data flow, and the business category of the mobile data flow, and adjusts the buffer resource allocation results in a timely manner based on network traffic changes and business types to optimize the overall loss in performance of mobile data flows. * We conduct a series of experiments to evaluate the performance of the proposed method. Experimental results show that our method outperforms other methods in terms of average packet delay, weighted average packet loss rate, and network overhead. In addition, the proposed method also has good performance in average handover delay. The rest of this paper is organized as follows. In Section 2, we review 5G-ICN network convergence research and research on seamless mobility support methods in ICN, respectively. In Section 3, we describe the proposed seamless mobility support method and describe the buffer resource allocation problem. In Section 4, we present the dynamic buffer resource allocation strategy. Section 5 discusses the experimental results of the proposed seamless mobility support method. Finally, we conclude the paper and discuss our plans for related future work in Section 6.
## 3 Results
### Conventional Power Supply Approach  
The conventional power supply approach has provided EL pictures with enough quality to be used as tool for monitoring and detecting failures in PV modules as Figure 7 shows. Identification of patterns in these EL images enables fault detection. Regions with low signal intensity (dark areas) can be associated with regions within the solar cell that do not contribute to power generation. Conversely, solar cells with high and homogeneous signal intensity indicate a good state of health and performance. All wave modulation, including square, full sine and half sine, has allowed a successful El signal extraction by using the FFT. Even when irradiance is higher than 1000 W/m\({}^{2}\) and therefore the ratio between EL signal and background signal in minimized, the processing approach is able to extract the EL signal, providing good enough SNR (>10 dB). Note that square modulation led to slight better results in terms of SNR, although all pictures have similar qualitative quality, it being difficult to detect major differences between them.  
Figure 6: Average FFT transformation for a sequence of 1000 images with 53 fps and 4 Hz of modulation frequency using a square wave.  
Figure 7: EL images using conventional approach with power supply using different waveforms. Exposure time of 2 ms, 53 fps and 1000 images per sequence.
## 3 Results and Discussion
### Hydrochemical Facies and Major Elements
Redox values cannot be checked to confirm this hypothesis because the parameter is missing in the database. In addition, the dominant activity in these areas is agriculture, so the high Cl concentrations could also result from irrigation or fertilizer use [74]. A further hypothesis concerns the possibility that in these areas traces of contamination of chlorinated solvent are present as indicated by [PERSON] et al. (2016) [75]. These compounds could release chloride ion into the water when subjected to decomposition by microorganisms present in aquifers [75]. The Ca/Mg-SO\({}_{4}\) water are limited in confined areas at the foot of the Apennine chain. In such areas, rainwater interacting with Triassic and Messinian evaporitic rocks causes dissolution and the release of SO\({}_{4}\)\({}^{2-}\) and Ca\({}^{2+}\) in groundwater [69]. In confined aquifers, 89% of the water samples are classified as Ca/Mg-HCO\({}_{3}\), with an increase of 7% in Na/K-HCO\({}_{3}\) water compared to the phreatic aquifers (Figure 5b); while Na-Cl, Na-SO\({}_{4}\), and Ca-MgSO\({}_{4}\) water constitute 4% of the total database for semi-confined and confined aquifers. In the north-west confined aquifers, the water composition reflects the above phreatic aquifers: Mg\({}^{2+}\) and HCO\({}_{3}\)\({}^{-}\) are the dominant ions and their origin is linked to the geological context [76]. The Mg-HCO\({}_{3}\) facies located in the westernmost part of the plain (PIE) is due to the contribution of ultramafic rocks (Figure 1) and the dissolution of Mg\({}^{2+}\). The samples have Ca\({}^{2+}\)/Mg\({}^{2+}\) ratio of 1:3, typical of water circulating in serpentinites [76]. In this area of the plain, concentrations of Cr and Ni higher than threshold values (50 ug/L for Cr, 5 ug/L for CrVI and 20 ug/L for Ni) are found and originate from the ophiolites and serpentinites outcrops, which constitute a constant natural source of these elements into the water [65; 77]. The sulfate-rich water are mainly detected in the central EMR area, close to the Apennine border but are present also as isolated samples moving towards the coastal area. Na-HCO\({}_{3}\) water are mainly located in the central area of the plain, in EMR. The water circulation inside the confined aquifers is very slow (less than 10 m/year), while in phreatic aquifers it is between 0.1 and 5.0 m/day [42]. The high residence times allows the cation exchange process in fine sediment between Na\({}^{+}\) of marine water and Ca\({}^{2+}\) of freshwater as found in previous studies carried out in EMR and FVG [63; 78]. Ionic exchange processes between Ca\({}^{2+}\) and Na\({}^{+}\) are very evident in the central area of the plain, where many Na-HCO\({}_{3}\) water samples with extremely high EC are detected [69]. The origin of these water is most probably linked to the presence of relict marine water, considering the distance from the current coastline.
## 1 Introduction
SM [7] is a recently developed transmission technique for wireless communication systems, which uses multiple-input multiple-output (MIMO) antennas. The main idea of SM is conveying additional information in the index of the transmitted antennas: increasing the number of transmit antennas increases the spectral efficiency, and transmitting additional bits without activating all transmit antennas and consume transmitted power. As only one antenna is activated in the SM transmission, SM is a highly energy-efficient modulation technique candidate for the next 5G mobile network [8] and also for IoUTs [9]. There are two information carrying items in SM: (1) constellation signal and (2) index of broadcast antennas with various fault amount. As the SM has two information axes, and each axis has a different level of bit error rate (BER), broadcasting encoded image bits with different level of protection can be applicable. This paper proposes a modified set partitioning in hierarchical trees (M-SPIHT) image coder [6] to generate two different stream of bits based on their significance including critical bits and uncritical bits. The critical bits will be transmitted over the low BER spatial modulation index bits and the uncritical bits will be transmitted via spatial modulation data bits carried using high modulation order scheme to solve the underwater acoustic limited channel bandwidth problem. In this paper, numerical analysis has been used to show the differences in the level of BERbetween the SM carrying information units. Simulation results have been used to show the error bit sensitivities of the SPIHT encoded bitstream and performance improvement in the reconstructed received underwater image. In particular, to improve the underwater multimedia transmission over the IoUTs, we have focused on the following aspects in this paper. 1. Encoded image bits classification using M-SPIHT encoder to classify the encoded bits into two categories: critical and uncritical bit streams. 2. Based on the M-SPIHT encoder classification, the UEP using the spatial modulation is proposed to improve the quality of the reconstructed image transmitted over the underwater acoustic channel. 3. A mathematical framework for assessing the ABER performance of the SM information carrying units is laid out thoroughly to prove the capability of UEP using SM scheme. 4. The proposed UEP-SM scheme carefully evaluated the reconstructed image improvement with the PSNR by simulation experiment on the simulated channel and measured underwater acoustic channel. The rest of the paper is structured as follows. Section 2 represents the SPIHT encoded image classification. Section 3 explains the proposed UEP technique using spatial modulation. The performance analysis and simulation results are discussed in Section 4, and finally the paper is concluded in Section 5. Notations: Column vectors (matrices) are denoted by boldface lower (upper) case letter; superscripts T, *, and H stand for transpose, conjugate, and conjugate transpose, respectively.