esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Study of Energy Savings for Various Control Strategies in the Tunnel Lighting System
## 5 Discussion and Suggestions  
An optimal control strategy is obtained by minimizing the total cost of initial investment and electrical costs of tunnel lighting systems within the hardware system's lifetime.  
Figure 9: Electrical costs of a tunnel lighting system versus traffic volume and tunnel length for the ICS strategy.  
Thus, a factor of the total cost (i.e., installation and electrical costs of tunnel lighting systems) in the case of different control strategies is being investigated as follows:  
\[T_{i}=I_{i}+E_{DC,j}\cdot\mathbf{r}\cdot L_{t}, \tag{6}\]  
Subscript \"\(i\)\" refers to the control strategy type, \(I\) is the initial installation cost (see Table 6), \(L_{t}\) is the lifetime of the tunnel lighting system hardware or another predetermined period (days), and \(r\) is the electric retail rate ($0.2/kWh).  
The results showed that for \(L_{t}\) equal to or less than 218 days, the \(Q_{LCL}\) is smaller than \(Q_{DACS}\), \(Q_{TSCS}\), and \(Q_{ICS}\). This indicates that the LCL strategy lighting system consumes the least energy, and hence, it is the most suitable control strategy for a tunnel lighting system if \(L_{t}\) is less than 218 days. Also, for \(L_{t}\) equal to or greater than 955 days, \(Q_{ICS}\) is always smaller than \(Q_{LCL}\), \(Q_{DACS}\), and \(Q_{TSCS}\), which indicates that the ICS is the most suitable control strategy for this \(L_{t}\) range. For \(L_{t}\) between 219 and 954, the most suitable control strategy varies with tunnel length and traffic volume. Figure 10 illustrates the suitable control strategies for various \(L_{t}\) lengths (365 days, 730 days, 800 days, and 900 days) as a function of different tunnel lengths and traffic volumes. The DACS strategy has better energy efficiency in the blue-color area in Figure 10, whereas the ICS strategy has better energy efficiency in the red-color area.
## 5 Control Performance Evaluation
### White Noise-Excited Structure Responses  
The control performance of the TLIMD compared with TID and TMDI are quantified by the following two indices, defined as  
\[I_{\text{TLIMD-TMDI}}=\frac{I_{H\text{-TLIMD}}}{I_{H\text{-TMDI}}},\;I_{\text{ TLIMD-TID}}=\frac{I_{H\text{-TLIMD}}}{I_{H\text{-TID}}} \tag{31}\]  
where \(I_{H\text{-TLIMD}}\), \(I_{H\text{-TMDI}}\) and \(I_{H\text{-TID}}\) represent the variance of the TLIMD, TMDI or TID equipped with host structure responses, respectively. According to the definition, the TLIMD is considered to be a better perform than other dampers when \(I_{\text{TLIMD-TMDI}}\) or \(I_{\text{TLIMD-TID}}\) is less than 1.  
Figure 7 shows the variation tendency of the two performance indices (given in (31)) with various critical parameters. The concentrated mass \(m_{2}\) used in TLIMD and TMDI  
Figure 6: Schematic configurations of these three dampers.  
are set to the same, with its value varying from 0.005 to 0.1, i.e., \(\mu=\) 0.005-0.1. The inertance ratio \(\mu_{b}\) of TMID and TID are identical, taken as \(\mu_{b}=\) 0.05-1.0, and the length ratio \(\alpha_{1}=\sqrt{1+\mu_{b}/\mu}\) can be determined from the equilibrium \((m_{2}+b)_{\text{TMDI}}=(m_{2}\alpha^{2})_{\text{TLIMD}}\). The optimal parameters for the TLIMD are determined by (30), while the TMDI and TID are optimized by numerical technique. As seen, both indices are below 1 under the selected configuration cases, which exhibits the superiority of the TLIMD. As expected, the increases in mass ratio \(\mu\) and inertance ratio \(\mu_{b}\) are always beneficial to the TLIMD control performance. The enhancement of the structural mass ratio \(\zeta_{1}\) leads to a decrease of \(I_{\text{TLIMD-TMDI}}\), while inducing an increase in \(I_{\text{TLIMD-TID}}\). Nevertheless, the TLIMD still shows big advantages over those conventional devices, even under the larger structural damping scenario (such as \(\zeta_{1}=\) 10%).  
Figure 8 further evaluates the TLIMD control performance in terms of the frequency response function (FRF) of the host structure. As seen, the TMDI and TID provide almost identical result, while the TLIMD (indicated by the red dashed line) results in a smaller structure response. Thus, it is concluded that the TLIMD consistently outperforms the TMDI and TID in terms of the structural peak response when adopting the \(H_{2}\) calibration criterion.  
Figure 8: Frequency response function (FRF) of the host structure equipped with different kinds of dampers.  
Figure 7: TLIMD performance indices variation tendency.
# Compact Dual-Band Tapered Open-Ended Slot-Loop Antenna For Energy Harvesting Systems
###### Abstract  
In this study, a compact dual-band combined loop-slot planar antenna is proposed. (1) Background: multi-function antennas are desired for wireless communication to cover the desired frequency spectrum. (2) Methods: the proposed antenna consists of a semi-rectangular open-ended loop (OEL) operating at the lower frequency band 920 MHz, an open-ended slot (OES) transmission line that provides resonance at the higher band 2.4 GHz, and a feeding port using the asymmetric coplanar strip (ACS) line. The ACS is used to excite the antenna to achieve dual-band performance. The overall dimensions of the fabricated prototype are \(32.5\times 53.5\) mm\({}^{2}\) (\(0.1\lambda_{o}\times 0.16\lambda_{o}\)), where \(\lambda_{o}\) represents the free-space wavelength at the lower frequency. (3) Results: from the calculations, the antenna shows two impedance bandwidths (estimated at \(-10\)dB) of 30 MHz (920-950 MHz) and 300 MHz (2.2-2.5 GHz) to cover the ISM band (920 MHz) and 2.45 GHz WiFi bands, respectively. Indeed, the antenna has stable radiation patterns and achieves peak measured realized gain of 1.8 dBi in the lower band and 4.2 dBi in the higher band. (4) Conclusion: the antenna shows the merits of low profile structure, single-layer, and low-cost fabrication. The proposed antenna not only achieves incremental increase in radiation efficiency, but also provides a lightweight, and small footprint.  
asymmetric CPW; dual-band; energy harvesting; loop; slot +
Footnote †: journal: Electronics Research Institute, Giza 12622, Egypt.
# Enhancing the 3 GPP V2X Architecture with Information-Centric Networking
## 5 3 GPP V2X-NDN Forwarding Strategy  
### Content Naming Scheme  
NDN naming schemes are not standardized yet. Granted the proposed architecture, we expect that 3 GPP can play a crucial role in pushing the specifications of NDN naming conventions, in liaison with actors in the automotive vertical (e.g., road transportation authorities and car manufacturers).  
Figure 2: The NDN-based protocol stack for UE and eNodeB.  
We assume that V2X applications can rely on semantic-rich hierarchical NDN naming schemes, which reflect the geographical and temporal scores of the contents, similarly to the work in [37; 38]. The V2X Control function can be responsible for naming parameters configuration in UEs, as it does for other V2X related parameters (e.g., the radio resources pool for out-of-coverage operation). Specifically, the V2X Control function pre-configures the UEs, or configures them on-the-fly through eMBMS signaling, with some well-known name prefixes and structures. Without loss of generality, as in [37], we propose a V2X namespace for non-safety content retrieval composed of the following elements: (i) _application type_ (e.g., infomobility and convenience); (ii) _the data type_ to be collected (e.g., average speed, high-definition map, charging station availability, fees and locations); (iii) _geo-location_, concerning a specific geographical area (e.g., a road segment within a highway) from where a UE requests data; and (iv) _timestamp_, which specifies the time range within which the requested data must be generated. For instance, the name _/infomobility/highwaySA-RC/north/avsspeed/11-12-09112019_ is used to request average speed conditions on the Salerno-Reggio Calabria highway in the north direction between 11:00 and 12:00 on 6 July 2019. The high volatility of V2X data makes vehicular traffic information to vary from instant to instant, while the freshest data are requested.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stressor** & **Enhancement Operation** & **Design Rationale** & **Application** & **References** \\ \hline Shadowing of the water table in the river, poor light conditions & Partial removal of trees along river banks Local widening of the riverbed & Improvement of light conditions (up to 50\%) & Skerne River (UK) & [28; 29; 30] \\ \hline Shallowing of the river channel & Wooden bars Double grownes & Dredging with the use of wooden bars, and single and double grownes & Avon River (UK) Lewis and Clark River (USA) & [29; 30; 31] \\ \hline Straight and channelized river channel with low heterogeneity of habitats & Wooden bars Single and double grownes & Diversification of the midstream & Skerne and Cole Rivers (UK) Molealume River, American River, Cuba River (USA) Lawrence River & [29; 30; 31] \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Straight and channelized river channel with low heterogeneity of habitats \\ \end{tabular} } & Wooden bars Single and double grownes & Diversification of the midstream & Skerne River (USA) Molealume River, American River, Cuba River (USA) Lawrence River & [29; 30; 31] \\ \cline{2-5}  & Mid-riverbed stone islands & Diversification of the shoreline (alternate concave and convex shores) & Skerne River & [29; 30; 31] \\ \hline \multirow{4}{*}{Water pollution} & By-pass creation, reconnection of former meanders & Forming riverbed sections parallel to the “old” riverbed, restoring connections with an oxbow lake & Cole River (UK) & [29; 30] \\ \cline{2-5}  & & Introduction of native plants along the stream and bioengineering structures to stabilize the stream channel and reduce stream bank erosion and sediment loads in the stream. & Avone, Marden, and Kennet Rivers (UK) & [28; 33] \\ \cline{1-1}  & & Introduction of macrophytes which are capable of assimilating chemical elements & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of stressors and solutions for successful stream restoration. the key interactions between the physicochemical parameters of the river system were considered. Therefore, the aim of this study was to present the treatments applied in the project to restore the Kwacza River ecosystem, to identify the most important correlations between hydrotechnical treatments and abiotic components, to predict the effects of the restoration project six years after its completion, and to analyze various methods for evaluating the progress of restoration. We hope that the presented results will be useful in other restoration projects, and will provide a rational basis for setting appropriate and cost-effective goals for ecosystem recovery.
# Potential of Waste Oyster Shells as a Novel Biofiller for Hot-Mix Asphalt
## 3 Results
### Thin-Layer Chromatography-Flame Ionization Detection (TLC-FID)  
To gain deeper insight into the impact of oyster shell powder (OSP) on the composition of asphalt cement, this latter was divided into SARA (i.e., saturates, aromatics, resins, and asphaltenes) fractions by using standard procedure based on adsorption chromatography (e.g., TLC-FID) [32]. Figure 6 pinpoints the distribution of SARA type hydrocarbons in the base asphalt (i.e., AP-5 OSP 0 wt %) and asphalt treated with different proportions (e.g., 5, 10, and 15 wt %) of OSP. Curiously, it shows that the composition of asphalt changed after the addition of OSP. The fraction of aromatics components decreased; meanwhile, the fraction of resins increased. The remaining species such as saturates and asphaltenes did not undergo any significant changes. The changing of such composition fraction implies that the aromatics in lighter fractions of asphalt were more probably changed to resins. In other terms, it seems that the aromatics were selectively interacted with OSP surface [49; 50] and readily converted into resin-like structures (see Figure 7). The oyster shell powder (i.e., CaCO\({}_{3}\)) with high calcium content is naturally hydrophilic material and tends to create strong bonds with hydrophobic organic compounds such as bitumen. The strong bonding between the calcite (mainly calcium sites) and adsorbate aromatic molecules most likely involved the carboxylic, hydroxylic, and amines groups; some van der Waals and electrostatic interactions may have also occurred [49; 50]. This is a very interesting finding that necessitates further in-depth investigation.  
Figure 5: XRD patterns of OSP, neat asphalt, and OSP-filled asphalts.
## 2 Materials and Methods
### Opto-Electronic Monitoring System  
To monitor the integrity and breakage of the optical fibers of the crack detection array, a special optoelectronic system was used, the configuration of which is schematically shown in Figure 5.  
The system monitored the intensity of the optical radiation transmitted through all optical fibers of the array by the multichannel optical transmitter, Tx. The breakage of the optical fiber led to a decrease in the signal intensity. The signals of all optical fibers were received by the multichannel optical receiver, Rx, and converted from analog to binary (two-level) form, that is, \"1\" or \"0\", by comparing the signal values against a given value. The zero level was interpreted as a structural crack, and the state of the corresponding color indicator on the display changed from green to red.  
The optical radiation generated by the transmitter Rx was a continuous 1 kHz sine optical pilot signal. The light sources were LEDs operating at a wavelength of 633 nm (visible red light). Each LED was coupled to one of the optical fibers of the array. The outputs of the optical fibers were connected to the respective photoreceivers. Each measuring channel had a threshold amplitude device, the purpose of which was to automatically signal a fiber break. This device turned on a light indicator on a special display when the signal dropped below a certain level. The threshold was set at 50% of the nominal, as it was assumed that such a threshold would minimize the false alarms of the signaling system caused by possible fluctuations in light sources and all kinds of noise.  
For this experiment only, the monitoring system was complemented with a 16-channel data acquisition device (DAQ) and a CPU that stored the data of all measuring channels in digital form. It should be noted that neither a DAQ nor a CPU is needed for a practical crack detection system. Instead, a practical system would require a means of transmitting alarms to the system users. (The cost of all elements and components of this monitoring system is about USD 300. The wholesale cost of the multimode optical fiber is about USD 50 per 1 km.).
# Central Kurdish Text-to-Speech Synthesis with Novel End-to-End Transformer Training
## 6 Results and Discussion
### Audio Quality  
We evaluated the generated synthetic audio files in the test set to obtain the MOS to measure the audio quality. We kept the sentence content consistent among the different models so as to exclude other interference factors and avoid biases by only examining the audio quality. Each audio was listened to by at least 54 evaluators who were all native speakers of Central Kurdish. We compared the MOSs of the audio samples generated by our KTTS model with those of other well-known TTS models, which included (a) the GT (the ground-truth audio), (b) Tacotron 2 [1] (Mel + multi-band MelGAN [40]), (c) VITS [13], a conditional variational autoencoder with adversarial learning for end-to-end text-to-speech conversion, and (d) Glow TTS [20], a generative flow for text-to-speech conversion via monotonic alignment search (Mel + multi-band MelGAN). For each model, only ratings greater than one were considered, while those equal to or below this threshold were excluded from the analysis. The results are shown in Table 3. It can be seen that our KTTS outperformed the mentioned one-stage TTS system, and it reached the quality of the two-stage TTS systems.
## 3 Results and Discussions
### Effects of pH and Temperature on the Solution Composition  
The effect of pH and \(T\) on the composition of solution in the scrubbed solution can be determined by \({}^{13}\)CNMR spectra, as shown in Figure 4a-d for No. 1, 5, 9, and 13, respectively. In order for a comparison, the data was adopted using the same gas flow rate, at the rate of 3 L/min, but different pHs and temperatures of the solutions we used. In Figure 4, the major peaks were presented at 42, 45, 164, 174, and 179 ppm, which shows \(GLY/GLH^{+}\) at 42 ppm and 174 ppm \((A^{\prime},B^{\prime})\), \(GLYH^{+}CO_{2}^{-}\) in 45 ppm and 179 ppm \((A,B)\), and the \(HCO_{3}^{-}CO_{3}^{2-}\) in 162 ppm (C) [37; 38]. The peak at 174 ppm shifted to 179 ppm, and disappeared when the pH value increased to 11, indicating the disappearance  
Figure 3: Schematic diagram of the apparatus used in the regeneration experiment. (1) Cooling system; (2) hot plate with stirrer; (3) oil bath; (4) three-neck round flask; (5) ball condenser tube; (6) electronic thermometer.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \(E\) & \(R_{A}\times 10^{4}\) & \(K_{C}a\) & \(\gamma\) & \(\Phi\) & \(\alpha\) \\ \cline{2-7}  & **(\%)** & **(mol s\({}^{-1}\) L\({}^{-1}\))** & **(s\({}^{-1}\))** & **(-)** & **(mol mol\({}^{-1}\) L\({}^{-1}\))** & **(mol mol\({}^{-1}\))** \\ \hline
1 & 55.33 & 2.30 & 0.051 & 4.34 & 0.4079 & 0.5044 \\
2 & 47.40 & 3.74 & 0.074 & 4.71 & 0.3892 & 0.4961 \\
3 & 36.54 & 4.37 & 0.077 & 6.72 & 0.4339 & 0.5605 \\
4 & 25.64 & 4.21 & 0.070 & 8.89 & 0.4028 & 0.6061 \\
5 & 73.03 & 3.14 & 0.088 & 3.06 & 0.3845 & 0.3671 \\
6 & 44.81 & 3.46 & 0.065 & 4.77 & 0.3727 & 0.4167 \\
7 & 62.82 & 7.14 & 0.173 & 2.70 & 0.2995 & 0.3147 \\
8 & 44.67 & 6.77 & 0.136 & 4.54 & 0.3448 & 0.4022 \\
9 & 78.57 & 3.30 & 0.102 & 2.56 & 0.3513 & 0.3372 \\
10 & 67.95 & 5.34 & 0.138 & 2.41 & 0.2887 & 0.2894 \\
11 & 49.34 & 5.78 & 0.116 & 3.42 & 0.2902 & 0.3388 \\
12 & 56.41 & 8.56 & 0.185 & 2.52 & 0.2512 & 0.2672 \\
13 & 86.84 & 3.45 & 0.132 & 0.87 & 0.1298 & 0.1110 \\
14 & 80.52 & 5.90 & 0.189 & 0.94 & 0.1326 & 0.1081 \\
15 & 55.92 & 6.48 & 0.144 & 2.81 & 0.2707 & 0.2741 \\
16 & 50.00 & 7.49 & 0.152 & 3.03 & 0.2604 & 0.2752 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Data obtained in the Taguchi experiments. \(E\)—absorption efficiency; \(R_{A}\)—absorption rate; \(K_{C}a\)—overall mass transfer coefficient; \(\gamma\)—flow rate; \(\alpha\)—loading of CO\({}_{2}\).  
of carbamate, and hence an increase in the absorption of CO\({}_{2}\). The effect of pH on the absorption of CO\({}_{2}\) can be explained in \(E\) and \(K_{\rm{G}}a\), as shown in Table 4; the values of \(E\) and \(K_{\rm{G}}a\) are 55.33 and 0.051, 73.03 and 0.088, 78.57 and 0.102, and 86.84 and 0.132 for No. 1, 5, 9, and 13, respectively. It was found that \(E\) and \(K_{\rm{G}}a\) increased with an increase in the pH value and \(T\).  
Figure 4: \({}^{13}\)CNMR spectra of sodium glycinate solution after the absorption of CO\({}_{2}\) gas: (**a**) No. 1; (**b**) No. 5; (**c**) No. 9; (**d**) No. 13.
## 4 Findings and Results
### Model Comparison  
The generated plot provides a comprehensive comparison of seven supervised machine learning models based on four critical performance metrics: accuracy, sensitivity, F1 score, and AUROC. The support vector machine (SVM) and decision tree models demonstrated superior performance across most of these metrics, with both achieving the highest values for accuracy and F1 score (0.988 and 0.993, respectively). Furthermore, these models exhibited very high sensitivity (0.993), suggesting that they are particularly adept at identifying positive cases. This consistent performance across multiple metrics indicates that the SVM and decision tree models are highly effective and reliable for classification tasks.  
Random forest, while also performing admirab in terms of accuracy and sensitivity, distinguishes itself with the highest AUROC (0.988). AUROC is a measure of the model's ability to distinguish between classes, with a higher value indicating better discriminatory power. This makes RF particularly suitable for applications where distinguishing between positive and negative classes with high precision is crucial. Conversely, the naive Bayes and K-nearest neighbors (KNN) models exhibited slightly lower AUROC values (0.971 and 0.973, respectively), suggesting a marginally reduced ability to differentiate between classes. Despite this, they still maintain high levels of accuracy and sensitivity, indicating their overall robustness as classifiers.  
The visualization effectively illustrates the comparative strengths and weaknesses of each model, offering valuable insights into selecting the most appropriate model based on specific performance criteria as presented in Figure 4. For instance, if the primary objective is to maximize discriminatory power, the random forest model is the optimal choice because of its superior AUROC. Conversely, if balancing precision and recall is more critical, models such as SVM and decision tree, with high F1 scores, would be preferable. This nuanced comparison allows for researchers and practitioners to make informed decisions tailored to their specific application needs, ensuring that the chosen model aligns with performance priorities.
## 4 Results
### Evaluation Result for Count Analysis  
The count analysis results for the 3U CubeSat are shown in Table 2. Both the part and screw count show a considerable amount of change in favor of the slot-based design concept. A 42% reduction is achieved by changing the interface method of internal subsystem, since the design merit is out of the equation because the designs of major structural parts in both concepts are similar in shape and quantity. Such a significant improvement, from 14 to 8, attained due to the reduction in the number of structural parts, such as the connecting frames and long rods that hold the PCB stacks and spacers. The conventional design utilizes many spacers that must be mounted between each stack, whereas in the case of the slot design, only four spacers are used for each PCB. The number of structural screws, however, does not show substantial improvement, being only 5%. This is because this STM utilizes several redundant structural screws. This point was taken as a potential area for improvement on the current EM model.
## 3 Results and Discussion
### Evolution of Side Pressures  
Figure 9 shows the pressures curves at each regenerator side. The pressure amplitude is weakly sensitive to frequency for the Str\(\_\)30% structure (Figure 9a). For the Str\(\_\)35% and Str\(\_\)40% structures, the pressure amplitude increases by approximately 0.2 bar between the lowest and highest frequency (Figure 9b,c). This corresponds to 20% of the average pressure. This phenomenon is probably due to the fact that the compressibility of the fluid in the presence of the Str\(\_\)30% structure is greater than with the other two structures.  
In Figure 9b,c the pressure maximum amplitude on either side of the regenerators (Str\(\_\)35% and Str\(\_\)40%) does not match the predicted theoretical temporal forecasts. The pressure reaches its maximum value for \(\theta=120^{\circ}\) at a low frequency. For higher frequencies, the maximum is obtained just before the neutral point (aka TDC), i.e., \(\theta=180^{\circ}\) instead of \(\theta=90^{\circ}\). This shift also occurs for Str\(\_\)30% (Figure 9a), regardless of the frequency, and always for \(\theta=180^{\circ}\). This phenomenon is probably due to compressibility, which is greater for the Str\(\_\)30% regenerator. This could explain the most disturbed structure and the lowest porosity. These results are confirmed by those obtained at different frequencies.  
Figure 9: Evolution of pressures at hot and cold inlet sides of regenerator ((a): Str\(\_\)30%, (b): Str\(\_\)35%, (c): Str\(\_\)40%. \(L_{1}=60\) mm, \(C_{1}=64.4\) mm) versus phase for temperature gradient \(\Delta T_{1}=70\,^{\circ}\)C/\(10\,^{\circ}\)C.  
This difference between the theoretical and experimental results was also experimentally observed by [PERSON] and [PERSON] [8].