esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Baseline Performance Characteristics
#### 3.1.2 Influence of Gasification Medium  
From operational viewpoints, the syngas characteristics can be improved by implementing alternative media, e.g., steam or oxygen-rich gas. Hence, two trial tests were conducted to verify the above scenarios.  
The first case addressed the influence of steam. The reference case in Figure 4 was carried out under the following operating conditions: Feedstock mass flow rate 2 (kg/h), temperature 900 \({}^{\circ}\)C, and ER 0.3. For comparison, an additional stream of steam with a steam-to-fuel ratio (kg/kg) SF = 0.4 was injected to the reactor. Due to the present equipment limitation, the steam temperature was substantially lower than that in the reactor; nevertheless, it was clearly seen that hydrogen generation was enhanced. Steam injection promoted the forward water-gas shift reaction (CO + H\({}_{2}\)O \(\rightarrow\) CO\({}_{2}\) + H\({}_{2}\)), which converted CO to CO\({}_{2}\) and produced more H\({}_{2}\).  
Oxygen-rich gas is another candidate for enhancing hydrogen generation. Again in Figure 4, the reference case was carried out under the following operating conditions: Feedstock mass flow rate 4 (kg/h), temperature 900 \({}^{\circ}\)C, and ER 0.3. Then, the oxygen content of gasification medium was increased to 35% (compared to -21% in air); as a consequence, hydrogen generation was enhanced, as was CO content as well. The result verified that the syngas characteristics can be improved under a nitrogen-lean environment, which envisages the potential for the concept of indirect gasification.  
Figure 4: Comparison of syngas composition with various gasification media.  
For the cases shown in Figure 4, the error bars were put in the data. The standard deviations were carried out, based on the four-time experimental results per case.  
The test data to address the influence of steam showed that hydrogen generation was enhanced with an additional steam injection to the reactor. It was seen that the existence of steam promoted the water-gas shift (WGS) reaction to generate a higher hydrogen content in the syngas composition. Furthermore, oxygen-rich gas improved the gasification process towards more fractions of both H\({}_{2}\) and CO in the syngas.
## 2 Related Work  
### Deep Learning Based Real-Time License Plate Detection  
Recent advancements in deep learning technology have led to active research on approaches that utilize deep learning techniques in license plate detection. A lightweight deep learning network that combines CNN and self-attention mechanisms for license plate recognition in low-quality images has been proposed [19]. By integrating an RNN encoder and a Transformer decoder, this approach enabled the effective learning of relationships between characters even in low-quality environments, achieving a 15% improvement in recognition accuracy compared to previously existing CNN-based methods.  
A vehicle detection algorithm based on an improved RT-DETR was developed in a previous study [20, 21]. This approach integrated CNN into a transformer to enhance local detection capabilities and designed a structure that effectively captured global dependencies through the self-attention mechanism. The proposed method demonstrated a 12% improvement in detection performance compared to existing YOLO series models, achieving particularly outstanding results in detecting small-sized license plates. Additionally, through DETR's end-to-end learning approach, accurate object location prediction without post-processing was enabled.  
Another study designed an efficient ANPR system for non-stop toll collection using YOLOv8 [22]. Specifically, YOLOv8's architecture was optimized for real-time processing, achieving a processing speed of 45 frames per second while maintaining a high detection accuracy of 96.8%. By introducing multi-scale feature fusion techniques, stable detection for license plates captured at various distances was enabled, demonstrating robust performance particularly against fast vehicle movements in highway environments.
# Parallelization of the Honeybee Search Algorithm for Object Tracking
## 2 Materials And Methods
### Normalized Cross-Correlation
\[\gamma(u,v)=\frac{\sum_{x,y}\left[I(x,y)-\bar{I}_{u,v}\right][t(x-u,y-v)-\bar{ t}]}{\sqrt{\sum_{x,y}\left[I(x,y)-\bar{I}_{u,v}\right]^{2}\sum_{x,y}\left[t(x-u,y-v )-\bar{t}\right]^{2}}} \tag{11}\]  
\[\bar{I}_{u,v}=\frac{\sum_{x,y}I(x,y)}{m\times n} \tag{12}\]  
\[\bar{t}=\frac{\sum_{x,y}t(x-u,y-v)}{m\times n} \tag{13}\]  
The value \(\gamma(u,v)\), called NCC, ranges between \(-1\) and \(1\)[35]. As the value approaches \(1\), the certainty of finding \(t\) in point \((u,v)\) with great robustness is greater. However, NCC is not capable of detecting certain transformations such as rotation and scale changes in the targeted object.
# Low-Complexity Multi-Size Circular-Shift Network for 5G New Radio LDPC Decoders
## 5 Conclusions  
In this paper, a novel reconfigurable message-passing network architecture has been proposed for 5G NR QC-LDPC decoders. The proposed MCSN structure using fine-coarse approach with low complexity is capable of supporting 51 different submatrix sizes of the 5G NR standard and exhibits low complexity. Simulation results prove that the proposed MCSN structure requires small area over its predecessors. Therefore, the proposed design is appropriate for applications with multimode QC-LDPC decoders in 5G standard requirements.  
[PERSON] and [PERSON] conceptualized the idea of this research, conducted experiments, collected data, and prepared the original version. [PERSON] reviewed, analyzed data, and updated the manuscript. [PERSON] supervised, validated, reviewed, and supported the research with funding. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the MSIT (Ministry of Science and ICT) under the ITRC support program (IITP-2021-0-02052) supervised by the IITP, and in part by INHA UNIVERSITY Research Grant.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Proposed** & **RIP [23,24]** & **FC + MUXs [16]** & **FC + RIP [24]** \\ \hline Maximum size \(P\) & 384 & 96 & 96 & 96 \\ CMOS technology & 65-nm & 32/28-nm & 130-nm & 32/28-nm \\ Message bits & 8-bit & 8-bit & 6-bit & 8-bit \\ \hline Frequency (MHz) & 580 & 637 & 500 & 649 \\ Norm. Frequency (MHz) & 580 & 314 & 1000 & 320 \\ \hline Area (mm\({}^{2}\)) & 0.301 & 0.042 & 0.278 & 0.036 \\ Norm. Area (mm\({}^{2}\)) & 0.301 & 0.696 & 0.371 & 0.599 \\ \hline \hline \end{tabular}
\end{table}
Table 2: ASIC Implementation Results of Multi-Size Circular-Shift Network for 5G NR LDPC Decoders.
# A Deep Learning Approach for Network Intrusion Detection Using a Small Features Vector
## 3 Background Theoretical and Conceptual Framework
### Single-Layer Neural Network  
It is well known that a single artificial neuron cannot model well because it cannot solve nonlinearly separable problems unless the input is transformed into a better representation. This makes it necessary to use several neurons. Figure 1 shows a single layer neural network, where several neurons are used in a hidden layer to transform the inputs into a more conducive representation. This mechanism makes the problem linearly separable. Hence, in a typical single-layer neural network, the first part computes the representation, while the second part classifies it. Each of the \(i\)th neurons in the hidden layer is able to connect with all the other inputs so that the connection between the \(i\)th hidden neuron with the \(j\)th input can be expressed in matrix \(W_{i,j}{}^{(1)}\), where the superscript represents the hidden layer number. The hidden layer bias is represented as \(b^{(1)}\), and X represents the input vector. Equation (1) represents the pre-activation computation in a hidden layer.  
\[a(X)=b^{(1)}+W^{(1)}X \tag{1}\]  
Equation (2) computes the activation \(h(X)\) over all the elements of pre-activation \(a(X)\).  
\[h(X)=g[a(X)] \tag{2}\]  
To compute the output activation \(f(x)\) in (3), the activation of the hidden units \(h^{(1)}\) is multiplied by \(W^{(2)}\), which weights the vector between the output and all the hidden units. In Figure 1, \(W_{i}{}^{(2)}\) represents weight of the connection between the output and the \(i\)th hidden unit; then, the bias term b is added, and \(X\) represents the input vector.  
\[f(x)=o\Big{[}b^{(2)}+W^{(2)}{}^{T}h^{(1)}(X)\Big{]} \tag{3}\]
# OPMSS: Optimal Producer Mobility Support Solution for Named Data Networking
## 3 Design of Proposed Optimal Producer Mobility Support Solution  
The OPMSS works immediately when the mobile producer relocates from the old PoA to the new PoA. The routing prefix name that is already stored in the FIB tables of the  
Figure 1: Overview of Kite Operation.  
intermediate routers becomes outdated due to the location change of the mobile producer. Therefore, there is a need for a mechanism to update the FIB tables about the new prefix of the mobile producer. The OPMSS consists of four components--mobility interest packet design, broadcast strategy design, restricted domain router configuration, and a best-route strategy to address producer mobility. Together, the four OPMSS components can solve the known handoff performance problems and provide an optimal data path after handoff.
## 4 Results and Discussion
### Speedup  
Figures 7 and 8 show the speed up of the entire dataset sorted on rows and the number of non-zeros, respectively. The speedup average and speedup maximum are calculated using Equations (9) and (10), respectively. The X-axis shows the number of matrices in our entire data set, and the Y-axis shows speedup against the average and worst-case scenarios.  
\[\text{SpeedUp}_{\text{avg}} = \frac{\text{t}_{\text{avg}}}{\text{t}_{\text{min}}} \tag{9}\] \[\text{SpeedUp}_{\text{max}} = \frac{\text{t}_{\text{max}}}{\text{t}_{\text{min}}} \tag{10}\]  
The X-axis shows the application domains, while the Y-axis shows the execution time on a logarithmic scale. The execution time is calculated by taking the sum of all execution times of each domain recorded against multiple blocks. Figure 9a,c,e show the execution time comparison of different application domains in the same numerological order, as listed in Table 3. Similarly, Figure 9b,d,f show the speedup achieved using the entire dataset. Speedup against the worst-case scenario is represented in blue while speedup against the randomly chosen block size is shown as average speed up in red.  
Figure 6: Execution time comparison of CSR and BCSR (with and without format conversion time).  
Figure 7: Speedup row sorted dataset (min to max).  
Figure 8: Speedup nnz sorted dataset (min to max).  
Figure 9: Cont.  
Figure 10 shows speedup of our proposed methodology against CSR (with and without) including format conversion time from CSR to BCSR). The speedup of BCSR execution (only) is represented in blue and red color represents the speedup of BCSR execution + format conversion time. Overall BCSR (including conversion time) showed speedup of 1.27 compared to CSR execution.
## 4 Results and Discussion
### The Simulation Environment  
In this part, we discuss the performance evaluation setting of TM2 RP using the testing environment in the Aqua-Sim package for Network Simulator 2 (NS2) to implement the underwater networking scenario [49]. A random topology with a different number of sensor nodes was performed (i.e., 25-400) in the deployed area of 1250 m\({}^{3}\). The transmission range was assigned to 250 m, with an initial energy of 100 J and data packet generation time equal to 15 s for source node with data packet size equal to 64 bytes. A hello packet interval was set to 100 s. In this case, every 100 \(s\), neighbor nodes were initiated with residual energy information for making forwarding decision. Next, distance based on the TM was calculated for prioritizing forwarding candidate neighbors. The energy model was utilized using the same as that used in VBF [32]. The power setting (2 w, 0.75 w, and 8 mw) represents the unit consumption level in data transmission, reception, and being idle at listening to the channel. We then employed a media access control (MAC) 802.11 - DYNAV protocol and the result was averaged from 25 runs [50]. We want to highlight that the DYNAV protocol is a potential improvement of 802.11 for underwater networking scenarios. It focuses on dynamically calculating the network allocation vector (NAV) for individual nodes, resulting in significant performance benefits to avoid unnecessary loner communication deferring by neighboring nodes. Table 1 below lists the simulation sitting. We want to clarify that the initial energy level consideration in any simulation experiment majorly depends on the network size, transmission range, and data size consideration for communication. We believe that our consideration of 100 J of initial energy in each sensor node was suitable for our experiment's network setting. We do agree that the realistic underwater sensor network is generally sparse in current application scenarios where the majority of the services are related to the normal monitoring of sea borders. However, when we think of next generation underwater smart and critical services such as underwater object recognition, micro mobility tracking, etc., the underwater sensor network should consider dense networking scenarios. Our consideration of a 250 m distance between sensor nodes is basically appropriate for such underwater networking applications.
## 3 Results
### Pressing Time  
The pressing time for CSO extraction in the three center-point STDs was 66.03 min. The observed pressing times for CSO extraction in center-point STDs were 80.00, 51.00, and 68.00 min (Table 2). The lowest pressing times for CSO extraction were predicted (46 min) and observed (80.00 min) in STD 7 (size of the restriction die: 1 cm; temperature: 60 \({}^{\circ}\)C; and duration of dry heat exposure at 100 \({}^{\circ}\)C: 30 min) and STD 1 (size of the restriction die: 1 cm; temperature: 40 \({}^{\circ}\)C; and duration of dry heat exposure at 100 \({}^{\circ}\)C: 0 min) (Table 2). The 2 FI model was utilized to evaluate variances in the pressing time for CSO extraction (Table 4).  
The 2 FI model for the pressing time of CSO extraction was significant (\(p\) = 0.0233) with adjusted R\({}^{2}\) and predicted R\({}^{2}\) values of 0.6175 and 0.5119, respectively, and a nonsignificant lack of fit (\(p\) = 0.9798). The results revealed that the 2 FI model equation was suitable for predicting the pressing time of CSO extraction.  
The CCD-generated 2 FI model equation for the pressing time of CSO extraction (min) was as follows (Equation (8)):  
\[\text{Time}=-79.6031+116.5072\text{A}+2.1827\text{B}+1.7049\text{C}-1.8126 \text{AB}+1.2082\text{AC}-0.0558\text{BC} \tag{8}\]  
where A is the size of the restriction die, B is the pressing temperature, and C is the duration of dry heat exposure.  
The size of the restriction die (\(p\) = 0.0258) and temperature (\(p\) = 0.0328) influenced the extraction time of CSO significantly (Table 5). RSM prediction showed that the reduced size of the restriction die and high temperature could reduce the pressing time needed for CSO extraction. However, the duration of dry heat exposure time did not affect the pressing time (Figure 6).  
Figure 5: Response surface plot illustrating the influence of restriction die size, temperature, and duration of dry heat time on the peroxide value of CSO. (**a**) Influence of restriction die and temperature (**b**) restriction die and dry heat time, and (**c**) temperature and dry heat time on the peroxide value of the CSO.  
Figure 6: Response surface plot illustrating the influence of restriction die size, temperature, and duration of dry heat time on the pressing time for CSO extraction. (**a**) Influence of restriction die and temperature (**b**) restriction die and dry heat time, and (**c**) temperature and dry heat time on the pressing time for CSO extraction.
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 4 Joint Digital Signal Processing Schemes for Carrier Recovery
### Algorithms for Joint Carrier Recovery
#### 4.1.2 Joint Phase Estimation  
Traditional, single-channel phase estimation schemes typically apply time-averaging filters on the estimated phase to remove the effects of additive noise. Since longer time-averaging decreases the ability to track fast phase changes, this creates a tradeoff between noise sensitivity and phase-noise tracking speed. The benefit of multi-channel phase estimation is that the averaging block can be extended to include several channels, which improves the tolerance to additive noise without increasing the time averaging length, thus increasing phase noise tolerance. In short, the SNR of the phase estimation can be increased without negatively affecting the tracking speed.  
Blind phase-search (BPS) [108] is a popular blind phase-estimation algorithm since it can be implemented in parallel without degrading tracking speed. Here, we describe how BPS can be extended to multiple channels. In the BPS algorithm, the received signal \(Z_{k}\), sampled at symbol rate, is first rotated with \(B\) test phase angles:  
\[\phi_{b}=\frac{b}{B}\cdot\frac{\pi}{2}, \tag{5}\]  
where \(b\) is the index of the test angles. Then, the distance to the closest constellation symbol is calculated as:  
Figure 7: (a) Master-slave frequency offset compensation for unsynchronized combs; (b) feedback phase alignment for joint phase estimation.  
\[|d_{k,b}|^{2}=|Z_{k}e^{-i\phi_{b}}-[Z_{k}e^{-i\phi_{b}}]_{D}|^{2}, \tag{6}\]  
where \([\cdot]_{D}\) denotes symbol decision. In a traditional BPS algorithm, the distances are then averaged over \(L+1\) consecutive symbols to reduce the impact of additive noise. When extending the algorithm to multiple channels, the averaging is extended to \(C\) channels, as well. This is described by:  
\[e_{k,b}=\sum_{c=1}^{C}\sum_{\ell=-L/2}^{L/2}|d_{c,k-\ell,b}|^{2}, \tag{7}\]  
where we added a channel index \(c\) to the distance. The estimated phase angle is chosen as the one that minimizes \(e_{k,b}\). The length of the summing window \(L+1\) is what determines the phase-tracking speed, where a smaller \(L\) gives a higher tracking speed. The total number of symbols used for the estimation, which determines the tolerance to additive noise, is \(N=C\cdot(L+1)\). In multichannel cases, the summing window length can be reduced by a factor of \(C\), which leads to a higher phase-noise tolerance, while maintaining the same tolerance to additive noise.
## 3 Results and Discussion
### BTEX Source Apportionment
On 28, 30 April, 3 May, and 21, 23, 25 October, B:T:E ratios were 0.32:0.64:0.04 and 0.48:0.50:0.02, respectively, which indicated the traffic and non-traffic-related BTEX sources. In July sampling days, three out of six sampling sites (S1, S2, and S6) depicted the distribution of B:T:E ratios in the coal combustion area. While, the distribution of B:T:E ratios of the rest of the sampling sites (S3, S4, S5) was in both (traffic and coal combustion) areas. The B:T:E ratios at sampling sites S3, S4, and S5 differed from S1, S2, S6 due to their closest location to the main roads. The correlation analysis of VOCs concentrations and BTEX source apportionment in Almaty showed the complex nature of air pollution and variation of the primary source depending on the season and sampling site. Figure 6: Benzene:Toluene:Ethylbenzene ratios in Almaty.
# Earth Rotation Parameters Estimation Using GPS and SLR Measurements to Multiple LEO Satellites
## 4 ERP Estimation Based on the Combination of Multiple LEO Satellites
### Combination of LEO Satellite and LAGEOS
It indicates that the combination of LEO satellites and LAGEOS can maximize the number of solved stations and strengthen the continuity of the station coordinate solutions.
## 2 Materials and Methods
### MuSyQ-GPP Algorithm  
The MuSyQ-GPP algorithm was designed as part of the multi-source data synergized quantitative (MuSyQ) remote sensing production system [12]. It presents an LUE approach to model the GPP using remotely sensed data and meteorological data with different spatial and temporal resolutions. The algorithm was adopted to generate the GPP at the site level in this research.  
The MuSyQ-GPP algorithm estimates GPP using the LUE theory described in Equation (4). It employs down-regulating scalars to characterize the effects of temperature and water stress on the \(LUE_{\rm P}\) as  
\[LUE_{\rm P}=LUE_{0}\times f_{1}(T)\times f_{2}(\beta) \tag{7}\]  
where \(LUE_{0}\) is the potential or maximum \(LUE_{\rm P}\) (gC mol\({}^{-1}\) PPFD), and \(f_{1}(T)\) and \(f_{2}(\beta)\) represent the down-regulation effects of temperature and water conditions on \(LUE_{0}\), respectively. A theoretical \(LUE_{0}\) value of 0.60 gC mol\({}^{-1}\) PPFD that was reported in a previous study [69] was used in this study.  
The temperature-limited effect on \(LUE_{0}\) is generated using two terms, \(T_{1}\) and \(T_{2}\)  
\[f_{1}(T)=T_{1}\times T_{2} \tag{8}\]  
where  
\[T_{1}=0.8+0.02T_{\rm opt}-0.0005T_{\rm opt}{}^{2} \tag{9}\]  
\[T_{2}=1.1814/\left\{\left[1+e^{0.2(T_{\rm opt}-10-T)}\right]\left[1+e^{0.3(-T _{\rm opt}-10+T)}\right]\right\} \tag{10}\]  
where \(T_{\rm opt}\) represents the optimum temperature, which is defined as 28 \({}^{\circ}\)C in our study as in [PERSON] et al. [70]The limited effect of water conditions on plant photosynthesis, which ranges between 0.5 and 1, is derived following the algorithm  
\[f_{2}(\beta)=0.5+0.5E/E_{\mathrm{P}} \tag{11}\]  
where \(E\) and \(E_{\mathrm{P}}\) represent the actual and potential evapotranspiration, respectively.  
In the MuSyQ-GPP algorithm, a modified Penman-Monteith (P-M) approach with biome-specific canopy conductance [71; 72; 73; 74] is used to estimate the actual evapotranspiration (see Text S1 in Supplementary Materials). The potential evapotranspiration is calculated using the Priestley and Taylor (P-T) equation [75].  
The original version of the MuSyQ-GPP model was established to estimate the daily GPP over regional and global scales [12]. In our study, we deployed its original algorithms to generate half-hourly averaged GPP at the site level to evaluate the performance of the LUE model.