esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Antenna Testing and Result Analysis
### Mode 2 (\(D_{1}\) Is Off While \(D_{2}\) Is On)  
In mode 2, corresponding to D\({}_{1}\) being in the off state while D\({}_{2}\) is in the on state, the antenna system offers a dual-band operational mode. Besides the wideband generated in mode 1, the antenna offers a low band across 1.8 GHz. The results offer corresponding simulated and measured values of 1.78-1.84 GHz and 1.77-1.87 GHz across the lower resonance. Meanwhile, at a higher resonance, the bandwidth of 3.91-7.23 GHz is observed for the simulated case, while the measured value ranges from 4.17 GHz to 7.13 GHz, as depicted in Figure 8a. The operational behavior can be understood by using the surface current graphs, where at a lower resonance of 1.8 GHz the maximum current is observed across the inner stub and upper part of the main radiator; moreover, a handsome amount of current is also induced in the outer stub as well, as depicted in Figure 8b As is depicted for mode 1, the higher bands are generated due to the main radiator and this is also validated by analyzing the surface current graph at 5 GHz.  
Figure 7: Mode 1 performance parameter comparison of (**a**) return loss, (**b**) surface current, and (**c**) peak gain.  
Lastly, the radiation pattern of the antenna working in mode 2 at the selected frequencies of 1.8 GHz and 4.5 GHz is presented in Figure 8c. In both simulated and measured cases, the antenna offers well-matched results having an omni-directional radiation pattern for the Phi = 90\({}^{\circ}\) plane, while a monopole-like bi-directional radiation pattern is observed for the Phi = 0\({}^{\circ}\) plane, as depicted in Figure 8c. The antenna offers a peak gain of 1.2 dBi at 1.8 GHz, while a peak gain of 2.4 dBi is observed for 4.5 GHz. Mode 2 targets the band spectrum allocated for GSM, Bluetooth, LTE Band-3, 5G sub-6-GHz, Wi-Fi-6E and C-band applications.
## 4 HCR-1 Protocol Description
### Routing Discovery in \(\mathcal{R}(1)\)  
After the cluster head selection, each CH needs to find its next-hop node in the routing algorithm. The CHs in \(\mathcal{R}(1)\) are divided into two sets according to their residual energy: CHs with NEXT = 1 and CHs with NEXT = 0. As stated in Section 4.1, the CHs with NEXT = 1 are determined to communicate with the sink directly, since they are sufficient in residual energy. On the other hand, the CHs with NEXT = 0 should select a closer relay to reduce the energy consumption. In HCR-1, a routing discovery algorithm is proposed for the CHs with NEXT = 0 to select a reasonable relay from the RELAY list.  
According to the model given in Section 3.1, the energy consumption is tightly relevant to the number of data packets and the transmission distance. To achieve energy balance, the relay selection should consider the energy consumption of both the sender CH and the receiver CH. Therefore, given a CH \(j\) and its relay candidates in the RELAY list, the CH \(j\) uses the routing cost function to select its relay.  
\[C_{route}(i,j)=\alpha\times d^{2}(i,j)+(1-\alpha)\times d^{2}(i,0),i\in RELAY \tag{7}\]  
where the CH \(i\) is a CH in the RELAY list and \(d(i,j)\) is the distance between CH \(i\) and CH \(j\). \(d(i,0)\) is the distance between CH \(i\) and the sink. The \(\alpha\) is the routing factor for the tradeoff of energy consumption between CH \(i\) and CH \(j\). The CH \(i\) with minimum \(C_{route}(i,j)\) will be selected as the relay of CH \(j\). The value of the routing factor \(\alpha\) has great impacts on the network lifetime, which will be discussed in Section 5.1.  
It is worth noting a special condition that the CH with NEXT = 0 has an empty RELAY list. In this case, the CH has to transmit its data to the sink directly.
## 1 Introduction
On August 15 th, 2016, a Spanish experimental GNSS-R \({}^{3}\)cat-2 satellite with a PYCARO (P (Y) and C/A reflectometer) payload that can receive reflected signals from different GNSS satellites was launched using the CZ-2D (2) rocket at China's Jiuquan Space Center [25]. This mission proves that the four-system GNSS-R system will be feasible in future. Additionally, other GNSS-R missions such as the PARIS-IoD (Passive Reflectometry and Interferometry System In-orbit Demonstration) and GEROS missions are also in progress [33; 34]. Increasing numbers of GNSS satellites will be operational in the next few years. Galileo will reach full operational capability (FOC) in 2020 [35]. China is also in the process of expanding its regional BDS to form the global BeiDou-3 GNSS by 2020 [36]. Approximately 120 GNSS satellites in orbit, including GPS, GLONASS, Galileo and BDS satellites, will be available for use as illuminators in a few years. The upcoming GNSS-R constellations should therefore receive more and better reflected signals simultaneously from all four main GNSS constellations. If this concept can be realized, then the coverage and revisit times of GNSS-R constellations will be upgraded to higher levels. To meet the specific requirements of a variety of missions, including monitoring of the sea surface height and surface winds, and detection of sea surface ice and other large targets, it is important to quantize the observation capacity exactly during the mission design phase. Analysis and evaluation processes are thus necessary to determine a series of accurate and effective simulation methods. In this paper, we first propose a strategy for construction of a multi-GNSS-R system, including orbit simulation of the LEO micro-satellite constellation and the formation of a fully global multi-GNSS constellation. Second, the face directions and receiving angles of GNSS-R antennas are discussed and a method for selection of contacted specular reflections based on the antenna characteristics is proposed. Third, the distributions and the revisit time of the reflection points in each \(1^{\circ}\times 1^{\circ}\) bin are analyzed. Finally, the results are discussed and conclusions are drawn.
# Reduction of Coupling Interface Degrees of Freedom in Mixed-Interface Component Mode Synthesis
## 3 Normal Mode Correction for the Master Substructure
### Mode Synthesis in the Revised MCMS Method [40]
\begin{array}{l}\mathbf{q}_{m}\\ \mathbf{u}_{c}^{B}\end{array} \right\}= \begin{bmatrix}\mathbf{0}&\mathbf{I}_{m}\\ \mathbf{P}_{1}&\mathbf{P}_{2}\end{bmatrix} \left\{ \begin{array}{l}\mathbf{q}_{k}\\ \mathbf{q}_{m}\end{array} \right\}= \begin{bmatrix}\mathbf{T}_{c}^{2}\end{bmatrix}  \left\{ \begin{array}{l}\mathbf{q}_{k}\\ \mathbf{q}_{m}\end{array} \right\} \tag{34}\]  
After the second coordinate transformation, the generalised stiffness and mass matrices of each substructure are written as follows:  
\[ \begin{bmatrix}\mathbf{K}_{f}^{(2)}\end{bmatrix} = \begin{bmatrix}\mathbf{T}_{f}^{2} \end{bmatrix} ^{\mathsf{T}} \begin{bmatrix}\mathbf{K}_{f}^{(1)}\end{bmatrix}\begin{bmatrix} \mathbf{T}_{f}^{2}\end{bmatrix} \quad\quad,\quad \begin{bmatrix}\mathbf{M}_{f}^{(2)} \end{bmatrix} = \begin{bmatrix}\mathbf{T}_{f}^{2}\end{bmatrix} ^{\mathsf{T}} \begin{bmatrix}\mathbf{M}_{f}^{(1)}\end{bmatrix}\begin{bmatrix}\mathbf{T}_{f}^{2} \end{bmatrix}  \tag{35}\]  
and  
\[ \begin{bmatrix}\mathbf{K}_{c}^{(2)}\end{bmatrix} = \begin{bmatrix}\mathbf{T}_{c}^{2} \end{bmatrix} ^{\mathsf{T}} \begin{bmatrix}\mathbf{K}_{c}^{(1)}\end{bmatrix} \begin{bmatrix}\mathbf{T}_{c}^{2}\end{bmatrix} \quad\quad,\quad \begin{bmatrix} \mathbf{M}_{c}^{(2)}\end{bmatrix} = \begin{bmatrix}\mathbf{T}_{c}^{2}\end{bmatrix} \begin{bmatrix}\mathbf{M}_{c}^{(1)}\end{bmatrix}\begin{bmatrix}\mathbf{T}_{c}^{2} \end{bmatrix}  \tag{36}\]  
The generalized stiffness and mass matrices of the global system can be assembled directly from Equations (37) and (38), and then eigenvalue problems can be solved by Equation (39). \[ \begin{bmatrix}\mathbf{K}\end{bmatrix} = \begin{bmatrix}\mathbf{K}_{f}^{(2)} \end{bmatrix} + \begin{bmatrix}\mathbf{K}_{c}^{(2)}\end{bmatrix}  \tag{37}\] \[ \begin{bmatrix}\mathbf{M}\end{bmatrix} = \begin{bmatrix}\mathbf{M}_{f}^{(2)} \end{bmatrix} + \begin{bmatrix}\mathbf{M}_{c}^{(2)}\end{bmatrix} \] (38) \[ \begin{bmatrix}\mathbf{M}\end{bmatrix} = \begin{bmatrix}\mathbf{M}_{f}^{(2)} \end{bmatrix} + \begin{bmatrix}\mathbf{M}_{c}^{(2)}\end{bmatrix} \] (39) \[ \begin{bmatrix}\mathbf{M}\end{bmatrix} = \begin{bmatrix}\mathbf{M}_{f}^{(2)} \end{bmatrix} + \begin{bmatrix}\mathbf{M}_{c}^{(2)}\end{bmatrix} \]  
From above discussions we can see that, the coordinate transformations for mode synthesis can be done in each substructure separately, and then it is easy to assemble the component generalized stiffness and mass matrices into the global ones according to the truncated normal modes.
## 3 Results and Discussion  
### Why Wastewater Characterization  
One of the primary industries in Europe that produces industrial wastewater is the dairy industry [47]. The properties of dairy wastewater may vary considerably based on factors such as the final products, the type of system, and the processes used in the production plant [7]. The results of the physicochemical characterization of the raw and autoclaved whey effluents used in this study are shown in Table 1.  
They is a dairy byproduct obtained after the coagulation of raw milk during the cheese manufacturing process. It consists of 94% water, 4 to 5% lactose, 0.5 to 1% proteins, and small amounts of mineralsand vitamins [48]. As shown in Table 1, the raw and autoclaved whey wastewaters had an acidic pH (<5), high turbidity, and significant total solids (much greater than reported in the literature), limiting their direct use for microalgal culture. The lactose concentration in raw whey wastewater was 66.75 g L\({}^{-1}\), whereas autoclaved whey wastewater had a lactose level of 61.57 g L\({}^{-1}\), consistent with the scientific literature (ranging from 10 g L\({}^{-1}\) to 92 g L\({}^{-1}\)[4; 8; 9]). Some species of microalgae can use lactose as a suitable carbon source to support growth [49]. High levels of chemical oxygen demand (COD) and total carbon (TC) were observed in the raw and autoclaved whey effluents, with values of 100.36 g L\({}^{-1}\) and 96.00 g L\({}^{-1}\) for COD and 39.85 g L\({}^{-1}\) and 39.07 g L\({}^{-1}\) for TC, respectively. These values were similar to those reported in the literature [6; 8; 9]. The concentrations of phosphate, total nitrogen (TN), nitrate, and ammonia were 795.3 mg L\({}^{-1}\), 2.81 g L\({}^{-1}\), 0.51 g L\({}^{-1}\), and 74.53 mg L\({}^{-1}\), respectively, in raw whey wastewater, and 756.31 mg L\({}^{-1}\), 2.54 g L\({}^{-1}\), 0.32 g L\({}^{-1}\), and 109.04 mg L\({}^{-1}\), respectively, in autoclaved whey effluent. The phosphate and TN contents were higher than those reported in the literature.  
It is noteworthy that the composition of whey wastewater was slightly modified after autoclaving. This thermal pretreatment induced the denaturation of milk proteins, including \(\beta\)-lactoglobulin and \(\alpha\)-lactalbumin, resulting in structural changes such as thiol-disulfide bond rearrangement and protein aggregation. These effects may explain the observed increases in turbidity and ammonia levels after autoclaving, as previously reported by [PERSON] et al. in their study on the thermal denaturation of whey proteins [50]. Despite these modifications, the nutrient profile observed in this study remained consistent with typical whey wastewater characteristics. Moreover, the selected pretreatment method was appropriate for investigating microalgal cultivation, as it effectively eliminated bacterial contaminants that could otherwise interfere with algal growth, metabolism, and experimental reproducibility.
# Minimum Audible Angle in 3 rd-Order Ambisonics in Horizontal Plane for Different Ambsonic Decoders
## 5 Results
There is a visible peak in the cone of confusion area at \(90^{\circ}\) and then another smaller one at \(135^{\circ}\). The second peak might be caused by the fact that the shift from the loudspeaker position always appears as a phantom source in between two loudspeakers, causing small fluctuations in energy vector angular errors. The same phenomenon, although on a smaller scale, is observed in the MAA values for \(45^{\circ}\). Figure 11: Comparison of the mean results of the MAA threshold for all of the investigated decoders. Loudspeaker positions are \(0^{\circ},45^{\circ},90^{\circ},135^{\circ},180^{\circ}\). Figure 12: MAA values for a subject highly experienced with audio engineering, with high familiarity with the playback system. The results for subject S05, who is highly experienced in audio engineering, with medium familiarity with the playback system used in the experiment, are shown in Figure 7:0 For the rear plane in the case of the AllRAD algorithm, the reoccurring systematic errors in the answers made detecting the MAA threshold impossible. As the subject had extensive experience with stereo systems, the low resolution above 30\({}^{\circ}\) may suggest difficulties with perception above the stereophonic range due to the contrast in the high familiarity with the stereo range and unfamiliarity beyond it. Figure 14 shows the results for subject S06, who has the lowest familiarity with the ambisonic system. In this case, only the data from two listening tests was available. For the range of the stereophonic scene, which is up to 30\({}^{\circ}\), there is no significant difference in perception between the two decoders. However, above that reference value, the MAA remains lower for the EPAD.
# SIC-Free Based Indoor Two-User NOMA-VLCP System
## 4 Conclusions  
In this letter, we propose a dual-users rate VLCP system architecture with no SIC process and can effectively avoid EP compared with the traditional baseband NOMA. Based on the combination of user rates of this system, the PSO algorithm is used to optimize the power allocation factor and system parameters to reduce inter-user interference. Simulation results show that in the indoor environment of \(5\;\mathrm{m}\times 5\;\mathrm{m}\times 2.5\;\mathrm{m}\), the communication BER of each user is lower than the FEC error correction limit, and still meets the communication requirements under the condition of low channel SNR. The proposed VLCP system can realize centimeter-level positioning for signals of different rates, with an average positioning error of 4.31 cm, which reduces the average positioning error by 63.74% when compared with the baseband NOMA-VLP system with SIC. Expanding the system environment to \(5\;\mathrm{m}\times 5\;\mathrm{m}\times 3\;\mathrm{m}\), the average positioning accuracy of the system is 5.15 cm and meets the communication needs of different users. To this end, the proposed VLCP system can provide information transmission and high-precision positioning services for smart terminals in indoor environments.  
Conceptualization and methodology, J.J.; software and validation, Q.S.; investigation and resources, J.W.; data curation and visualization, H.L.; writing--original draft preparation, D.C.; writing--review and editing, D.Y. All authors have read and agreed to the published version of the manuscript.  
This work was financed by the Guangdong Basic and Applied Basic Research Foundation, China (2022A1515110154, 2022B1515120086, 2022A1515110770), Fundamental Research Funds for the Central Universities, China (FRF-TP-22-049A1, FRF-TP-22-044A1), National Natural Science Foundation of China (62374015).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no competing interests. The author [PERSON] was employed by the company World Telecom Satellite Technology Limited. There are no conflict of interest between any of the authors and the company.
# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 3 Proposed Method
### Attention Block  
The high-frequency region is a crucial part of an image, as it significantly influences the visual quality, including contour lines and texture information. Therefore, restoring high-frequency components during the super-resolution process is vital. Therefore, we introduced an attention mechanism that uses CSBlock to emphasize high-frequency features. As shown in Figure 8, connecting the CSBlocks in a series, we strengthened the emphasis on high-frequency features. Skip connection structures in series connections help mitigate gradient vanishing, which can occur in deeper networks. With this architecture, we can effectively emphasize high-frequency features and train without encountering gradient-vanishing.  
\[AB(x)=H(CSBlock_{10}(CSBlock_{9}(\cdots CSBlock_{1}(x)\cdots))+x). \tag{5}\]  
Equation (5) presents the formula for the attention block, which emphasizes super-resolution. \(AB(*)\) denotes an attention block, while \(H(*)\) denotes a convolutional operation. \(CSBlock_{n}(*)\) indicates the nth CSBlock, and \(x\) is the input feature map.  
In this study, we used ten CSBlocks constructed with skip connection structures and convolutional operations to prevent gradient vanishing and aid network training.
## 3 The Proposed SensorAnt Protocol
### The Sensor Node Model  
In this paper, we modeled the behavior of the sensor nodes in three modes: initial, idle and final as explained in Figure 2. In the initial mode, the algorithm starts by initializing the parameters and then comes to the idle mode where the response to the events occurs. The occurred events are of three types: the receipt of data, control and link error messages.  
Figure 2: The sensor node model.  
If the event is a received data message, the scheme starts to route the packets to the sink node. If the current sensor is the sender of the data message, it checks the existence of the path if no information it sends the request on-demand (Ant-Forward) message.  
If the received event is a control message either on-demand, proactive or fixed, the protocol verifies this kind of message in order to take suitable action. If it is the request (Ant-Forward) or reply (Ant-Backward) messages, the sensor handles it with the appropriate forward or backward method, from the sender towards the sink and _vice versa_, in order to adjust its routing table accordingly. In case the control message is a hello or dissemination pheromone packet, the sensor needs to get notices that still have a connection with the current sender neighbor; consequently, it derives the information inside the message to modify its routing table.  
If the received event is a link error message, the sensor starts to change its routing table to reveal the modified status. Later, if the sink becomes inaccessible, due to link error, the sensor is required to transmit a suitable response; if the sender node lost the path to sink, it will automatically transmits the request on-demand message. On the contrary, if the sensor is an intermediate node, it checks whether the error is due to failure to transmit a data message, if this is the case, it begins to transmit a request fixed message. Otherwise, if the error due to failing to transmit a control message, it sends an error message correction.
# Influence of the Quality of Consumer Headphones in the Perception of Spatial Audio
## 4 Test 2. Frequency Response about Quality and Spatial Impressions
### Results  
Figure 7a shows the average of the normalized (zero to 100) quality answers for the hidden reference, all five headphones simulated and the two anchors. As shown, the reference has been properly identified in most cases. The three supposedly good quality headphones have high scores; meanwhile, the two supposedly poor quality ones have the lowest scores. Both anchors remain in the middle of the scores of these two groups.  
An analysis of variance confirms that the headphones have a very significant influence (\(F\) = 58.33, df = 7, \(p\) < 0.001) over the quality perceived.  
Figure 7b shows the average of the normalized (zero to 100) spatial impression answers for the hidden reference, the five headphones simulated and the two anchors. The results seem to be similar to the answers about quality, with a high correlation of \(r^{2}\) = 0.648. Nevertheless, in this case, the confidence intervals are a bit wider, and the scores have some differences. The three supposedly good quality headphones have high scores again, but the confidence intervals do not separate them very much. There is a bigger difference between the two supposedly poor quality headphones, and the low cost multimedia ((e)-LCmul) ones are in the same range as both anchor signals. It is also noticeable that the Anchor Signal 2 (ANC2) as a monaural signal does not have a lower score.  
In any case, an ANOVA confirms that the headphones have a very significant influence (\(F\) = 58.33, df = 7, \(p\) < 0.001) over the perceived spatial impression. No significant influence of the type of sound has been detected, even though some of them were binaural recordings.  
Figure 7: (**a**) Average answered quality _versus_ reference, headphones and anchors (minutes); (**b**) average answered spatial impression _versus_ reference, headphones and anchors.
## 2 Results and Discussion
### Interactions and Dispersion Stability of Anion-\(\pi\) Type PNPs with CuPc and CB
Importantly, the stronger \(\pi\)-\(\pi\) interactions between PNPs-5 and CuPc promoted the electron transfer from PNPs to CuPc, which further lowered the binding energies of the two spin-orbit peaks. All peak parameters are listed in Tables S3 and S4 in the Supporting Information. The above results showed that the order of interactions' strength between these PNPs and CuPc was as follows: CuPc + PNPs-5 \(>\) CuPc + PNPs-4 \(>\) CuPc + PNPs-1 \(\geq\) CuPc + PNPs-3 \(>\) CuPc + PNPs-2. Subsequently, the stability of the mixtures of these PNPs with CuPc in water was investigated by measuring \(\zeta\) values. The results showed that the absolute \(\zeta\) values of all samples increased, indicating improved stability. Additionally, the order of absolute \(\zeta\) values corresponded to the strength of interactions between these PNPs and CuPc (Figure 3i). This finding suggested that stronger interactions enhanced the dispersion stability of CuPc in water. Furthermore, these PNPs were mixed with the pigment CB in the same mass ratio. The absolute \(\zeta\) values were ranked from high to low as follows:  
Figure 3: Interactions and dispersion stability of anion-\(\pi\) PNPs with CuPc, using CuPc without PNPs as the control group. (**a**) UV-Vis spectra in water. (**b**) UV-Vis spectra in the range of 705–730 nm. (**c**) Predicted values of p\(K_{a}\) in water for the corresponding acids of these PNPs. (**d**) UV-Vis spectra of CuPc in ethanol mixed with different contents of the oil phase monomer St. High-resolution XPS spectra of N 1s in the ranges of (**e**) 400–397 eV and (**f**) 407–399 eV. (**g**) High-resolution XPS spectra of Cu 2p. (**h**) Changes in binding energies in high-resolution XPS spectra of Cu 2p. (**i**) \(\zeta\) values in water. CB + PNPs-5 > CB + PNPs-4 > CB + PNPs-1 > CB + PNPs-3 > CB + PNPs-2 (Figure S13). These conclusions were similar to those obtained for CuPc, suggesting that PNPs with good dispersion stability for CuPc also enhanced the dispersion stability of CB. This finding suggested that these PNPs possessed extensive applicability in improving the dispersion stability of pigments with an intermolecular force type of \(\pi\)-\(\pi\) stackings. In conclusion, for both CuPc and CB, these PNPs effectively enhanced the dispersion stability of the pigments in water by forming electrostatic and \(\pi\)-\(\pi\) interactions.