esa-sceva/satcom-chunk-collection huggingface dataset:
# Progressive Multiple Alignment of Graphs
## 4 Computational Results
### Ambiguous Edges  
Ambiguous edges in intermediate alignments are a concept that arises in graph alignments and more specifically in the step-wise construction of MGAs as super-objects. It has no counterpart in approaches that solely focus on the maximal common sub-objects. Since ambiguous edges arise from alignment columns with disjoint representation of input graphs, their number is expected to increase with the number of gaps. This aspect can be empirically verified as well. Figure 6 exhibits the perfect correlation (Pearson correlation coefficient 0.99) found between said variables over our constructed test set.  
In general, random guide trees tends to introduce more gaps and thus more ambiguous edges; see Appendix A.3 for details. As shown in the right panels of Figure 5, there is slight improvement from the ambiguous edges when random guide trees are used. In this case, it appears that ambiguous edges can compensate for the larger differences in-between input graphs. At least for the samples of small and fairly similar graphs considered here, ambiguous edges seem to play no significant role when the alignment follows a similarity-based guide tree. We suspect that ambiguous edges may gain more practical importance for large graphs with more pairwise differences.  
Figure 5: Consensus graphs as function of the voting threshold \(\alpha\). (**a**) The size of the consensus, as expected, decreases monotonously with increasing fraction \(\alpha\) of non-gaps in the retained alignment columns. (**b**) Correspondingly, the fraction of the vertices of the input graphs that are retained in the consensus decreases. (**c**) The distance between the reference graph \(G_{0}\) and the alignment of its noisy offspring \(G_{1}\) through \(G_{7}\), however, reaches a minimum when only the columns that contain less then 50% gaps are used to form the consensus. In each panel, we compare guide trees computed with kernel-based similarity (full lines), random guide trees without considering ambiguous edges (dashed lines), and random guide trees taking ambiguous edges into account (dotted lines). The latter are nearly indistinguishable form the kernel-based guide trees. Here line colors are a visual aid to distiguish the methodologies and their variations outlined before.  
Figure 6: Correlation of the number of ambiguous edges with the number of gaps. The values here correspond to the 400 alignments resulting from running the 8 experiments, described in Figure 7, over our set of 50 scenarios.
## 8 Results and Discussions
### Uncertainty Analysis from Initial Stage to Stable VANET
#### 8.2.1 The Effect of Mobility Scenario  
To simulate the adversarial effect, we have considered half of the nodes malicious i.e., 25 nodes having speed from 0 to 10 m/s in an arbitrary fashion. From Figure 8, we can see that 3 VSR is better in throughput and packet loss ratio as compared with other routing protocols i.e., AODV and AASR. The performance of these protocols may be degraded under different mobility scenario. Despite the performance variation, our proposed scheme always achieves better throughput and lower packet loss as can be seen in Figure 8a,b, because it reduce computational overhead by avoiding complex cryptographic techniques. Meanwhile, AODV has lower delay values then AASR because if AASR is attacked by adversary it requires more cryptographic processing, which increases the delay. As a result, sometimes AASR performs worse than AODV, e.g., in the \"slow\" movement scenarios. The curves of the end-to-end delay are shown in Figure 8c, thus 3 VSR is a better choice to adopt.  
Figure 7: (**a**) Simulated analysis of belief, disbelief and uncertainty at network start. (**b**) Simulated behavior of trusted, malicious and new comers After some interaction.  
Figure 8: Performance comparison under different mobility setting: (**a**) Packet throughput. (**b**) Packet loss ratio. (**c**) End-to-end delay.
## 3 SDMob Architecture
### Basic SDMob Architecture
#### 3.1.1 Collision Avoidance between Control Plane and Data Plane  
The SDN-based architecture comes with an inevitable control overhead that can saturate the MAC layer congestion mechanism in both upward and downward directions.  
Figure 2: A schematic view of the basic SDMob architecture in **(a)** supporting a single mobile node compared with enhanced SDMob **(b)** with multiple mobile nodes and buffers.  
To streamline the traffic, we have implemented a reserved period for control packets called Control Window (CW) out of which transmission of control packets is prohibited. CW can be adjusted based on network conditions; for instance in a network with a long delay between the MN and the controller, it is advised to select a longer CW to allow the control packets to reach the controller. A long CW limits the available time for data packets, and hence data packets are expected to be further delayed.
## 3 Experimental Results
### AE Activity
The relationship between confining pressure and AE activity has been studied previously [37], using the following expression:  
\[\tau_{\text{eff}}=\sigma_{\text{s}}-(c_{0}+\sigma_{N}\tan\varphi) \tag{4}\]  
where \(\tau_{\text{eff}}\) is effective stress on fracture surface, \(\sigma_{0}\) is cohesion on fracture surface, \(\varphi\) is the internal friction angle on the fracture surface, \(\sigma_{\text{S}}\) is the shear stress on fracture surface that was caused by axial stress and confining pressure, and \(\sigma_{N}\) is the vertical stress on fracture surface caused by axial stress and confining pressure. Using Equation (4) and the geometric relationship that is shown in Figure 7e, Equations (5) and (6) can be derived:  
\[\sigma_{\text{s}}=(\sigma_{1}-\sigma_{3})\cos^{2}\beta+\sigma_{3}\sin^{2}\beta \tag{5}\]  
\[\sigma_{N}=(\sigma_{1}-2\sigma_{3})\cos\beta\sin\beta \tag{6}\]  
where \(\sigma_{1}\) is the axial stress, \(\sigma_{3}\) is the confining pressure, and \(\beta\) is the horizontal inclination of the fracture surface. When the axial loading stress was the same, the effective shear stress on the fracture surface became lower with increasing confining pressure, which inhibited crack expansion and AE activity.
# Network Measurement and Performance Analysis at Server Side
## 3 Measurement Methodology
### Performance Analysis
* The flight size \(F>MSS\) and remains unchanged for at least three flights. * The flight is not limited by slow start, receive window or network congestion. The first condition ensures that the network is unsaturated (i.e., flight size is less than network BDP). The second condition ensures that the flight size is fixed by send buffer. The flight size is required to be at least one MSS, since a send buffer less than one MSS is impractical in real world. **Application limited. ** Interactive applications (SSH, HTTP, etc.) might not generate data fast enough to fill the network. A flight is considered application limited if the following conditions are met:  
* The flight size \(F<0.8\cdot BtlBw\cdot RTprop\). * The flight contains at least one less-MSS-sized packet. * The flight is not slow start limited, receive window limited, congestion limited or send buffer limited. It is worth noting that we cannot just make a conclusion based on the second condition, since a fixed small send buffer may also introduce less-MSS-sized packets. **Congestion control limited. ** A flight is deemed congestion control limited if the size \(F<0.8\cdot BtlBw\cdot RTprop\), and is not limited by above factors. **Bandwidth limited. ** A flight is considered bandwidth limited if more than half of the delivery rate samples in the flight are large than \(0.8\cdot BtlBw\). **Bufferbloat limited. ** The flight is determined to be bufferbloat limited if all the RTT samples are larger than \(2.5\cdot RTprop\). Note that bufferbloat is independent of all the limitations above since it focuses on delay rather than throughput. \begin{table}
\begin{tabular}{c l} \hline \hline
**Performance Bottleneck** & **Method** \\ \hline Slow start limited & Consecutive flight size fits the exponential relationship \\ Receive window limited & Minimum receive window size \(\leq 3\cdot MSS\) \\ Congestion limited & Packet retransmissions are seen and at least one RTT sample \(>1.5\cdot RTprop\) \\ Send buffer limited & Flight size \(F<0.8\cdot BDP\) and remains unchanged for at least three flights \\ Application limited & Flight size \(F<0.8\cdot BDP\) and at least one less-MSS-sized packet is seen \\ Congestion control limited & Flight size \(F<0.8\cdot BDP\) \\ Bandwidth limited & More than half of the delivery rate samples in the flight are large than \(0.8\cdot BtlBw\) \\ Bufferbloat limited & All the RTT samples are larger than \(2.5\cdot RTprop\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of EVA’s method to identify performance bottlenecks.
# Software Integration of Power System Measurement Devices with AI Capabilities
## 2 Monitoring Devices
### Smart Meter  
A smart meter calculates consumption or production in a more detailed way than conventional meters. Domestic meters in urban low-voltage networks are common. They allow local service providers to monitor and manage the consumption of each user effectively. Their accuracy ranges between \(\pm\)1% and \(\pm\)2%. They usually have an adjustable sending rate between 1 and 60 min.
# Influence of Solvent Selection in the Electrospraying Process of Polycaprolactone
## 3 Selection of Solvent Systems Used in PCL Electrospraying
### Solution Properties for Tailoring Characteristics of Electrosprayed PCL Particles
#### 3.3.1 Droplets or Fibers
among macromolecular chains, while the latter increases with the increase in D. Therefore, spherical particles may be obtained by selecting a polymer/solvent system with a low value of D, while matrix or fibrillar structures resulting from a higher entanglement and therefore a higher value of D or a partial solubility of the polymer in the system. According to [PERSON] and [PERSON], and [PERSON] et al., with a fixed PCL concentration, the use of solvents such as 1-methyl-2-pyrrolidone, formic acid and N, N-dimethylformamide, in which the solubility of PCL is partial, results in smooth and uniform fibers [120; 146]. On the other hand, the use of solvents that perfectly solubilize PCL such as toluene, chloroform, dichloromethane, and tetrahydrofuran leads to the formation of beaded structures or spherical particles.
## 5 Discussion
Future research can further optimize the model in the following directions: first, the Transformer mechanism or a more efficient attention mechanism can be introduced to further enhance the feature expression ability of the model in complex scenarios; second, an adaptive data augmentation strategy can be combined to make the model have a better generalization ability in the face of variable field environments; lastly, quantization techniques can be explored in response to the deployment requirements or model pruning methods to make Slim-YOLO more suitable for resource-constrained edge devices and realize more efficient real-time detection.
## 2 Theory Background and Methodology
### Description of the Equivalent Source Method  
The principle of ESM is that the sound field radiated by an arbitrarily shaped source can be substituted by a number of equivalent sources on a fictitious plane. This method is not involved in the Fourier transform in the wave number domain, hence it can avoid \"windowing effects\" and \"wraparound errors\" fundamentally. The diagrammatic sketch of ESM is shown as Figure 1.  
It is assumed that there are \(N\) virtual equivalent sources on equivalent source plane and \(M\) microphones on the measurement plane. The sound pressure at the \(m\)-th measurement point can be expressed as Equation (1):  
\[p(m)=\sum_{n=1}^{N}g(\mathbf{r}_{m}|\mathbf{r}_{n})q_{n} \tag{1}\]  
where \(g(\mathbf{r}_{m}|\mathbf{r}_{n})\) is the monopole pressure-pressure transfer function linking the \(n\)-th equivalent source to the \(m\)-th microphone, and it can be expressed as in Equation (2) [26]:  
\[g(\mathbf{r}_{m}|\mathbf{r}_{n})=\frac{exp\Big{(}-jk||\overset{\rightarrow}{r_{nm}}|| \Big{)}}{4\pi||\overset{\rightarrow}{r_{nm}}||} \tag{2}\]  
where \(k\) is the acoustic wavenumber (in rad/m), and \(||\overset{\rightarrow}{r_{nm}}||\) is the distance between the considered source-microphone couple. Then, the relationship between measured sound pressure and equivalent source strengths can be described in matrix vector notation as Equation (3):  
\[\mathbf{p}=\mathbf{A}\mathbf{q} \tag{3}\]  
where \(\mathbf{A}=j\rho\omega g(\mathbf{r}_{m}|\mathbf{r}_{n})\) is the transfer matrix relating the measured pressures to the equivalent sources, \(\omega\) is the angular frequency (in rad/s ), and \(\rho\) is the density of the fluid (in kg/m\({}^{3}\)). \(\mathbf{p}\) (size \(M\times 1\)) is the column vector that contains the pressures at the \(M\) measurement points, and \(\mathbf{q}\) (size \(N\times 1\)) is the column vector that contains the source strengths.  
The source strength vector \(\mathbf{q}\) is typically obtained in a least-squares sense by minimizing the cost function, as seen in Equation (4) [11]:  
\[\mathbf{\Gamma}_{\lambda}(\mathbf{q})=||\mathbf{p}-\mathbf{A}\mathbf{q}||_{2}^{2}+\lambda||\mathbf{q} ||_{2}^{2} \tag{4}\]  
where \(||\cdot||_{2}\) denotes \(l_{2}\) norm. \(\lambda\) is the regularization parameter. Once the source strengths \(\mathbf{q}\) are obtained, the sound pressure on any reconstruction plane can be calculated as Equation (5):  
\[\mathbf{P}_{R}=\mathbf{G}\mathbf{q} \tag{5}\]  
where \(\mathbf{G}\) is the transfer matrix relating the reconstruction points to the equivalent sources.  
Figure 1: The diagrammatic sketch of the equivalent source method (ESM).
# Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering via Item-Variance Weighting
## 3 Proposed Approach
### Example of TCIBCF Approach in RSs  
Here, we present an example to explain the TCIBCF approach more clearly. Table 1 shows a user-item rating matrix by four users for seven items. The user set is comprised by \(\{U_{1},U_{2},U_{3},U_{tu}\}\), where \(U_{tu}\) means the target user. The item set consists of \(\{I_{1},I_{2},I_{3},I_{4},I_{5},I_{6},I_{7}\}\). The rating value is from 1 to 5, where a higher value indicates that the user likes the given item more. Table 2 illustrates a user-item time matrix which shows the time that the user gave the rating in Table 1 and we take one day as a unit of rating time.  
In the traditional IBCF approach, because \(I_{6}\) and \(I_{7}\) have the same rating scores from \(\{U_{1},U_{2},U_{3}\}\), respectively, thus they will have the same neighborhood and predicted rating scores. However, \(I_{6}\) and \(I_{7}\) may have different rating time and characteristics, so they should have different recommended order for the target user \(tu\). In our proposed TCIBCF approach, after the time-aware similarity and covering-based rating prediction, neighborhood and predicted rating scores are quite different between \(I_{6}\) and \(I_{7}\). The detailed procedures of TCIBCF are as follows.  
Step 1: Time-aware similarity computation. In order to compute the time-related correlation degree (Equation (3)), we treat \(T_{0}=30\) days in \(\lambda=\frac{1}{T_{0}}\), it means the time weight is reduced by one month. Then, we use Equation 7 to compute item-item similarity, Table 3 shows the result of item-item similarity with time-related correlation degree.  
Step 2:  
\[N_{7}(3)=\{I_{1},I_{2},I_{6}\}\]  
Step 3: Covering-based rating prediction. Here, we treat item whose rating is great than or equal to 3 as the target user's preferred item, then, the target user's preferred item set is \(R_{tu}=\{I_{2},I_{3},I_{5}\}\). According to the covering degree we proposed, we compute the covering degree between each item and the target user's preferred item set \(R_{tu}\):  
\[CD(I_{1},R_{tu})=2/3;CD(I_{2},R_{tu})=1/3;\]  
\[CD(I_{3},R_{tu})=1/3;CD(I_{4},R_{tu})=2/3;\]  
\[CD(I_{5},R_{tu})=1/3;CD(I_{6},R_{tu})=1/3;\]  
\[CD(I_{7},R_{tu})=1/3.\]  
Then, we utilize Equation 9 to predict the rating score for \(I_{6}\) and \(I_{7}\). Here \(P_{U_{tu},I_{6}}=3.811,\ P_{U_{tu},I_{7}}\) = 1.600.  
Step 4: Item recommendations. Because \(P_{U_{tu},I_{6}}>P_{U_{tu},I_{7}}\), if we select the top one movie as recommendation, \(I_{6}\) will be recommended to the target user \(tu\).
# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
###### Abstract
Furthermore, task requestors may consider the outcomes or results as private assets and may not want to disclose them. In the crowdsourcing IoT, it is critical to preserve the privacy of both the requestors and the participants. Besides privacy, trust is also another critical factor in establishing confidence and privacy between the entities. The current and existing crowdsourcing approaches which resolve privacy and trust issues do not focus on privacy and trust during data exchanges in IoT or crowdsourcing IoT. This paper also reviews trust and privacy by integrating crowdsourcing and IoT at a later stage. Furthermore, the existing solutions utilize a trust scoring system and a centralized server. These scoring systems can be exploited and manipulated by eavesdroppers. Therefore, the specific research works of trust and privacy for edge-based crowdsourcing are also reviewed. Privacy is also dependent on the security of data and infrastructure. Security threats usually come from the external environment. The external attacks may involve eavesdropping on communication channels to intercept the encrypted sensing data. These attacks may be disguised as legitimate and authorized participants to submit false data to the fog nodes in the fog architecture. Hence the latter part of the paper also reviews some security aspects in crowdsourcing IoT and edge-based crowdsourcing. Finally, the paper presents the research works on diverse applications of crowdsourcing IoT. The applications include transport and smart cities, health, localization, monitoring and guidance, 3D, video and live streaming, se mantics and data mining, disaster/emergency and risk management, ocean/marine and environment, recruitment and workers, finance, budget and planning, and so on. To the best of the authors' knowledge, there are no comprehensive reviews on specific research works from different aspects for crowdsourcing IoT and edge-based crowdsourcing. The remaining paper is organized as follows. Section 2 presents the research methodology employed in this paper. Section 3 presents architectures for crowdsourcing and a proposed crowdsourcing IoT architecture. This is followed by Sections 4 and 5, which discuss trustworthiness, privacy, and security in Crowd-IoT and resources, sharing, storage, and energy considerations in Crowd-IoT. Various applications for the Crowd-IoT are discussed in Section 6. Section 7 concludes the paper.
# Depth-Optimized Quantum Circuit of Gauss-Jordan Elimination
## 3 Parallel Implementation for Quantum Circuit of Gauss-Jordan Elimination
### Parallel Implementation of Swap: Copying Pivot
#### 3.2.1 Replacing Controlled-Swap Gates with Toffoli Gates  
We use Toffoli gates instead of controlled-swap gates. As far as we know, this optimization method was first presented in [9]. We briefly review this method. Let the \(anc_{0,0}\) (pivot of \(H_{0,0}\)) be \(|0\rangle\). If controlled-swap gates are used, the swap between the first row \(H_{0\sim 4,0}\) and the second row \(H_{0\sim 4,1}\) is performed as follows (where \(anc_{4,0}\) is \(anc_{c,0}\)):  
\[CSWAP(anc_{0\sim 4,0},H_{0\sim 4,1},H_{0\sim 4,0}):anc_{0\sim 4,0}^{\prime}\to anc_{0\sim 4,0},H_{0\sim 4,1}^{\prime}\to H_{0\sim 4,0},H_{0\sim 4,0}^{\prime}\to H_{0\sim 4,1}.\]  
In contrast, if Toffoli gates are used, the first row changes to \(|1\rangle\) through the following linear operation (where \(anc_{4,0}\) is \(anc_{c,0}\)):  
\[Toffoli(anc_{0\sim 4,0},H_{0\sim 4,1},H_{0\sim 4,0}):anc_{0\sim 4,0}^{\prime}\to anc_{0\sim 4,0},H_{0\sim 4,1}^{\prime}\to H_{0\sim 4,1},\]  
Figure 2: Quantum circuit implementation of the swap stage (where \(|H_{0,0}\rangle\) is the pivot).  
\[H^{\prime}_{0\sim 4,0}\to H_{0\sim 4,0}\oplus(anc_{0\sim 4,0}\cdot H_{0\sim 4,1}).\]  
Both methods successfully change the pivot (\(H_{0,0}\)) to \(|1\rangle\) (assuming \(H\) is invertible). However, as described in [9] (see Section 3.2), replacing controlled-swap gates with Toffoli gates is more efficient in terms of quantum resources (controlled swap gate can implemented using one Toffoli gate and two CNOT gates). Further, using Toffoli gates between rows has the advantage of parallelization compared to using controlled-swap gates. The controlled-swap gates in Figure 1 cannot be performed in parallel (i.e., subsequent operations must wait for previous operations to complete). However, if the controlled-swap gates in Figure 1 are replaced with Toffoli gates, many of these Toffoli gates can be executed simultaneously.
# Resource-Based Quality Performance Estimation Method for Construction Operations
## 1 Introduction  
In the construction industry, quality is one of the most significant management targets with duration and cost in delivering projects, determining the success or failure of a project [1]. Therefore, most construction projects demand a plan to meet the appropriate schedule, budget, and quality targets. Duration and cost are always carefully considered factors, while quality is treated as a factor requiring only approval in construction projects [2, 3]. Since the attainment of acceptable levels of quality for construction projects has not been defined, there is no efficient and formal quality control procedure [4]. In terms of construction management, shortening duration or reducing costs is considered a successful management case, but the quality is managed by satisfying only a certain level of performance. This quality control behavior is undermining project managers' efforts to improve quality performance. Although the importance of quality control in construction management is constantly being discussed, it is still being sacrificed under the goal of shortening durations and reducing costs [5]. However, construction quality management should apply the same techniques to manage project duration and cost.  
This study aims to (1) explore the current researches and methods of measuring and quantifying quality performance indicators in construction projects, (2) present a method that allocates the quantitative quality performance scores of resources to the operation simulation modeling using discrete event simulation (DES), and (3) develop a decision-making support system to estimate and search optimal alternatives considering duration, cost, and quality performance of construction operations simultaneously.
# Emerging Topics in Joint Radio-Based Positioning, Sensing, and Communications
## 3 Emerging Topics in JPSAC Context
### Standardization Efforts  
Among the multiple JPSAC domains (see Figure 1), the research in the ISAC area has garnered the most attention from the standardization committees. For example, European Telecommunications Standards Institute (ETSI) has created an Industry Specification Group (ISG) for ISAC in November 2023, and this group is working actively to define use cases, develop advanced channel models, and propose Key Performance Indicators (KPIs)and evaluation methodologies for ISAC. ISAC standardization efforts can also be observed at the 3 GPP level, with recent documents addressing near-field channel models (e.g., 3 GPP TR 38.901 and 3 GPP TR 38.900 documents) or the integration of TN with NTN models (e.g., 3 GPP TR 38.821 document). A description of the latest progress in 3 GPP regarding ISAC channel modeling can be found in [70].