esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### In Vitro Disintegration Study  
The disintegration times for the orodispersible film, with different concentrations of gum and ratios of sorbitol in the formulation, are presented in Table 2. An increase in the gum concentration extended the disintegration time. Also, adding the film plasticizer (sorbitol) accelerated the film disintegration period. The fastest disintegration lasted 8.14 min, which came from the substrate formulation of the gum concentration of 3.5% and 3% sorbitol from the gum. This might be due to the polyhydroxy groups in the sorbitol molecule increasing the solubility of the film. Sorbitol molecules become inserted between the gum polymer structure, contributing to low-polymer networking and increasing the hydrophilic property of film [26]. The disintegration times of all substrate formulations were longer than 3 min, as indicated in the specifications for oral disintegration tablets of the European pharmacopoeia [36]. This is because the disintegration apparatus is designed for tablet formulations. The sieve size of the disintegration apparatus is narrow, aiming to screen drug particles. Therefore, during the orodispersible film disintegration, the film became a gel, attaching to the screen of the basket of the disintegration apparatus. This made the disintegration time longer. However, the in vitro dissolution test result indicated that 77% of the drug was released within 1 min, and 97% of the drug dissolved in 5 min (Figure 6). This result suggests that the disintegration apparatus for orodispersible films must be redesigned, as previously mentioned in the work of [PERSON] et al. [37]. However, the disintegration time result in this manuscript can be used to compare the solubility of different formulation orodispersible films.
## 4 Security Attacks
### Contract Layer
#### 4.4.1 Re-Entrancy Attack  
A re-entrancy attack [18; 30; 42; 47] occurs when a smart contract's function is called recursively before the initial invocation completes its execution. In this scenario, an attacker repeatedly calls a vulnerable function, manipulating the contract's state and draining funds before the original transaction has been finalized. The primary impact of a re-entrancy attack is the unauthorized withdrawal of funds or assets stored within the contract, along with unintended modifications to critical contract parameters.  
Figure 6 visually illustrates the sequence of actions in a re-entrancy attack, using a step-by-step flow. It begins with a malicious attacker (top box) who initiates the process by calling a vulnerable smart contract. This contract contains a flaw: it does not update its internal state (such as the user's balance) before sending out funds. Once the initial function call is triggered (center of the diagram), the contract sends funds to the attacker's external address or contract. However, instead of completing the transaction, the attacker's contract calls back into the vulnerable contract before it finishes execution. This triggers a re-entrant call, shown on the left branch, which reactivates the same withdrawal logic repeatedly. Since the contract has not yet marked the initial transaction as complete, it continues to send funds, resulting in funds being drained, as shown on the right side of the flow. The image clearly shows how the recursive loop is formed and how control flow returns to the vulnerable contract multiple times, emphasizing the core vulnerability. This attack exploits the contract's failure to follow safe design patterns, such as updating state before making external calls.
# Evaluation of Low-Temperature Performance of Recycled
## 3 Results and Discussion
### Creep Rate  
Asphalt mixtures exhibit typical viscoelastic behavior within their operational temperature range, with mechanical properties being closely related to loading time and ambient temperature. The results from the BBCT are illustrated in Figure 13, indicating that lower temperatures result in a decreased creep rate and stress relaxation of the asphalt mixture, thereby compromising its resistance to deformation. Analyzing the influence of RAP content on the LTP of RAMs, it was observed that an increase in RAP content leads to a gradual reduction in the creep rate of RAMs. This suggests that the addition of RAP diminishes the LTP of RAMs, aligning with findings from TSRST and the trabecular bending test. Furthermore, analysis of the influence of different thermal histories of RAP on the LTP of RAMs shows that the stress relaxation capacity of a mixture displays varying trends at different temperatures. When the ambient temperature exceeds the transition temperature, the UVRAP results in a more rapid decay in the stress relaxation capacity of RAMs as the temperature decreases. Conversely, when the ambient temperature falls below the transition temperature, the decay of the stress relaxation capacity of RAMs slows down. However, this trend is reversed for RAMs with THRAP. By the time the temperature drops to \(-20\)\({}^{\circ}\)C, the RAMs become more susceptible to cracking than the original asphalt mixture in a low-temperature environment. This observation aligns with the fracture strength results obtained from the TSRST. RAPs with varying thermal histories alter the low-temperature viscoelastic behavior of RAMs. Furthermore, the LTP of an RAM is correlated with the ambient temperature.
# Calculation Method of New Assembled Corrugated Steel Initial Support Structure of Highway Tunnel
## 4 Practical Engineering Application
### Numerical Simulation
#### 4.2.1 Model Parameter  
1. Surrounding rock: The tunnel is buried at a depth of 50 m, with a height from the lower boundary to the tunnel floor bottom being five times the tunnel height, and the span from the left and right boundaries to the tunnel wall is five times the tunnel span. The surrounding rock is classified as grade IV. Geological survey data provide mechanical parameters for the surrounding rock in each stratum, as detailed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Stratum**} & \multicolumn{2}{c}{**Surrounding Rock**} & \multirow{2}{*}{**Thickness/m**} & \multirow{2}{*}{**Unit Weight**} & **Modulus of** & **Poisson’s** & **Internal** & \multirow{2}{*}{**Cohesion**} \\  & **Classification** & & & **Elasticity** & **Ratio** & **Friction Angle** & \\ \hline
1 & IV & 33 & 20 kN/m\({}^{3}\) & 1.8 GPa & 0.32 & 27\({}^{\circ}\) & 0.20 MPa \\
2 & IV & 33 & 21 kN/m\({}^{3}\) & 2.3 GPa & 0.32 & 28\({}^{\circ}\) & 0.30 MPa \\
3 & IV & 39 & 22 kN/m\({}^{3}\) & 2.8 GPa & 0.32 & 30\({}^{\circ}\) & 0.40 MPa \\
4 & IV & 22 & 23 kN/m\({}^{3}\) & 3.3 GPa & 0.32 & 32\({}^{\circ}\) & 0.50 MPa \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mechanical parameter table of surrounding rock.  
Figure 13: Design of lining of Qipanshan Tunnel: (**a**) design of lining of Qipanshan Tunnel; (**b**) support structure FE model; (**c**) overview of the FE model.  
2. CSP: The model is 380 mm \(\times\) 140 mm \(\times\) 5 mm. According to Equation (11), it is calculated that \(h_{eq}\) = 157 mm and \(b_{eq}\) = 41 mm. In addition, the parameters of the rotational connector are entered according to Table 4.  
3. Backside shotcrete of CSP: the concrete grade is C20, the modulus of elasticity is E = 2.55 \(\times\) 10\({}^{4}\) MPa, the critical yield stress is \(\sigma_{y}\) = 9.6 MPa, and the thickness is 5 cm. (4) Secondary lining concrete: the concrete grade is C30, the modulus of elasticity is E = 3.0 \(\times\) 10\({}^{4}\) MPa, and the thickness is 35 cm. (5) Anchor bolt: The angle of depression of the anchor bolt is 15\({}^{\circ}\), which is located at the arch foot and the arch waist; the actual diameter of the anchor bolt is 42 mm, and the spacing between the anchor bolts is 0.38 m \(\times\) 4 = 1.52 m. After conversion, the diameter of the anchor bolt per linear meter is 38 mm.
# Mapping Processing Elements of Custom Virtual CGRAs onto Reconfigurable Partitions
## 2 Related Work
### Mapping Tools and Techniques
When an application is mapped onto such an overlay, appropriate parameters' values are determined and applied to the placed and routed generic configuration in a so-called specialization stage. This way, setting registers are kept entirely in the configuration memory of the FPGA instead of using additional logic resources. A similar approach is proposed for changing settings of individual routing switches; however, this is possible only for hypothetical FPGAs, as vendors do not provide access to low level routing reconfiguration infrastructure. It is worth noting that parametrized configuration generation and specialization require an entire dedicated toolchain developed for this purpose. Despite being identical to the overlay perspective, individual PEs of virtual architectures are usually implemented independently for better global optimization. According to [PERSON] and [PERSON] [5], partitioning can be used as a simple solution to improve compute density and speed of mesh based overlays, as it prevents unnecessary deduplication of components in the critical paths. Moreover, the implementation result of a single instance can be reused at other locations. Rapid Overlay Builder (ROB) [12] proposes a combination of techniques for fast implementations of virtual architectures in Xilinx FPGA, without compromising the efficiency of the solution. Instead of performing independent placement and routing for each PE, ROB uses hard macro relocation of implementation results, with possible multiple variants for different sets of resources. The flow comprises resource budgeting for PEs, floorplanning their distribution in FPGA fabric, placing and routing initial PE variants, relocating implementation results, and interconnecting the design. However, the two initial phases require manual engagement from the users. The proposed techniques can be used to obtain significant speedup of placement and routing process, but as they are based on now obsolete ISE and GoAhead tools, the flow is incompatible with newer Xilinx FPGAs. Recently introduced IMPRESS [14; 15] is an academic design flow for Xilinx FPGAs capable of mapping virtual architectures onto networks of reconfigurable partitions. The tool is based on set of TCL scripts working under Xilinx Vivado. It exploits module relocation to reduce implementation time and bitstream memory footprint. Although proper floorplanning of the reconfigurable partition is claimed to be crucial for efficient mapping of virtual architectures, no automation for the process is provided.
## 2 Problem Definition
### Problem Description  
This study focuses on passenger flow prediction in multiple stations of urban rail transit, using the historical swipe card data of the AFC system to predict the future inbound and outbound traffic simultaneously. That is, for a given target time slice \(t\), the spatio-temporal flow data of the past \(n\) time slices are taken as input for the prediction model, and the predicted value for the time slice \(t\) is the output. We built a spatial matrix within each time interval using the location information of stations and then converted the inbound and outbound passenger flow into a matrix of dual channels. The real passenger flow at time slice \(t\) can be expressed as a tensor \(X_{t}=\left(X_{t}^{(in)},X_{t}^{(out)}\right)^{T}\in R^{2\times I\times J}\), where \(X_{t}^{(in)}\), \(X_{t}^{(out)}\) are the real inbound and outbound volumes respectively, \(I\times J\) represents the spatial area size. \(Y_{t}=\left(Y_{t}^{(in)},Y_{t}^{(out)}\right)^{T}\in\mathbb{R}^{2\times I \times J}\) denotes the predicted passenger flow at time slice \(t\), \(Y_{t}^{(in)}\), \(Y_{t}^{(out)}\) are the predicted inbound and outbound traffic, respectively. In general, the urban rail transit passenger flow prediction problem analyzed in this study can be formulated by:  
\[Y_{t}=F((X_{t-n},\cdots,X_{t-1})) \tag{1}\]  
where \(F\) is the mapping function, and \((X_{t-n},\cdots,X_{t-1})\) is the historical passenger flow tensor.
# Key Management Scheme Based on Route Planning of Mobile Sink in Wireless Sensor Networks
## 3 The Scheme Design
### Establishment of Session Keys
#### 3.3.1 Initial Stage  
Before the node deployment, the server generates a pair of public key \(P\) and private key \(S\) based on ECC for each node, which satisfies the relationship: \(P=S\times G\), where \(G\) denotes a base point on the elliptic curve. Each member node has a pre-stored unique identifier \(ID_{Ni}\), a pair of public and private keys (\(S_{Ni}\),\(P_{Ni}\)), a digital signature (\(W_{Ni}\),\(w_{Ni}\)), the binary symmetric function \(F\), and the hash function \(H\). MS has a pre-loaded unique identifier \(ID_{MS}\), a pair of public and private keys (\(S_{MS}\),\(P_{MS}\)). a digital signature (\(W_{MS}\),\(w_{MS}\)), and the hash function \(H\). The digital signature (\(W_{i}\),\(w_{i}\)) [20] can be expressed as follows:  
\[\left\{\begin{array}{l}W_{i}=r_{i}\times G=(x_{w_{i}},y_{w_{i}})\\ w_{i}=r_{i}{}^{-1}(H(ID_{i}||T_{i})+S\times x_{w_{i}})(mod~{}n)\end{array}\right. \tag{9}\]  
where \(r_{i}\) is a random value, \(G\) is a base point on the elliptic curve, \(H\) is the hash function, and \(S\) is the private key.  
Figure 3: The flowchart of the route planning algorithm for the movement of MSs.  
When node \(i\) sends a request for digital signature verification within time \(T\), the legality of node \(i\) can be validated only if we can validate: \(N_{i}=W_{i}\). The process of the validation can be expressed as follows:  
\[\begin{array}{ll}N_{i}&=w_{i}^{-1}H(ID_{i}\Big{\|}T)G+w_{i}^{-1}x_{w_{i}}P\\ &=w_{i}^{-1}(H(ID_{i}\Big{\|}T)+x_{w_{i}}S)G\\ &=w_{i}^{-1}r_{i}r_{i}^{-1}(H(ID_{i}\Big{\|}T)+x_{w_{i}}S)G\\ &=w_{i}^{-1}r_{i}\left\{r_{i}^{-1}(H(ID_{i}\Big{\|}T)+x_{w_{i}}S)\right\}G\\ &=w_{i}^{-1}r_{i}w_{i}G\\ &=W_{i}\end{array} \tag{10}\]
# A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs +
## 3 System Model  
In this section, we introduce the network topology used for VANETs in urban areas, incorporating clustering to enhance route connectivity. The network nodes are organized into clusters, grouping nodes with similar characteristics. Each cluster is led by a CH, which is responsible for managing communications within the cluster. A key concern in this topology is the threat of blackhole attacks among the vehicle nodes. These attacks falsely claim to have the optimal route to the destination and then drop packets without forwarding them, causing a disruption in the network performance. To address this challenge, we propose a DLSR protocol, which can predict and counter blackhole attacks by establishing secure routes to the destination. Additionally, we employ the DLC protocol, which groups vehicle nodes into clusters based on distance, speed, direction, and remaining energy. This approach ensures strong connectivity and enhances the overall performance of the network.
# Phytocannabinoids Biosynthesis in Angiosperms, Fungi, and Liverworts and Their Versatile Role
## 3 Biosynthesis of Phytocannabinoids
### Rhododend  
DCA and its derivative are produced and stored inside specialized glandular scales in _Rh. dauricum_. DCA utilizes carbon atoms from acetyl-CoA and farnesyl-CoA, with two significant intermediates, i.e., orsellinic acid (OSA) and GFA. DCA biosynthesis in _Rhododendron_ is split between the cytosol, plastid, and apoplastic spaces (Figure 6) [11; 36].  
The biosynthesis of DCA starts in the cytosol with polyketide formation, type III polyketide synthase (PKS) helps in acetyl-CoA chain extension. Then another enzyme, orcinol synthase (ORS), catalyzes orcinol, OSA, triacetic acid, tetracetic acid, lactone and phloroacetophenone, where malonyl-CoA (three units) act as a carbon donor. Furthermore, tetraketide cyclase catalyzes OSA from ORS [36; 37]. OA is transported to the plastid via a transporter, which is still unknown. Inside the plastids, the MEP pathway derives famesyl-CoA. The inhibition of the MEP pathway via clomazone decreases OSA and DCA synthesis. In contrast, inhibition of the mevalonate-dependent pathway via mevastatin led to an increment in OSA and DCA biosynthesis. Moreover, aromatic farnesyltransferase _Rh. dauricum_ prenyltransferase (PT) helps in regiospecific farmesylation; this enzyme mediates sequence identity with UbiA aromatic PTs that lie within chloroplasts. Geranyl-CoA and geranylgeranyl-CoA serve as the alternative prenyl donors used by PT, but their activity rate is 13%, and 2.5% of the activity acquired by farnesyl-CoA. GFA is synthesized as the intermediate within the plastids. Then, within apoplastic spaces, an oxidocyclization reaction takes place via DCA synthase (DCAS) forming CBC scaffold; reaction moves forward by H\({}_{2}\)O\({}_{2}\) release. Like \(\Delta^{9}\)-THCAS and CBDAS, DCAS is active enzymatically outside apoplastic spaces and dependent on O\({}_{2}\). In _Rh. dauricum_, DCA decarboxylated forms produce confluentin; spontaneous decarboxylation occurs via heat, irradiation, and during storage, similar to the decarboxylation acidic to neutral phytocannabinoids in _C. sativa_ trichomes [5; 11; 37].  
Apoplastic spaces serve as storage for many metabolites, essential oils, DCA, and confluentin. Moreover, GFA and DCA act as phytotoxic compounds in _Rh. dauricum_ cell culture, as they induce cell death. Similarly, H\({}_{2}\)O\({}_{2}\) formed as a side product in DCA biosynthesis also increases cell death by enhancing apoptosis-related reactions. However, to overcome cell death, autotoxicity, and cell damage, DCA storage occurs in the apoplast, and H\({}_{2}\)O\({}_{2}\) is released to participate in the plant-defense system and provide plant immunity. In _Rh. dauricum_,transport proteins and vesicle trafficking mechanisms are still not well understood and remain a valuable and exciting approach for further future investigations [1; 2; 37].  
Figure 6: Phytocannabinoids biosynthesis in Rhododendron. Abbreviations: DCAS, daurichromeic acid synthase; MEP, methylerythritol-4-phosphate; PT, preryltransferase.
## 5 Automatic Signal Recognition
### Extended Validation for Mixed Signals
#### 5.3.1 Dataset Extension for Mixed LTE-U and Wi-Fi Signals  
As LTE-U is not yet commercialized, capturing it directly from the operator is not an option. Hence, traces containing mixed RSSI samples are fabricated based on the previously collected data. Following the recent standardization of LTE License Assisted Access (LAA) [39], the eNodeB applies carrier sensing and random back off within a certain contention window, and the transmission burst may take place for 2 ms, 3 ms, 8 ms or 10 ms, depending on the channel access priority. This ensures that Wi-Fi or other technologies could also compete for the medium. In principle, there will be no over-the-air collision; hence, the mixed trace is simply composed of interleaving Wi-Fi and LTE down link signals, collected at the same location and on the same day. In total, 60 mixed traces are produced, 10 traces for each combination of the collection date and location.  
A part of the mixed RSSI trace is shown in Figure 13 for illustration purposes. A transmission burst of LTE (indicated by 'L') is a continuous range of RSSI randomly selected (random permutation is used to ensure that no sample is reused) from the LTE down link samples, whereas the length of the transmission burst is chosen among the specified values \((2,3,8,10)\) ms with equal probability. Similarly, RSSI from Wi-Fi traces (indicated by 'W') are also selected and inserted arbitrarily into the trace, though without the limitation on the transmission duration. The fabricated traces are only based on \(N=20\) and \(T=1\), the smallest averaging interval and largest observation time, to ensure that the duration of the transmission burst from LTE is accurate.
# Video over DSL with LDGM Codes for Interactive Applications
## 3 Methodology
### Modeling the Wireless Channel  
To model adverse channel conditions across a wireless link, \"bursty\" errors (time correlated errors) were modeled by a Gilbert-Elliott two-state hidden Markov model [31], as illustrated in Figure 5. This channel model was introduced into the ns-2 simulator. The Gilbert-Elliott channel model itself forms a two-state Markov chain. It is based on good and bad states, the probabilities of these states, and the probabilities of the transition states between them. In the bad state, \(B\), losses happen with higher probability, whereas in the good state, \(G\), losses happen with lower probability.  
\(P_{GG}\) refers to the probability of being in the good state and \(P_{GB}\) is the probability of a transition from the good state to the bad state. \(P_{BB}\) is likewise the probability of being in the bad state and  
Figure 4: Peak Signal-to-Noise Ratio (PSNR) evaluation framework.  
Figure 5: Gilbert-Elliott model.  
transitioning back to the same bad state. \(P_{BG}\) refers to the probability of a transition from the bad to good state. \(P_{GG}(P_{BB})\) can be interpreted as the probability of remaining in the good (bad) state, given that the previous state was good (bad). Conversely, \(P_{GB}\) represents the probability that, given that the previous state was good, a transition is made from the good to the bad state. By the law of total probability, all probabilities sum to one (certainty). Therefore, we have \(P_{GG}+P_{GB}=1\), resulting in Equation (10). A similar argument for the bad state leads to Equation (11).  
\[P_{GG}=1-P_{GB} \tag{10}\]  
\[P_{BB}=1-P_{BG} \tag{11}\]  
For the stochastic process to remain stationary in time,  
\[\pi_{G}P_{GB}=\pi_{B}P_{BG} \tag{12}\]  
where \(\pi_{G}\) and \(\pi_{B}\) are the steady-state probabilities of being in a good or bad state respectively. The law of total probability \(\pi_{B}=1-\pi_{G}\) again applies. Substituting this expression for \(\pi_{B}\) into Equation (12) easily leads to:  
\[\pi_{G}=\frac{p_{BG}}{P_{BG}+P_{GB}} \tag{13}\]  
Similarly, \(\pi_{G}=1-\pi_{B}\). Substituting this expression for \(\pi_{G}\) into Equation (13) easily leads to:  
\[\pi_{B}=\frac{P_{GB}}{P_{BG}+P_{GB}} \tag{14}\]  
Thus, the average loss rate produced by the Gilbert-Elliott channel model is  
\[L=p_{G}.\pi_{G}+p_{B}.\pi_{B} \tag{15}\]  
where \(p_{G}\) and \(p_{B}\) are the internal error rates of the good and bad states respectively.