esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results  
To perform our experiments, we employed the Incident Dispatch Data publicly available on the NYC OpenData website [23]. Each entry in the dataset contains detailed information about a specific incident, spanning from the time it was opened in the dispatch system to the moment it was marked as closed. At the time of writing, the dataset includes entries up until 3 October 2024.  
To model critical data in our proposed approach, we extracted all dataset fields containing timestamps, which were generated and recorded by the dispatch system whenever significant events occurred. A complete list of these fields (7 out of 31 columns in the dataset), along with their descriptions, is provided in Table 2.  
The dataset contains 24 additional fields that provide supplementary details about each incident, such as the borough name, dispatch location zip code, and police precinct code. These fields, categorized as ordinary data in our system, are collected by HEA until the corresponding incident is officially closed. Due to space constraints, Table 3 presents a representative subset of these fields, including example values to illustrate the data format. The complete list of fields and their descriptions is available in Appendix A and on the dataset's homepage [23]. The salient characteristics of such a dataset are in Appendix B.  
HEA operated continuously on a central server, receiving and storing incoming incident data in its local database. The data gateway was deployed on a cloud node, ensuring robust client interaction and seamless forwarding of incident data to HEA. The blockchain module, operating on a separate node, processed the incident data by generating a hash and recording it on the blockchain according to a predefined storage strategy. Once the transaction was successfully written on the blockchain, a transaction receipt was generated and returned to HEA for logging it onto the local database. Subsequently, both the receipt and the corresponding incident data, along with the computed hash, were transmitted to the cloud database. In our experiments, we evaluated three storage strategies: (i) time-based hash storage, (ii) threshold-based hash storage, and (iii) combined hash storage strategy.
# Development of a Natural Matrix Hybrid Hydrogel Patch and Evaluation of Its Efficacy against
## 2 Materials and Methods
### Cell Compatibility Analysis  
The cytotoxicity of patch was estimated based on the international standard used for biological evaluation of medical device (ISO 10993-12 and ISO 10993-5) [17].  
Briefly, 0.5 g of the patch sterilized by ethylene oxide gas was incubated in 50 mL of RPMI 1640 medium (BenchStable(tm) RPMI 1640, Gibco, Plymouth Meeting, PA, USA) containing 10% fetal bovine serum, 500 U/mL penicillin, and 500 ug/mL streptomycin (Gibco, USA) at 37 \({}^{\circ}\)C for 24 h under shaking. Afterwards, the patch extracts were filtered to remove insoluble material residues and sterilized by passage through a 0.2 mm filter. The cells were cultured at 37 \({}^{\circ}\)C in 5% CO\({}_{2}\) atmosphere for 3-5 days and 1 \(\times\) 10\({}^{5}\) I929 (murine fibroblast cell line) cells were seeded into each well of 96-well culture plates. After 24 h, the culture medium was replaced by either fresh RPMI 1640 medium or patch extracts. Cells were then further incubated for 24 h. After replacing the old medium, cell counting kit-8 (CCK-8, Dojindo Laboratories, Kumamoto, Japan) was added to each well, and the cells were incubated for 4 h (37 \({}^{\circ}\)C, 5% CO\({}_{2}\)). Absorbance at 450 nm was measured using an ELISA Reader (Infinite(r) 200 PRO, Tecan, Mannedorf, Switzerland) to evaluate cell viability. Cell viability (%) was expressed as the relative absorbance of the sample to that of the control (\(n=3\), means \(\pm\) SD).
## 9 Related Work  
We already discussed the literature that is conceptually closest to our work, namely [19], which explored mechanisms for handling \"wall-clock\" time in the context of action languages and ASP-based domain representations, and [21], where additive fluents were investigated. The body of literature on linear temporal logics (LTLs) [24; 25] is also relevant to the representation and reasoning about \"wall-clock\" time. Particularly interesting are the line of research on linear dynamic logic on finite traces (LTLf) [26; 27; 28], as well as the integration of LTLs with ASP [29], but while all of these approaches provide useful and convenient solutions to the _representational_ problem related to \"wall-clock\" time, it is not immediately clear whether they provide a solution to the _computational_ problem that we faced.  
More closely related to our computational problem is the recent work on overgrounding [30]. Overgrounding provides a \"ground-once-solve-many\" approach for the efficient processing of a continuous stream of data by limiting the number of times the corresponding ASP program is grounded. There are two important aspects that, in our opinion, limit the potential applicability of overgrounding to the problem at hand. First of all, overgrounding is most appropriate for a stream of data in which the new data is about more recent time points than the prior data. In our case, the new data may be about past time points, e.g., newly received information about tests (or exposure) that occurred in the past. Second, as we noted in Section 6, in the instances that are problematic for single-shot solving, it is the grounding process that times out due to a dramatic growth of the grounding of the program. Since overgrounding essentially relies on building a larger grounding than needed in order to accommodate future inputs without further grounding, it is difficult to see how the overgrounding technique may help address the performance issues that motivated our work without major modifications.  
It is worth pointing out that research has been recently conducted at the intersection of ASP and high-utility pattern mining (HUPM). The work in [31] describes an approach that lifts the typical assumption according to whcih each item of a knowledge base is associated with a single utility. It is conceivable that approaches of this kind may provide insights into alternate techniques for improving efficiency in the context of the problems considered in this paper. Additionally, it may be interesting to investigate how HUPM may be leveraged go beyond the reasoning task considered here, and particularly for mining the data received by the system and identify potentially relevant patterns, such as unexpected virus propagation pathways.  
Finally, other solvers with multi-shot capabilities have been recently released, e.g., [8]. An investigation on the relationship between our techniques and the multi-shot computation capabilities provided by these solvers is certainly important, but beyond the scope of the present paper.
# Numerical Analysis of Friction Reduction of Grouting in Long-Distance Pipe Jacking
## 2 Theoretical Calculation Methods for the Jacking Force  
During the pipe jacking processes, the axial loads primarily include the front resistance of the pipe and the frictional resistance between the pipe and the surrounding soil. The frictional resistance around the pipe is influenced by the soil characteristics, contact conditions, and properties of the grout, which are reflected by the interface friction angle. The forces acting on the pipe are illustrated in Figure 2.  
Based on the axial force equilibrium, the total jacking force required can be expressed as  
\[F=P_{A}+P_{f} \tag{1}\]  
Figure 2: Analysis of pipe forces.  
\(P_{A}\) represents the front resistance, which is influenced by factors, such as the soil type, excavation depth, and support pressure.  
\(P_{f}\) represents the frictional resistance, which is the circumferential resistance generated by the interaction between the pipe and the surrounding soil.
## 1 Introduction
(2012) examined the SSE from the precedent treatment stage on the HF propagation and shape at next stages by using the Unconventional Fracture Model (UFM) model. According to the detailed simulation study, the authors indicated that the HF propagation is more dominated by the SSE when the principal stress anisotropy is smaller. In a formation with small stress anisotropy, fracture interaction can lead to dramatic divergence of the fractures as they tend to repel each other. However, they also pointed out that even when the stress anisotropy is large enough to limit the fracture turning magnitude, the stress shadowing still has a strong effect on the fracture geometry and proppant placement. [21]. The influence of the SSE generated by the fractures may vary as the hydraulic fracture is generated transversely or longitudinally with respect to the horizontal well. However, little work has been performed to understand the relationship between the principal stresses, the lateral direction of a horizontal well, and HFT efficiency. Therefore, we performed this study to achieve the following,  
* construction of the reservoir model in Brushy Canyon formation and reflect the in-situ principal stress,
* investigation of the effects of \(\sigma_{\rm Hmax}\)-dir in the Permian Basin and drilling direction of horizontal wells on the hydraulic fracturing treatment efficiency,
* analysis of the stress shadow influence according to the transverse or longitudinal fracture direction.
## 2 Background Knowledge
### Mathematical Description of the Circuit-Partitioning Problem
For partition \(j\), the connectivity of vertex \(v\) to partition \(j\) can be defined as:  
\[C(v,j)=\sum_{u\in j}e(u,v) \tag{6}\]  
Among them, \(e(u,v)\) denotes the weight of the edge between vertex \(v\) and vertex \(u\) in partition \(j\), and \(u\in j\) denotes all vertices belonging to partition \(j\). The level of vertex-to-partition connectivity reflects the relevance between vertex \(v\) and partition \(j\). The higher the connectivity, the closer the relationship between vertex \(v\) and partition \(j\). The definition provides an important theoretical foundation for the subsequently proposed initial partitioning algorithm and refinement algorithm. The vertex movement gain, denoted as \(g(v,n)\), refers to the change in the objective function when a vertex \(v\) from subset \(G_{m}\) is moved to another subset \(G_{n}\). The gain can be calculated by the following formula:  
\[g(v,n)=C(v,n)-C(v,m) \tag{7}\]
# Computational Evaluation of Turbulent Supersonic Jet Impinging on Inclined Plate
## 2 Computational Model
### Numerical Settings  
In the framework of the OpenFOAM package, the _rhoP_impleFoam solver was employed for the current numerical investigations. This transient, density-based, compressible flow solver has been validated over a wide range of low to medium supersonic Mach numbers, showing a good ability to capture shock structures in several different cases. For instance, it was successfully used for freely exhausting jets [22], and transonic flow over airfoils [23].  
Due to the hybrid nature of the present DES formulation, suitable numerical schemes should have low dissipation and dispersion errors over the resolved range of scales, while being dissipative for non-resolvable small scales [24]. Therefore, the numerical method used for the gradient discretization was the second-order _cellLimited Gauss linear_ scheme, with a correction factor of 0.5. In fact, due to the non-orthogonality of the mesh away from the plate, such a parameter needed to be used for an accurate estimation of gradients and diffusive fluxes. The divergence terms, including the turbulence quantities, were discretized using the second-order _Gauss limitedLinear_ scheme. Moreover, particular attention was paid to the discretization of the velocity field. Due to the high compressibility nature of the flow, the total variation diminishing (TVD) scheme _Gauss limitedLinearV_ was chosen for the calculation of the velocity divergence. Such a particular scheme, which prevents the creation of unphysical local extrema, allows a corrected shock wave capturing effect in the proximity of the wall, resulting in a more accurate estimation of the pressure distribution on the plate [25]. Also, a linear scheme was used as the default interpolation scheme. Finally, the second-order implicit Adams-Bashforth scheme was selected for the time integration, as it has been shown to provide a good combination of accuracy and stability, compared to other methods such as the Crank-Nicolson scheme, which shows an oscillating behaviour [26]. The time step size was allowed to be adjusted such that the maximum CFL number did not exceed the value of 0.8, which was empirically found to be sufficient for numerical stability.
## 6 Simulation Evaluation
### Simulation Results
#### 6.2.2 Total Service Time  
Figure 5 shows the effect of the size of the computational task offloaded by the SN on the service time. The total service time comprises the computation time, offloading time, and propagation latency of the sub-task and computing results. Here, the size of the computational task in the dataset is a variable parameter and considers 18 values. In order to calculate the propagation delay, the distance between the SN and BS is assumed to be 100 m when the task is offloaded. Considering the mobility of BS, the distance between the SN and BS is assumed to be 300 m when the BS sends back the result of the computational task. In our proposed method, the BS with the shortest distance to the primary coalition node is selected for the secondary coalition node, so the link between the cooperation nodes is more reliable with our method; meanwhile, with any other three methods, the service session can terminate in failure probably with the movement of BSs since the BS can move out of the transmission range.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **Success Rate** & **Failure Rate** \\ \hline Proposed method & 90.12\% & 9.88\% \\ Greedy method & 60\% & 40\% \\ Always-Migrate method & 21.9\% & 78.1\% \\ Random method & 12.66\% & 82.34\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Success rate and failure rate for four algorithms.  
Figure 6 shows the effect of the number of SNs which offload different tasks to the same BSs on the total service time. Here, we consider three parameters, i.e., the size of computational task, the distance between SN and the primary BS, and the distance between the SN and the secondary BS. The computational size is 1000 MB, and the distance between the SN and the primary BS when offloading is 100 m. Alternatively, the distance is changed to be 300 m when the BS sends back the result to the SN after processing the task due to the mobility of BS. We assume that the number of SNs whose tasks are processed by the same BS is 14 in the experiment. It can be seen from Figure 6 that the total service time of the random scheme fluctuates between that of the Always-Migrate and our proposed scheme. Simultaneously, the overall service time of the Random Process is also near or equal to our proposed method. Nevertheless, it is never lower than that of our proposed method. Hence, our proposed method reduces the total service time. This is because, in our proposed method, the computational task is divided into two sub-tasks. Afterwards, the service time of each sub-task is calculated individually. From the combinations of service time of each sub-task, our proposed method chooses the divided sub-tasks with a lower service time. It should also be noted that the total service time can be further reduced by increasing the number of BSs for cooperative computing and parallel processing.  
Figure 5: Overall service time vs. the total size of the computational task offloaded by the SN.  
Figure 6: Overall service time vs. number of SNs on the same one BS.
# Ultra-Low-Power Wide Range Backscatter Communication Using Cellular Generated Carrier +
## 3 Propagation Models
### ITU Outdoor D2D Model
The transition distance \((w)\) of 20 m was assumed in Figure 2. Algorithm 2 presents the pseudocode of the ITU outdoor D2D propagation model. ```
Input:\(f_{c,MHz}\), \(d_{km}\), \(p\), \(w\), \(L_{urban}\), \(\sigma_{LoS}\), \(\sigma_{NLoS}\) Output:\(PL(d,p)\)
1:procedureComputation of path loss
2 Compute median value of path loss in LoS \((PL_{LoS,dB}^{Median})\) using Equation (2)
3 Compute LoS location correction \((\Delta_{LoS})\) is the using Equation (3)
4 Compute path loss in LoS condition \((PL_{LoS,dB})\) using Equation (1)
5 Compute median value of path loss in NLoS \(PL_{NLoS,dB}^{Median}\) using Equation (5)
6 Compute NLoS location correction \((\Delta_{NLoS})\) is the using Equation (6)
7 Compute path loss in NLoS condition \((PL_{NLoS,dB})\) using Equation (4)
8 Compute the LoS distance \((d_{LoS})\) for given location percentage \((p)\) by using Equation (8)
9 Compute path loss in transition state distance \((PL_{Trans,dB}(d,p))\) for given transition distance \((w)\) using Equation (9)
10 if\(d<d_{LoS}\)then
11\(PL(d,p)=PL_{LoS,dB}(d,p)\)
12 elseif\(d>d_{LoS}+w\)then
13\(PL(d,p)=PL_{NLoS,dB}(d,p)\)
14 else
15\(PL(d,p)=PL_{Trans,dB}(d,p)\)
16 end if
17 return\(PL(d,p)\)
```  
**Algorithm 2** ITU outdoor D2D propagation model
## 4 Results and Discussion
### Impact of Phase Wind-Up  
#### 4.2.1 Carrier Phase Wind-Up Effect  
The electromagnetic wave emitted by GPS satellites is typically in the form of right-hand circular polarization (RHCP). Relative rotation of the transmitting or receiving antennas will change the carrier phase, which is called the phase wind-up (PWU) effect. Note that this error only affects the phase observations and not the pseudo range. We compute the PWU corrections (\(\omega\)) using the following expressions [18]:  
\[\mathbf{D}=\mathbf{x}-\mathbf{k}(\mathbf{k}\cdot\mathbf{x})+\mathbf{k}\times\mathbf{y}\] \[\overline{\mathbf{D}}=\overline{\mathbf{x}}-\mathbf{k}(\mathbf{k}\cdot\overline{ \mathbf{x}})-\mathbf{k}\times\overline{\mathbf{y}}\] \[\omega=\text{sign}\big{(}\mathbf{k}\cdot\big{(}\overline{\mathbf{D}} \times\mathbf{D}\big{)}\big{)}\text{arccos}\Bigg{(}\frac{\overline{\mathbf{D}}\cdot \mathbf{D}}{|\overline{\mathbf{D}}||\mathbf{D}|}\Bigg{)}\]  
where \(\mathbf{D}\), \(\overline{\mathbf{D}}\) are the effective dipole vectors of the transmitter and receiver antenna, respectively; \(\mathbf{k}\) is the unit vector pointing from transmitter to receiver antenna; \(\mathbf{x}\), \(\mathbf{y}\) are the corresponding dipole unit vectors of the transmitting antenna and \(\overline{\mathbf{x}}\), \(\overline{\mathbf{y}}\) are the corresponding dipole unit vectors of the receiving antenna.  
First, PWU corrections can be absorbed by epoch-wise estimation parameters if uncorrected, and they are finally reflected in the accuracy of orbit and clock offset. Second,  
Figure 6: The difference in GRCC’s clock offset between solutions using nominal and measured attitude.  
the PWU effects can be divided into two categories: the PWU caused by the rotation of the transmitting antenna and the PWU caused by the rotation of the receiving antenna [20]. The ground receiver antenna always remains in a static state and remains oriented towards a fixed reference direction (usually north). The local east north up (ENU) coordinates can be used for the ground receiver, and the corresponding dipole unit vectors of the receiving antenna can be defined as pointing to the east and north [19]. This means that only the PWU caused by the rotation of the transmitting antenna is taken into consideration. However, the LEO antenna in high-speed motion also has relative rotation. Thus, the receiver orientation must be carefully defined to calculate the correct PWU. In this paper, we use SBF coordinates to define the corresponding dipole unit vectors of the receiving antenna instead of ENU coordinates.
## 3 Preliminaries
### Security Assumption  
In this paper, the adversary, whose goal is to let the sink accept the fraudulent data that is claimed to be the one sensed by \(s_{i}\) at the target round \(\bar{r}\), is considered. The adversary can simultaneously compromise \(k\) sensors _at each round_. The conceptual illustration is shown in Figure 2. It should be particularly noted that the adversary is assumed to migrate and control different sets of compromised sensors in the literature. We have a stronger adversarial assumption that, once being compromised, the sensor is always under the control of the adversary. The secret information stored in the compromised sensors will therefore be exposed to the adversary. The adversary can launch sensor compromise attack right after the sensor deployment in order to maximize its the possibility of subverting the network functionality. Nevertheless, since each sensor will be reset at the end of each collection interval, we focus on the security issue in a specific collection interval. We also note that the adversary is a global eavesdropper, which means that it can eavesdrop on each message transmitted over the network. In addition, the adversary learns its objective, \(s_{i}\) and \(\bar{r}\), at the end of round \(\bar{r}\). Note that \(s_{i}\) and \(\bar{r}\) are unknown to the sink so that the sink cannot apply protection only on specific sensors.  
Since the adversary focuses on substituting specific data, with the consideration of the unattended nature of UWSNs, _forward-secure property_, which means that the adversary gains no advantage about the data sensed before the round \(r\) from the sensor compromised at the round \(r\), can be useful in resisting the adversary. Key evolution, in which the key used in round \(r\) is evolved from the key used in round \(r-1\) through the cryptographic hash function, is a simple means to provide forward-security. Nevertheless, forward-secure cryptographic techniques are effective against only _reactive adversary_, which means that the adversary starts compromising sensors after it identifies the target, but is of no use when the _proactive adversary_, which means that the adversary can compromise sensors even before identifying the target, is considered. As a consequence, the _backward-security_, which means that the adversary learns no secret of the next round, is required. An authentication scheme with both forward-security and backward-security is called _key-insulated_, which means that the adversary can learn only the current secret.