esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.3.5 Ridge Regression  
Multiple linear regression can be used to analyze the effects of environmental variables on GPP, with the model expressed as  
\[RSM=\sum_{j=1}^{p}\beta_{j}x_{j}+c \tag{6}\]  
where \(j=1,\,2,\,3,\,\ldots\) and each \(x_{j}\) represents an independent variable, \(p\) is the number of independent variables, \(c\) is the constant term, and \(\beta_{j}\) are the regression coefficients estimated using the least squares method:  
\[\beta={(X^{T}X)}^{-1}X^{T}y \tag{7}\]  
where \(X\) represents the feature matrix of independent variables, and \(y\) is the dependent variable. However, in this study, interactions among variables may lead to multicollinearity, which can cause the matrix \(X^{T}X\) to approach singularity, thereby destabilizing regression results. To address this issue, we introduced the perturbation term \(\lambda I\) in ridge regression, which effectively mitigates multicollinearity, stabilizes regression coefficients, and reduces sensitivity to collinear predictors [13]. The ridge regression coefficient estimation formula is  
\[\eta_{ridge}={(X^{T}X+\lambda I)}^{-1}X^{T}y \tag{8}\]  
Ridge regression is a linear regression method used to address multicollinearity issues. It introduces an \(L2\) regularization term based on ordinary least squares (OLS) regression, resulting in more stable regression coefficients [49]. Ridge regression typically requires standardization of independent variables:\[X_{m}=\frac{x-\min(x)}{\max(x)-\min(x)} \tag{9}\]  
where \(x\) is the original variable value, \(\min(x)\) and \(\max(x)\) represent the variable's minimum and maximum values, respectively, and \(X_{m}\) is the standardized variable. The ridge regression model is formulated as  
\[Y_{m}=\sum_{t=1}^{n}\varphi_{t}x_{tm}+c \tag{10}\]  
where \(\varphi_{t}\) represents the ridge regression coefficients, \(t\) denotes the \(t\)-th factor, \(x_{tm}\) is the standardized influencing factor data, and \(Y_{m}\) quantifies the impact of factors on standardized GPP. The individual factor contribution is calculated as  
\[\gamma_{ct}=\varphi_{t}\times X_{trend} \tag{11}\]  
where \(X_{trend}\) represents the standardized trend of factors, and \(\gamma_{ct}\) denotes the contribution of an individual factor \(t\). The absolute contribution given by  
\[\gamma_{act}=\frac{|\gamma_{ct}|}{\gamma_{a,trend}}\times Y_{ trend} \tag{12}\] \[Y_{n\_trend}=|\gamma_{c1}|+|\gamma_{c2}|+|\gamma_{c3}|\]  
where \(\gamma_{act}\) represents the absolute contribution of factor \(t\), \(Y_{trend}\) denotes the observed GPP trend, \(Y_{n\_trend}\) is the sum of absolute contributions from all factors, and \(\gamma_{c1}\), \(\gamma_{c2}\), and \(\gamma_{c3}\) indicate the individual contributions of RSM, PRE, and RUM to GPP, respectively. The data processing and analytical framework of this study is shown in Figure 2.  
Figure 2: Conceptual framework of data processing and analytical methods.
# A Decentralized Receiver in Gaussian Interference
## 1 Introduction
### Results  
We perform an analysis of the achievable rates of the system in the presence of an interferer. The following contributions are provided:  
* Upper and lower bounds on the system's achievable communication rate in correlated Gaussian noise and regimes where these bounds are tight (Section 3). The strongest lower bound is given in Theorem 7.
* Performance characteristics of these rates in the presence of an interferer (Section 4.1).
* Behavior of the strategies in various scattering environments (Section 4.2). The scattering environment is seen to not affect average performance if LAN resource sharing adapts to the channel.
* A strengthening of an existing achievability proof for the system, where the same rate is achieved using less cooperation between nodes (Remark 7).
## 4 Results and Discussion
### Effect of Thickness on the Subsurface Drainage of a PFC  
The effect of thickness (\(T\)) of the PFC on its subsurface drainage was evaluated. Figure 7 describes the relationship between the time for surface ponding (\(t_{p}\)) and rainfall intensity values (\(l\)) in the consideration of thickness (\(T\)) for PFCs having an equivalent length (\(L_{R}\)) of 10 m.  
In Figure 7, it can be seen that the thickness (\(T\)) has a strong effect on the results of the time for surface ponding \(t_{p}\) of a PFC. As the thickness \(T\) of the PFC increased, the results of the time for surface ponding \(t_{p}\) increased sharply. It is apparent that as \(T\) increases by a factor of two, \(t_{p}\) only doubles. Moreover, as \(T\) increases by a factor of three, \(t_{p}\) rises more than six times. The evidence of this can be clearly seen in the case of the PFC at \(I\) = 30 mm/h, when \(T\) increased two times from 25 to 50 mm, \(t_{p}\) also increased two times (from 13 to 26 min). At the same condition, when \(T\) increased three times from 25 to 75 mm, \(t_{p}\) rose about 7.3 times (from 13 to 95 min). This behavior implied that the thickness \(T\) of the PFC has a strong effect on its subsurface drainage. The finding further supports the idea of [PERSON] et al. (2018), who found that the thickness of the PFC had the most remarkable effect on the permeability of the PFC.  
Figure 6: Relationship between the time for surface ponding for PFC (\(t_{p}\)) and rainfall intensity (\(l\)) with different slope (\(S_{R}\)) values.  
Figure 7: _Cont._This study found that the subsurface drainage of the PFC is more sensitive to its thickness T at lower rainfall intensity values. For instance, at \(I=20\) mm/h and \(S_{R}=4\%\), the results for the time for surface ponding \(t_{p}\) for PFC with \(T=25\) mm and \(50\) mm are \(22\) and \(45\) min, respectively. They vary significantly by \(23\) min. However, at \(I=120\) mm/h, the results are \(2.7\) and \(6\) min. The difference in the values of \(t_{p}\) is only \(3.3\) min, which are close to each other. The conclusion was drawn that the thickness \(T\) of the PFC does not seem to significantly affect the subsurface drainage of the PFC at high rainfall intensity. A similar conclusion was found in the study of [PERSON] and [PERSON] [16].
# A Graph Convolutional Network-Based Method for Congested Link Identification
## 2 Proposed Model  
### Criteria for Link Congestion  
In many applications, the perception of link congestion is more realistic and helpful than a concrete measure of network performance [31]. According to [12], each packet  
Figure 2: Congestion caused by node factors or channel factors.  
traversing a link can suffer performance degradation, such as loss or delay. Once the nature of each impairment is known, a summary statistic (e.g., loss rate or mean delay) can be calculated and mapped to a binary performance measure using a predefined threshold. If the calculated statistical values exceed this threshold, then the performance is classified as \"bad\"; otherwise, it is classified as \"good\". Paths consisting of multiple links will undergo the same classification process, with different thresholds being applied to each link.  
In this paper, we choose packet loss rate as the criterion for link congestion. Therefore, we measure the congestion status of all end-to-end paths in terms of packet losses. During an end-to-end packet loss rate measurement period, each route of the end-to-end path is probed \(\lambda\) times. We denote the number of packets received from the source node through the \(k\)th route as \(\lambda(p_{i}^{k})\). To obtain the status of each route, we first calculate its packet loss rate \(\gamma\) as follows:  
\[\gamma=1-\frac{\lambda(p_{i}^{k})}{\lambda} \tag{1}\]  
By comparing the packet loss rate \(\lambda\) with a given threshold \(\alpha\) (e.g., 1% [12]), an estimated congestion status is attached to route \(p_{i}^{k}\):  
\[y_{i}^{k}=\left\{\begin{array}{ll}1&\text{if }\gamma\geq\alpha,\\ 0&\text{if }\gamma<\alpha.\end{array}\right. \tag{2}\]  
If a route's measured packet loss rate falls below a predefined threshold \(\alpha\), then it will receive a Boolean value of \"0\", indicating its state is good; otherwise, it will receive a Boolean value of \"1\", indicating congestion.
## 2 Materials and Methods
### Reconfigurable Antenna  
The novel proposed frequency-reconfigurable microstrip antenna, illustrated in Figure 4a-d, features a compact size. The overall dimensions of the proposed antenna are 25 \(\times\) 18 \(\times\) 1.6 mm\({}^{3}\). The ring resonator dimensions are detailed in Figure 4a. The ground plane length (\(L_{\beta}\)) represented in Figure 4b is varied from 3 to 6 mm in steps of 1 mm. After parametric study, the optimum value of Lg is taken as 4 mm as depicted in Figure 4b. The dimensions of the physical parameters (\(W\)s: substrate width, Ls: substrate length, \(W\)f: feed line width, Lf: feed line length, gap: g, Lg: ground plane length) are also  
Figure 3: Add-drop ring resonator: a single input, single output waveguide with ring resonator structure.  
depicted in Figure 4a,b. Figure 4c,d illustrate the utilization of switches, specifically the PIN diode of BAR64-02V and SPDT switches of CG2415M6 models, respectively. The proposed antenna incorporates a decagon ring resonator, which is inserted using these switching components. The antenna structure is fabricated on an FR-4 substrate material with a relative permittivity of 4.3, tangent loss of 0.0025, and a substrate thickness of 1.6 mm. The CST(r) Studio Suite 3D EM simulator (CST-MWS) is employed to analyze the antenna parameters. The overall schematic diagram of the proposed antenna structure is depicted to provide a comprehensive representation of the design.  
Figure 4: (**a**) Top, (**b**) bottom, (**c**) PIN diode replacement, and (**d**) SPDT replacement of the proposed novel frequency reconfigurable antenna structure.
# Research on Mechanical Performance of Porous Asphalt Mixture with High-Viscosity Modified Asphalt
###### Abstract  
Porous asphalt mixtures play a pivotal role in enhancing pavement drainage capacity and traffic safety, where the performance of asphalt binder constitutes a determining factor. This study introduces an innovative advancement through the development of a high-viscoelastic modifier and corresponding modified asphalt based on SBS-modified asphalt, coupled with optimized preparation protocols. The optimal composition and dosage of the modifier were systematically determined through standardized tests including penetration, ductility, softening point, and bending beam rheometer (BBR) analysis. A comprehensive evaluation of road performance was conducted on two porous asphalt mixtures, namely conventional SBS-modified asphalt versus the novel high-viscoelastic modified asphalt (designated as 10-A). Experimental protocols encompassed high-temperature rutting resistance tests, low-temperature beam bending tests, freeze-thaw splitting tests, two-point bending fatigue tests, accelerated abrasion tests, and dynamic friction tester (DFT) measurements. The results demonstrate that the 10-A-modified mixture exhibits superior high- and low-temperature performance. Notably, its fatigue resistance and skid resistance showed minimal divergence from conventional SBS-modified asphalt, attributable to physicochemical crosslinking interactions among antioxidants, resins, and stabilizers. This research elucidates the synergistic mechanism of components within the 10-A modifier system. The proposed high-viscoelastic asphalt formulation meets the technical requirements for functional drainage asphalt mixtures while providing material-level support for implementing sponge city initiatives.  
high-viscoelastic modifier; porous asphalt mixture; mechanical performance; skid resistance
## 5 Result and Discussion
### Vorticity Contours
#### 5.1.1 Tip Vortex Variation  
A higher pressure inequality of the passage's sides (SPR-1) leads to more widespread tip vortex; also, pressure-side pressure is more effective on the extension of the tip vortex (Figure 8, Section A). Although the pressure level in passages 10-11 is higher than in passages 6-7, its tip vortex size is smaller than in passages 6-7, because of the lower pressure difference (Figure 8, Section A). The source of the tip vortex is the pressure gradient, so the SPR numbers that are closer to 1 lead to the smaller vortex width. For the same reason, although passages 9-10 with an SPR of 0.88 have a lower pressure level in the entrance, they resulted in the bigger tip vortex in comparison with passages 4-5 with SPR of 1 (Figure 9, Section A). In the same way, in passages 5-6 (Figure 8, Section A), with SPR = 1.02, a more extended tip vortex than passages 10-11 with an SPR of 1 can be seen. The pressure-side pressure level has more impact on the extension of this vortex than suction-side pressure, so in passages with higher pressure at P.s, a wider tip vortex is expected. So, in passages 5-6, a more widespread tip vortex can be seen than in passages 6-7.  
In the case of intensity, passages 10-11 have the most powerful vortex because of the higher pressure level on both sides (Figure 8, Section B). The tip vortex takes place near the pressure side, so the higher pressure of the P.s leads to the stronger tip vortex core (Figure 8, Section B). Therefore, in passages 5-6, the tip vortex has a higher intensity than in passages 6-7. The tip vortex center is located close to Section B. In equal admission, when the SPR changed 2% from unity, the vorticity width extended 16% more. Furthermore, an 8% drop in the pressure level caused that maximum of vorticity intensity at the tip vortex center to decrease by 10%.  
In the partial case (Figure 9, Section A), the P.s pressure of passages 3-4 is more than the P.s pressure of passages 4-5, so, its tip vorticity is more widespread than that in passages 4-5, but in comparison with passages 9-10, it has a smaller vortex because of the lower inequality. Passage 9-10 has a very strong tip vortex, which is because of being in the vicinity of the tongue region, where the mixing loss is dominant.  
The center of the tip vortex in passages 3-4 and 4-5 is located in Section A, but in passages 9-10, it is placed at Section B, which showed a decreasing pressure level that caused it to shift the center of the tip vortex to the previous sections.
## 4 Simulation Results
### iQBER Analysis  
#### 4.2.1 iQBER Analysis--LEO  
A critical factor influencing both the performance and security of space-based QKD systems is the intrinsic QBER (iQBER) of the space-based sources. The iQBER is essentially a measure of the error rate of the erroneous quantum bits transmitted by the source, and it directly impacts the fidelity and trustworthiness of the quantum communication channel. In the context of space-based QKD, where the communication link spans hundreds to thousands of kilometers through the vacuum of space and the Earth's atmosphere, even sources with seemingly low iQBER (1.5%) can significantly diminish the efficiency of key generation and the security of the transmitted keys. A higher iQBER necessitates more robust error correction and privacy amplification procedures to extract a secure key, which, in turn, reduces the effective key generation rate and increases the complexity and resource requirements of the QKD protocol. Therefore, minimizing the iQBER of a space-based QKD source is paramount for optimizing the system's performance.  
Figure 6 presents the calculated SKR for a single satellite pass assuming various source's iQBER values for (a) SNSPD detection with SMF coupling, (b) SPAD detection with SMF coupling, and (c) free-space SPAD detection. For these results, a receiver aperture diameter of 1 m was assumed along with a transmitted block size of \(10^{10}\) bits and a mere tip-tilt correction. It is evident that even a value of 1% for the intrinsic QBER can have an effect of up to 20% lower SKR values compared to the ideal case. Furthermore, it was observed that higher values of iQBER do not drastically affect the key rates but can limit the communication time window for cases where the overall end-to-end loss is high (e.g., SPAD detection with SMF coupling (Figure 6b). To enable the satellite segment to accommodate flexible OGS, which may be equipped with either SPADs or SNSPDs, an iQBER value of less than 2% is recommended under our assumptions. Performance analysis for a LEO case using state-of-the-art GHz-scale sources [29; 30] with an iQBER below 1% will be discussed in Section 5.  
Figure 5: End-to-end link loss and normalized SKR over time for different transmitted block size values of (**a**) \(D_{rx}\) = 1.5 m and SNSPD with SMF coupling, (**b**) \(D_{rx}\) = 2.3 m and SNSPD with SMF coupling, and (**c**) \(D_{rx}\) = 2.3 m and SNSPDs without SMF coupling.
## 2 Fundamentals of Shape-Memory Polymers (SMPs) and Shape-Memory Polymer Nanocomposites (SMPNCs)
### Classification and Types of SMPs  
The categorization of SMPs and their nanocomposites relies on the criteria used for their specific assessments. According to thermally-induced phase changes, SMPs can be broken down into four types based on their netpoints and switching domains: covalently cross-linked glassy thermoset networks, covalently cross-linked semi-crystalline networks, physically cross-linked glassy copolymers, and physically cross-linked semi-crystalline block copolymers [5].  
Depending on their response, SMPs can be divided into one-way SME (1W-SME), two-way SME (2W-SME), and multiple SME. 1W-SME is considered irreversible because the polymer chain fixation in its initial shape creates a specific structure without the ability to repeat SME. After recovering its initial state, it requires reprogramming to reactivate the effect [45]. In contrast, 2W-SME is different from conventional 1W-SME polymers. SMPs with reversible 2W-SME can spontaneously switch between two distinct shapes through external stimuli, eliminating the need for additional programming [25; 46]. 2W-SMPs include liquid crystalline elastomers-based 2W-SMPs and semicrystalline polymers-based 2W-SMPs. For liquid crystal elastomers, the temperature shift below or above the transition temperature (T\({}_{\text{trans}}\)) results in a transition between the anisotropic and isotropic phases, introducing the phenomena of crystallization-induced elongation (CIE) and melting-induced contraction (MIC) [47]. Above T\({}_{\text{trans}}\), the MIC effect dominates the molecular structure of the polymer matrix, while below T\({}_{\text{trans}}\), the CIE effect prevails, resulting in reversible contraction/extension behavior. Another type of SMP with 2W-SME can be obtained by introducing semicrystalline monomers into the polymer network. This type of SMP is considered a suitable candidate for soft actuators and sensors, artificial muscles, and soft robotics.  
SMPs with multiple SMEs have multiple temporary shapes, allowing for multiple shape changes at different temperature intervals that can be fixed at a time, with therecovery process carried out step by step [48]. The principle of this effect is to design an SMP with a wide range of thermal T\({}_{\text{trans}}\) and multiple independent phases with different T\({}_{\text{trans}}\). Implementing this strategy is more complex because it requires comprehensive programming of the molecular structures of the polymer, with the number of temporary shapes determining the number of required programming steps [49].  
Based on the external stimulus needed to trigger the shape-memory actuations, SMPs and SMPNCs are classified into various categories, including magnetic field [43, 50, 51], pH [14, 15], water/solvent [52, 53, 54], electric [2, 55], and light-responsive [8, 56, 57] SMPs. In this review, SMPNCs have been categorized and discussed based on the types of nanofillers incorporated into the polymer matrix.
## 6 Simulation Setup and Results
### Key Performance Indicators  
In addition to SINR graphs, other KPIs measured through an extensive simulation study and used to evaluate the results are:  
* Throughput is the aggregated application layer throughput achieved in each scenario run across all active links.  
* PER (%) is the average application layer PER observed in each scenario run across all active links.
* Uptime (%) is the average percentage of simulation time that links were operational with 99.9% PSR and above across all active links.
* Spectrum utilization represents the percentage of allocated spectrum used for actual transmissions. Spectrum utilization of 100% means that all transceivers use their full allocated capacity (all allocated slots are used for transmission of 3 PPDUs).
* Latency is the average application layer latency observed in each scenario run across all active links.
* Wireless and wired protocol overhead of the DSS techniques in terms of information sharing via a control channel between GSO, NGSO, ESIM, and the terrestrial network.
* CP overhead is the overhead per second induced from running the CP protocol across all wired and wireless links.
* The slot selection protocol overhead is the overhead per second of the slot allocation protocol across all wireless links.
## 3 Results and Discussion
### Phase 5: Integrate into a Smart Building  
Smart building infrastructures aim to maximise building efficiency in different fields of application. Specifically, this research focused on the assessment of IAQ to maintain a safe atmosphere for building occupants. However, given that the main issue at present day is the transmission of the COVID-19 disease, the parameter related to CO\({}_{2}\) concentration was used as a reference. For this purpose, a low-cost device was designed to transfer the captured data to the cloud. In this way, it is possible to retrieve the information for decision making or integration with other systems, such as those in charge of ventilation and air conditioning (HVAC) or even other systems for automatic opening and closing of elements for natural air renewal.  
The other issue that has been addressed is the definition of a location for this equipment that allows it to be properly measured. In this regard, CFD studies have shown that, although the general recommendation of up to 1.2 m above the floor is correct, there are many more points where reliable data on CO\({}_{2}\) concentration averages can be obtained. In  
Figure 18: [PERSON] and [PERSON] analysis of different scenarios.  
addition, vortices were determined and excluded from the potential location of the device, as its measurements would be distorted from the actual average. A functional scheme of the investigation is presented graphically in Figure 19. Integration with other systems in smart building was not addressed within the scope of this research, although this would allow better monitoring and control of these systems, promoting efficient energy use and helping to control indoor quality for disease prevention.