esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Bandwidth Reconfigurable Patch Antenna for Devices in WLAN and UWB Technology Applications
## 2 Materials and Methods
### Equivalent Circuit Model of Pin Diode  
A PIN diode can be used as a switch to modify the electrical length of the antenna, influencing its resonant frequency and radiation pattern. Two diodes (BAP65\(-\)02, 115) were used in our configuration [39], which can be modeled as a series or parallel combination of passive components (Figure 4), to achieve the desired reconfiguration of the patch antenna. When the DC bias voltage is above a certain threshold, the PIN diode can be modeled as a serial combination of resistance \(R_{s}=1\,\Omega\) and inductance \(L_{s}=0.6\,\)nH. When the DC bias voltage is below the activation threshold, the diode can be modeled as a parallel combination of \(C=0.5\,\)pF capacitor and \(R_{h}=20\,\)k\(\Omega\) resistor in series with \(L=0.6\,\)nH inductance.  
Figure 5 shows the comparison of \(S_{11}\) parameters between the various operating modes providing the switching states of the PIN diodes. The study found that two of the four ON-OFF state combinations of the PIN diodes provided a bi-band, i.e., the antenna operated at two resonance frequencies.  
* When the two PIN diodes are on, the two resonance frequencies are at \(2.5\,\)GHz and \(4.4\,\)GHz, with a return loss of \(-16.9\,\)dB and \(-34.9\,\)dB, respectively.
* When diode D1 is turned on and diode D2 is off, the two resonance frequencies are at \(2.8\,\)GHz and \(5.3\,\)GHz, with a return loss of \(-14.85\,\)dB and \(-30.35\,\)dB, respectively.
* When diode D1 is switched off and diode D2 is on, there is only one wide bandwidth of \(2.41\,\)GHz impedance.
* When both diodes are turned off, the antenna does not have a band.  
In general, the study shows that the proposed antenna with two PIN diodes has multiple operating states, which can be used to achieve different frequency bands and bandwidths. Figure 6 shows the proposed antenna peak gain for the configuration with both diodes switched on as a function of frequency. As can be  
Figure 4: Equivalent circuit model of the PIN diode.  
Figure 5: Reflection coefficients of the proposed antenna for the different states of two PIN diodes.  
increases, the peak gain also increases. This could be explained by the fact that the size of the patch becomes larger than its wavelength when the frequency increases to 4.29 dB. However, the increase in gain is limited by the antenna's efficiency and losses, including radiation losses and conductor losses. At higher frequencies, the conductor losses due to the skin effect become more significant, leading to a decrease in efficiency and gain. This is why the gain of the antenna starts to decrease at higher frequencies, as seen in Figure 6. Additionally, at very high frequencies, the antenna's ability to efficiently capture or radiate the signal becomes more difficult due to limitations in the antenna design and size.
## 4 Selection and Analysis of Resonant Compensation Network
### Selection of Resonant Compensation Networks
#### 4.1.1 Low-Order Resonant Compensation Networks  
Low-order resonant compensation networks are mainly divided into four typical topologies: Series-Parallel (S-P), Series-Series (S-S), Parallel-Parallel (P-P), and Parallel-Series (P-S) [25]. Since the outputs of the proposed inverter are voltage sources and cannot be connected in parallel with capacitors, this paper only considers S-P and S-S in the low-order resonant compensation networks. Their topology and operating characteristics are shown in Figure 9 and Table 2, respectively. Specifically, the S-P resonant compensation network can provide stable impedance matching within a certain range by combining series and parallel capacitors and can achieve CV output. However, its resonant frequency is related to the value and position of the load, and it is prone to entering a detuned state. The S-S resonant compensation network forms a bilateral LC series resonant through series capacitors in the transmitting and receiving sides, which can achieve high efficiency and power density, strong resonant frequency stability, and CC output. Therefore, this paper chooses the S-S resonant compensation network to provide CC to the loads.
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 3 System Model
### Two Nodes Reliable Model (2n-Rm)
#### 3.2.5 Selection Process of Reliable Link  
In 2N-RM, source node sends hello packet towards its neighbors. Hello packet format only consists of node id. Nodes receive hello packet and send the acknowledgment to source node. Acknowledgment packet format consists of receiver node id, location, and residual energy. After that, source node calculates physical distance and compares its location with other nodes. Nodes having higher Y axis and their distance is greater than 70 m will be discarded from the queue. Link has been established from source to the selected nodes and reliability of each node is measured through ETX value by sending 10 hello messages after a specific time interval. \(Sr\) and \(Ar\) are calculated and the resulted value of each link is stored in a queue. All of the results are saved in ascending order. Link has closer value to 1 will be selected as a most reliable route.  
In the case of an infected node or a node having lower residual energy, such type of sorting reduces the time that is required by 2N-RM for the selection of other nodes. By taking the advantages of tree properties, we find the optimal path with respect to the maximum number of nodes participation. For this purpose, the Iterative Depth First Search (IDFS) algorithm is used. IDFS sorts the paths and provides us the optimal path. After selecting the optimal path, data packets will be forwarded towards the sink node. Nodes residing in predefined limit have approximately same ETX value and, due to harsh underwater environment, a node having lower distance as compared to other will be selected (see Figure 6).  
Figure 5: Tree Topology of Establishing Paths.  
Figure 6: Next Forwarder Selection.
## 3 On-Site Measurements
### Tunnel Section
#### 3.1.2 Immission Measurements  
After completing the admittance measurements, the tunnel sensors (abutment and walls) remained in place for 4 weeks until the next maintenance window. During this period, they recorded the immissions of all trains passing through the tunnel. This yielded  
Figure 8: Vibration velocity signals measured during the vibrotruck sweep excitation (**a**,**c**) and the excitation during impact testing (**b**,**d**). (**a**,**b**) show raw time signals and spectrograms of vibrations velocities 2 m from the source; (**c**,**d**) show raw time signals and spectrograms of vibrations velocities 97 m from the source.  
Figure 9: Point transfer admittance spectra at different sensor points in the tunnel and at the surface (distance from source given in brackets) when using different excitation sources at Q2.  
over 3000 train passages (including approximately 1000 high-speed long-distance trains, about 800 regional trains and over 600 freight trains). The sensors at the surface were reinstalled repeatedly for a few days at the same locations as during the admittance survey in order to simultaneously capture surface impressions of the same trains. This yielded records for 42 high-speed long-distance trains, 22 regional trains and 18 freight trains, the exact number depending on the sensor location and signal-noise ratios.  
To show some examples, Figure 10 summarizes the vibration velocity immission spectra at the abutment sensor Q1 for the most frequent train types. The recorded passages were divided into six speed classes per train type (in km/h: <90, 110-130, 130-150, 150-170, 170-190 and 190-240). The solid lines in Figure 10 indicate the mean impression spectrum per speed class, and the surrounding hatched area represents the range between the 5 th and 95 th percentile of the respective category. The number of trains per category are given in brackets.
# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 7 Open Source Approaches  
Open source RAN software enables the RAN software to be accessible to the majority of people, thus bringing innovation to the RAN technology. Currently, academics and research institutions can use open source RAN platforms, such as srsRAN, OAI, and OSC, to provide solutions to challenges faced in by the industry in RAN technology. As a result, more and more research is being carried out and more papers published that leverage the open source RAN platforms [33; 46; 47; 74; 75; 91; 134]. Some of these researchers also open up their work to be used for free, thus widening the open source RAN platforms available. These researchers help provide feedback on the shortcomings of open source RAN platforms and also on O-RAN Alliance specifications. The solutions proposed by these researches can also be used for new feature development in O-RAN specification. It can be expected that open source will be one of the research directions for the future of Open RAN.  
Most 5G products currently use commercial software provided by software design house companies. Open-source RAN software is not used for commercial products. Open source can be used as a reference design or a proof of concept. However, with the rise of open-source RAN software platforms, we are starting to see the utilization of open-source software in commercial Open RAN products. BubbleRAN is an example of a company that uses open source as its core technology [135]. BubbleRAN is a startup company that aims to accelerate innovation in multi-vendor 5G through open source. BubbleRAN uses OAI's experimental RT wireless platforms and Mosaic5G's agile 5G platforms to develop their products. BubbleRAN's products would offer the basic functionalities in the open-source platforms mentioned before. BubbleRAN provides extra features on their commercial products to support the features of particular solutions their customers require.
# Enhanced FANO Structure Based on Tip-Field-Enhancement Theory
## 2 Simulations  
The proposed structure is illustrated in Figure 1a. It looks like two asymmetric crescents placed together oppositely. This shape is created by cutting an ellipse off of a circle that has the same center and cutting a rectangle with a different center. The radius of the circle was 166.5 um, the major axis of the ellipse was 325 um, while the minor axis was 285 um, and the edge of the rectangle going across the center of the circle had a height of 38 um. Asymmetric arcs make the resonant frequency lie nearby, thus the resonances could affect each other through coupling, leading to out-phase-to-in-phase conversion, forming a FANO resonance. FANO resonant frequency of the structure was 420 GHz. A THz wave was projected vertically into the substrate of the structure as shown in Figure 1b, with the E-field polarization perpendicular to the major axis. We tuned the major axis of the ellipse so that the  
The highest Q could be found with 190 um substrate thickness in the simulation, as shown in Figure 4. The structure we proposed reached a 3 dB Q factor up to 120 compared with 50 of the typical structure, and \(-20\) dB resonance intensity compared with other high-Q structures whose transmission is higher than \(-10\) dB [17]. The relationship between Q factor, resonance intensity and minimum width of the structure was analyzed, as shown in Figure 6. Dots are simulation results and solid lines are the fitted curve. It can be observed from the figure that there was negative correlation between Q factor and minimum width. Resonance intensity and Q factor also showed negative correlation, as described above. In this model, resonance intensity changed in the process of setting the minimum width from 4 to 24 um, and 24 um was the maximum width of the structure. By changing the minimum width under the circumstances of fixed maximum width, sharpness was tuned. However, the transmission was \(-21.46\) dB, still smaller than \(-20\) dB, which still allowed the easy detection of the resonant frequency swinging.  
Figure 4: Simulation result while tuning structure’s minimum width. minW: minimum width. Maximum width of the structure was fixed, so minimum width indicates tip sharpness. Solid lines: transmission characteristics of structure with sharpest tips and unsharpened structure. With minimum-width increase, 3 dB bandwidth increased, thus its 3 dB Q factor decreased.  
Figure 5: Model schematic. Charge is more likely to accumulate at sharp tips, leading to intense coupling between tips, enhancing FANO resonance.
# Targeted Delivery Strategies for Hydrophilic Phytochemicals
## 2 Delivery Systems for Hydrophilic Phytochemicals
### Polymeric Nanoparticles
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Group** & **Structure** & **Size** & \multicolumn{2}{l}{**Preparation/Isolation Methods**} & \multicolumn{1}{l}{**Advantages**} & \multicolumn{1}{l}{**Limitations**} & \multicolumn{1}{l}{**References**} \\ \hline  & & & & - ultracentrifugation & & & \\  & & & - ultrafiltration & & & \\  & & & - size-exclusion & - biocompatible & & & \\  & Single lipid bilayer & & chromatography & - biodegradable & - short-half life in & & \\  & containing RNAs, & -30–200 nm & - polymer precipitation & - low toxicity & circulation & [47, 48, 50, 51, 80, 81, 82, 83, 84] & \\  & proteins and lipids & & - magnetic separation & - low immunogenicity & - special storage conditions & [80, 81, 82, 83, 84] & \\  & & & - acoustic fluid separation & - innate stability & are required & \\  & & & - immunological separation & - targeting delivery & & \\  & & & - dielectrophoretic & & & \\  & & & & separation & & \\ \hline  & & & & - biodegradable & & \\  & & & - low toxicity & & & \\  & & & - dialysis & - high stability & & \\  & & & - emulsification diffusion & - both hydrophilic and & & \\  & Solid core with & - nanospheres & - interfacial polymerization & hydrophobic compounds & & \\  & polymeric matrix & (10–200 nm) & - nanopeprictition & delivery & - possible degradation & \\  & & & - phase inversion & - long shelf life & - possible monomer & [53, 85, 86, 87, 88, 89] & \\  & & & & temperature & - both hydrophilic and & & \\ nanoparticles & Inner liquid or solid & - nanocapsules & - salting out & hydrophobic compounds & aggregation & \\  & & & (50–300 nm) & - super critical fluid & delivery & \\  & & & technology & - controlled release of active & \\  & & & - solvent evaporation & compounds & \\  & & & & - high loading capacity & \\  & & & & - targeting delivery & & \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._While the aforementioned delivery platforms offer a wide range of strategies for enhancing the dermal and systemic availability of hydrophilic compounds, their effectiveness ultimately depends on the molecular characteristics of the compounds themselves. Therefore, the following section presents a detailed analysis of selected polyphenols, focusing on their chemical structure, solubility, biological activity, and formulation-specific delivery challenges. This compound-level perspective provides essential context for understanding how delivery systems are matched to molecular needs.
## 5 Discussion
### Security Analysis  
In the proposed scheme, we need to stack all shares to fully recover the original secret image; otherwise, the secret image cannot be seen. For each pixel, only one share is encrypted, the others are the same as the cover. When we only stack \(k\) (\(k<n\)) shares, if the encrypted shares are not collected, it is the same as what we do for the XOR operation for \(k\) random values, so the original secret image cannot be reconstructed. Similarly, if we have the encrypted share but lack any other share on these \(k\) shares, a random value is lost, so we cannot fully recover the original secret image. If we try to guess the value of an encrypted share or missing share, it is equivalent to guessing a random value in the range of 0 to 255. As a result, we can ensure that only those who have all the shares can reconstruct the secret. This means that the proposed schemes are secure. Moreover, binary (black-and-white) images are not common nowadays. Therefore, if we have binary images on our cellphone or computer, it may attract malicious attacks. Hence, the proposed schemes are more practical and safer than existing schemes  
In the proposed scheme, the pixels used in all but one shares are equal to the pixels of the cover images, and the pixel of the last share is calculated solely from the pixels of the secret image and all other shares. Therefore, if one cannot obtain all the shares, the secret image cannot be restored. To demonstrate that our scheme is secure, we stacked the  
Figure 11: The secret and 15 different covers: (**a**) cannon; (**b**) boat; (**c**) cartoon; (**d**) architect; (**e**) cars; (**f**) Barbara; (**g**) baboon; (**h**) castle; (i) scientists; (**j**) pepper; (**k**) agriculture; (**l**) butterfly; (**m**) houses; (**n**) hill; (**o**) feather; (**p**) secret.  
partial, incomplete \(k\) shares in Figures 8 and 10 for observation (\(k<n\)). Figure 12a shows that we stacked two shares: Figure 8h,i. Figure 12b shows that we stacked another two shares: Figure 8k,l. Figure 12c shows that we stacked three shares: Figure 8h-j. Figure 12d shows that we stack another three shares: Figure 8h,i,k. Figure 12e shows that we stacked four shares: Figure 8h-j,l. Figure 12f shows that we stacked another four shares: Figure 8i-l. Figure 12g shows that we stacked two shares: Figure 10i,k. Figure 12h shows that we stacked another two shares: Figure 10h-j. Figure 12i shows that we stacked three shares: Figure 10h-j. Figure 12j shows that we stacked another three shares: Figure 10h-j. Figure 12l shows that we stack four shares: Figure 10h-j,l. Figure 12l shows that we stack another four shares: Figure 10h,j-l.  
From Figure 12, we can see that if we only stacked \(k\) (\(k=2\), \(3\), \(4\)) shares, it generated a meaningless image. Because we did not stack all of the shares, these stacked images lost their meaning. As a result, we were able to ensure that our scheme was secure since the secret image could not be reconstructed until all shares were collected.
## 3 Results
### Sensitivity Analysis  
Table S5 shows that valorisation of either Scenarios 2 and 3, which both consider transport distances of 20 km and 50 km, are environmentally more advantageous than direct landfill. As expected, the environmental impacts in the 18 categories of impact increase proportionally with the increase of the transport distance by percentages from 1% and 3% (20 km) to 4% and 10% (50 km) in Scenarios 2 and 3, respectively. Figure 5a-d shows the details of the results obtained for four of the most relevant categories of impact.  
Figure 5: Sensitivity analysis of the environmental impacts in (**a**) Particulate matter formation, (**b**) Human toxicity, (**c**) Fossil depletion, and (**d**) Climate change categories of impact (ReCiPe2016 Midpoint (H)) for Scenario 1 (GS disposal in landfills), Scenario 2 (GS valorised as a substitute for feldspar in ceramic materials), Scenario 3 (GS valorised as a substitute for fine–medium inert filler in concrete mix) considering transport at 20 km and 50 km distance from the sawing plant. All values are referred to the functional unit 1 m\({}^{2}\) granite slab. (Cut-off 0.1%).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Unit** & **Scenario 1** & **Scenario 2** & **Scenario 3** \\ \hline Total costs & \(\text{\textc/m}^{2}\) & 8.41 & 8.14 & 8.26 \\ \hline Disposal in landfill & & 2.00 & 0 & 0 \\ Electric Energy & & 10.93 & 11.24 & 11.08 \\ Heat & & 0 & 0 & 1.43 \\ Lubricating Oil & & 0.11 & 0.11 & 0.11 \\ Polyacrylamide & \% & 0.01 & 0.01 & 0.01 \\ Raw Material & & 85.98 & 88.43 & 87.17 \\ Steel & & 0.01 & 0.01 & 0.01 \\ Stucco & & 0.2 & 0.21 & 0.20 \\ Transport to Landfill & & 0.77 & 0 & 0 \\ \hline \end{tabular}
\end{table}
Table 8: Life-Cycle Costs and process cost contribution in Scenarios 1–3.  
Regarding the economic cost, the sensitivity analysis showed that the transport of GS may be responsible for an increase in the total cost from 1.96% and 1.84% (20 km) to 3.81% and 3.56% (50 km) in Scenarios 2 and 3, respectively. Transport distances longer than 40 km generate costs higher than the GS disposal in the landfill for both Scenarios 2 and 3 (Figure 6).
# Audio-Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model
## 3 Proposed AVSR Method Based on DCM Attention
### Seq2 seq Transformer
#### 3.2.3 DCM Attention  
The AV align model provided improved performance by fusing two modalities with attention in the encoder side instead of modality fusion in the decoder side [13]. However, when using audio as a query in the AV align model, attention weights may not be properly obtained in noisy environments. On the other hand, since video data is independent of acoustic noise, it may be important to make the role of video modality to a level of audio modality by fully exploiting input audio and visual information in learning attentions. Therefore, using video as a query may be helpful to achieve noise robustness. To consider a video query for audio context in addition to an audio query for video context of the AV align model [13] and to apply them to the transformer model, our DCM attention model has two multi-head attention layers between the two modality encoders, as shown in Figure 3. The configuration of each multi-head attention layer used in the DCM attention model is the same as that in the multi-head attention used for the encoder and decoder. Using \(\mathbf{h}^{a}\) and \(\mathbf{h}^{v}\), DCM attention outputs, \(\mathbf{AV}\) and \(\mathbf{VA}\), can be expressed as  
\[\mathbf{AV} =\text{Attention}\left(\mathbf{h}^{a},\mathbf{h}^{v},\mathbf{h}^ {v}\right),\] \[\mathbf{VA} =\text{Attention}\left(\mathbf{h}^{v},\mathbf{h}^{a},\mathbf{h}^ {a}\right). \tag{16}\]