esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Design
### Architecture
The user node modifies the address of the data packet to the address of the next hop and forwards it to the underlying IP network. The local resource monitoring module measures the forwarding workload. Step 6: The data packet returns to the ICN ordinary node for routing and forwarding. During the data transmission process, it may pass through different local resolution domains. In each of these domains, user bandwidth resources can be utilized to assist in ICN network transmission. Step 7: At regular intervals, the blockchain application selects the accounting nodes based on the network contribution of the user nodes. Block rewards are distributed proportionally to the contribution level, with accounting nodes that exceed a certain threshold receiving additional economic incentives. Figure 3: Logical topology diagram of the integration of ICN and blockchain in an overlay network. The workflow can be summarized as shown in Figure 4. Compared to some application-layer overlay network methods, our approach fully utilizes the characteristics of ICN's locator-identifier separation. We implement relevant mechanisms at the network layer based on ID/NA cooperative routing, which refers to the ability to query new network addresses from NMRS based on specific IDs during packet routing and modify the destination address of the packets as needed. In our ICN architecture, the ID design is inspired by the flat ID design method in MobilityFirst [10], using 20 bytes to name various entities in the network. Additionally, the NMRS plays a crucial role as both the receiver device ID and UBSID query NA operations require the involvement of NMRS. Finally, we employ blockchain technology to provide a secure and transparent network resource sharing environment, encouraging users to contribute bandwidth resources and extend online time. Existing methods for utilizing user bandwidth resources are mostly implemented at the application layer, either a centralized management server needs to be introduced for node management and task scheduling, which will have scalability issues, or a decentralized approach is adopted, but the routing efficiency is low and node selection cannot be based on network status. Some network layer implementation methods are limited in application scenarios and belong to innovative network architectures, and the cost of device updates is too expensive, which does not meet the needs of future network smooth evolution. This paper proposes a method of using users' idle bandwidth under an IP-compatible ICN architecture and further proposes the introduction of blockchain technology to solve the incentive problem on the user side.
## 3 LCI System Design Framework
### Motion Artefacts and Fringe Washout  
For in situ profilometry, data measurement on the fly is necessary. However, the effect of sample motion can negatively impact on data integrity due to the signal being integrated over a period of time. This results in a reduction in SNR and image degradation [54]. The magnitude of motion artefacts and SNR reduction is controlled by the total axial or transverse displacement during a singular signal acquisition time. Hence, for a given sample motion, if the capture rate is increased or axial and transverse displacements are decreased, then the impact of motion artefacts will reduce.
## 3 Results and Discussion
### Sample Characterization
Cysteine-modified C@Fe\({}_{3}\)O\({}_{4}\) samples show lower specific surface areas and total pore volumes than C@Fe\({}_{3}\)O\({}_{4}\) due to grafting of the surface L-cysteine ligands, which results in blockage of surface channels and reduced porosity [52; 53]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & \(\mathbf{S_{\text{BET}}}\) **(m\({}^{2}\cdot\)g\({}^{-1}\))** & \(\mathbf{V_{\text{t}}}\) **(cm\({}^{3}\cdot\)g\({}^{-1}\))** & \(\mathbf{D_{\text{a}}}\) **(nm)** \\ \hline Uncoated Fe\({}_{3}\)O\({}_{4}\) & 55 & 0.07 & 2.28 \\ C@Fe\({}_{3}\)O\({}_{4}\) & 90 & 0.11 & 2.24 \\
250 Cys-C@Fe\({}_{3}\)O\({}_{4}\) & 30 & 0.04 & 2.44 \\
500 Cys-C@Fe\({}_{3}\)O\({}_{4}\) & 23 & 0.03 & 2.52 \\
1000 Cys-C@Fe\({}_{3}\)O\({}_{4}\) & 40 & 0.04 & 2.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Porous structural data for as-synthesized samples (\(\mathbf{S_{BET}}\), specific surface area by BET; \(\mathbf{V_{\text{t}}}\), total pore volume; \(\mathbf{D_{\text{a}}}\), average pore diameter).
_Article_  
Analysis on the Accuracy of Intraoral Scanners:  
The Effects of Mandibular Anterior Interdental Space  
[PERSON] 1, [PERSON] 2, [PERSON] 3, [PERSON] 4 and [PERSON] 5,* (c)  
Graduate School of Clinical Dentistry, Ewha Womans University, Seoul 07985, Korea; [EMAIL_ADDRESS]  
Harvard School of Dental Medicine, Harvard University, Boston, MA 02115, USA; [EMAIL_ADDRESS]  
Department of Orthodontics, Ewha Womans University, Seoul 07985, Korea; [EMAIL_ADDRESS]  
Department of Prosthodonics and Dental Research Institute, Seoul National University Gwanak Dental Hospital, Seoul 08826, Korea; [EMAIL_ADDRESS]  
Department of Orthodontics, School of Medicine, Ewha Womans University Mokdong Hospital, 1071 Anyangcheon-ro, Yang Cheon-Gu, Seoul 07985, Korea  
Correspondence: [EMAIL_ADDRESS]; Tel.: +82-2-2650-5112; Fax: +82-2-2650-5764  
Academic Editors: [PERSON], [PERSON] and [PERSON] Received: 20 June 2017; Accepted: 10 July 2017; Published: 13 July 2017  
In this study, we evaluated the effects of mandibular anterior interdental space on the accuracy of intraoral scanners. Four models of mandibular arch with varying distances of anterior interdental space were analyzed; incisors were evenly spaced out between the two canines by 0 mm, 1 mm, 3 mm, and 5 mm. The full arch of each model was scanned 10 times with iTero(r) and Trios(r). The images were superimposed with those from the reference scanner (Sensable S3) and compared using Geomagic Verify. Statistical analysis was conducted using a \(t\)-test, paired \(t\)-test, and one-way analysis of variance (ANOVA). Differences in the accuracy of images were statistically significant according to both iTero(r) and Trios(r); a greater deviation was noted with increasing anterior interdental space (\(p<0.05\)). Upon dividing the lower arch into five sections, larger deviation was observed with iTero(r) in the molar area, except in the model with 1 mm space. With Trios(r), the largest deviation was observed in the right molar area in all models. The maximum deviations of interdular width were 0.66 mm and 0.76 mm in iTero(r) and Trios(r), respectively. This comparison suggests that Trios(r) demonstrates a higher accuracy than iTero(r) in all models and in all sections of the arch. However, the mean deviations indicate that both iTero(r) and Trios(r) are clinically acceptable.  
intraoral scanner; scanning mandibular arch; interdental space; accuracy
# An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks
###### Abstract  
In opportunistic networks, the requirement of QoS (quality of service) poses several major challenges to wireless mobile devices with limited cache and energy. This implies that energy and cache space are two significant cornerstones for the structure of a routing algorithm. However, most routing algorithms tackle the issue of limited network resources from the perspective of a deterministic approach, which lacks an adaptive data transmission mechanism. Meanwhile, these methods show a relatively low scalability because they are probably built up based on some special scenarios rather than general ones. To alleviate the problems, this paper proposes an adaptive delay-tolerant routing algorithm (DTCM) utilizing curve-trapezoid Mamdani fuzzy inference system (CMFI) for opportunistic social networks. DTCM evaluates both the remaining energy level and the remaining cache level of relay nodes (two-factor) in opportunistic networks and makes reasonable decisions on data transmission through CMFI. Different from the traditional fuzzy inference system, CMFI determines three levels of membership functions through the trichotomy law and evaluates the fuzzy mapping from two-factor fuzzy input to data transmission by curve-trapezoid membership functions. Our experimental results show that within the error interval of 0.05-0.1, DTCM improves delivery ratio by about 20% and decreases end-to-end delay by approximate 25% as compared with Epidemic, and the network overhead from DTCM is in the middle horizon.  
delay tolerant network; opportunistic social networks; routing algorithm; fuzzy inference system; delivery ratio; end-to-end delay Article
# Bioelectrical Impedance Vector Analysis (BIVA) and Somatotype in Female Rugby Players
## 1 Introduction
Vector displacements parallel to the major axis of tolerance ellipses indicate changes in hydration status, dehydration with long vectors, and hyperhydration with short vectors, while vector displacements parallel to the short axis indicate changes in body cell mass and in the relationship between extracellular water (ECW)/intracellular water (ICW) [18; 19]. The classic BIVA does not make assumptions about body compartments, body mass, geometry, or hydration, nor is it subject to the error sources of other modalities of BIA. Recently, a new variation of BIVA has been developed that adjusts the individual values of R and Xc for cross-sectional areas to reduce the effect of body dimensions: the specific BIVA (BIVA-sp) [20]. In BIVA-sp, vector displacements parallel to the major axis of tolerance ellipses indicate changes in FM% (higher FM% with larger vectors); the interpretation of vector displacements parallel to the short axis (changes in PA) is the same as in the classic BIVA [21]. This method has demonstrated higher precision in FM% estimation and the ECW/ICW relationship than the classic BIVA in adults [20] and elderly adults [22]. Many studies on the application of different BIA modalities to monitor BC and hydration in sports have recently been published [23; 24; 25]. Given that the bioelectrical behaviour of athletes differs from the general population, specific models for BC estimation and reference tolerance ellipses have been created for them. Moreover, depending on the sport, differences in BIVA patterns between athletes have been found, which some authors suggest could be explained by the differences in their somatotype [26]. Considering that rugby players present important differences in BC according to player position [1], the BIVA (both classic and specific) could be useful for the monitoringof BC. Although BIA has proven to be a useful and fast technique, anthropometry is still the most used method for BC studies due to its affordability. Therefore, the aim of this study was to analyse different models, based on both anthropometry and BIA, for the study of BC in a female semi-professional rugby team of the Spanish National Rugby League. Players' somatotypes and positions in the BIVA graph were also compared.
## 5 Evaluation
### Recommendation Performance Results  
The feasibility of fixing algorithm parameters, namely C, the number of tag suggestions generated and, in the case of the NCP model, N, was investigated with the intention of simplifying the subsequent tests. Furthermore, this allowed the effect of these parameters to be ascertained experimentally. To limit the computation time involved, a smaller subset of 40 start tags was randomly selected and used during these tests. Other parameters, such as the method of combining tags were held constant.  
Figure 11: MAP values for different C with the SCO model with a fixed \(|\)N\(|\) = 50.  
Figure 12: Effect of N on MAP in the NCP model with different values of C.  
Figure 11 presents the MAP values of the SCO model for a selection of values of C between 1 and 40 with a fixed \(|N|=50\). Broadly speaking, as C increases, MAP decreases. Larger values of C, however, indicate that MAP can be expected to stabilize. Figure 12 shows the MAP of the NCP model under influence of different values for C and N. From the results shown there we can conclude:  
* All variants of the NCP model perform well with a size of \(N\) between 40 and 60. As larger \(N\) results in increased computation effort, \(|N|\) should be chosen to be smaller when larger values offer little increase in performance. As a result, \(|N|\) was set to 50 for the rest of the evaluation.
* The shape of the MAP curve is essentially the same regardless of C. This uniformity leads to the hypothesis that C and N can be set independently.  
The degradation in performance resulting from increasing C is progressively smaller as C increases. MAP appears to stabilize for values of C over 15.  
Based on the optimal parameters \(C\) and \(|N|\) determined from the tests we aim to test the relative performance of the NCP model against the base line SCO model in terms of mean average precision. Figure 13 shows the MAP values of the NCP and the SCO approach. The NCP model performs better than the SCO. However, the algorithms tested delivered MAP values within 3% of one another meaning that improvement offered is marginal.
# A Reliability Scheduling Algorithm for the Static Segment of FlexRay on Vehicle Networks +
## 2 Proposed Methodology
### Message Reliability Processing Module  
The FlexRay protocol is widely used for in-vehicle communication; however, it has no mechanism to prevent transient faults in its application layer. Therefore, improved communication reliability for FlexRay is important. The function of the message reliability processing module is to ensure reliability by computing the number of times a message delivery is duplicated for a static message in FlexRay. The flowchart of the message reliability processing module is shown in Figure 3. We attempt to increase the reliability of messages and then denote them with the following parameters:  
* \(GS_{mi}\): This estimates the probability of success (reliability) for a message (\(M_{i}\)) in transmission, as in Equation (5), which is equal to that in [8]. \(M_{i}\) consists of four parameters; in addition, it has a probability of failure and re-transmitted time (\(RT_{i}\)). The parameter is used to improve the probability of success by re-transmission;
* Global success reliability (\(GS\)): This denotes the probability that all the messages (\(M\)) can be successfully transmitted at least once in the testing time (\(\tau\)). If the reliability of message (\(M\)) which is computed in this module is bigger than or equal to the system reliability goal, the system is reliable in transmission and sends all messages to the scheduling output module. \(M\) consists of each message (\(M_{i}\)); therefore, \(GS\) is obtained by multiplying each \(GS_{mi}\), as in Equation (5): \[GS=\prod_{i=1}^{n}GS_{mi}\ =\ \prod_{i=1}^{n}(1-PF_{i}^{RT_{i}+1})^{\frac{T}{t_{i}}}\] (5)
* Single message reliability goal (\(SR_{mi}\)): This parameter denotes the reliability goal of each message. \(SR_{mi}\) must be bigger than or equal to the system reliability goal (\(SR\)). According to Equation (5), \(GS\) is obtained by multiplying each \(GS_{mi}\): in fact, \(GS\) does not meet \(SR\) when the reliability goal of each message (\(SR_{mi}\)) is equal to \(SR\). This is because each \(GS_{mi}\) is less than 1 so that the multiplication result (\(GS\)) will fail to meet the system reliability goal.  
A communication system is usually subject to reliability and deadline constraints (\(D_{i}\)). The estimation of the failure probability of message transmission is critical for the scheduling algorithm. In this module, first, each message (\(M_{i}\)) is sorted by \(GS_{mi}\) in descending order. Sorting is a fast and effective method to reduce the procedure of the re-computed \(SR_{mi}\) and \(RT_{i}\). For example, the order is \(GS_{1}\) > \(GS_{4}\) > \(GS_{3}\) > \(GS_{2}\) after sorting, if \(GS_{1}\)\(\times\)\(GS_{4}\) > \(SR\), and then these are multiplied by \(GS_{3}\). \(SR_{mi}\) and \(RT_{i}\) must be re-computed when the multiplication result fails to meet \(SR\). The proposed method sorts each message (\(M_{i}\)) and then excludes the messages that do not need to retransmitted to further improve slot utilization. How to obtain \(SR_{mi}\) is described in the next section.
## 2 Materials and Methods
### Personalized Chemotherapy
#### 2.6.1 Target Steady State  
The steady states of the personalized model must be analyzed to find an appropriate target state for the therapy. For \(t\to\infty\) and \(v(t)=0\), the state variables will approach their steady values \(N_{0},\ L_{0},\ T_{0}\). Given the fact that we are dealing with a nonlinear system, it is typical to have multiple steady states, each with a specific region of attraction. Convergence of the states to one of the stable steady states depends on the initial state \(x(0)\). Determining the steady states is crucial for the chemotherapy dosing design as we need to specify the target (reference) state representing the cancer in remission. The steady states can be determined as the solutions of a system of algebraic equations resulting from (1) under the assumption of \(\frac{\text{d}N}{\text{d}t}=\frac{\text{d}L}{\text{d}t}=\frac{\text{d}T}{ \text{d}t}=0\).  
Another important aspect is that since the chemotherapy dosing will terminate after completing the treatment protocol, it is necessary to ensure that the state variables remain stable after reaching the vicinity of the target steady state. Therefore, satisfying the criterion of local stability for the chosen target steady state is necessary for the success of therapy and inducing remission. Otherwise, the cancer would relapse, and the tumor would regrow. By studying the Jacobian matrix, we can determine the local stability of a steady state based on its eigenvalues. If all eigenvalues have negative real parts, the equilibrium point is locally stable.  
The Jacobian matrix \(A(x,\mathbf{\theta})\in\mathbb{R}^{4\times 4}\) is given by  
\[A(x,\mathbf{\theta})=\begin{pmatrix}a(1-2 bN)-\alpha_{1}T-k_{N}u&0&-\alpha_{1}N&-k_ {N}N\\ rT&-(\mu+\beta_{1}T+k_{L}u)&rN-\beta_{1}L&-k_{L}L\\ -\alpha_{2}T&-\beta_{2}T&c(1-2 dT)-\alpha_{2}N-\beta_{2}L-k_{T}u&-k_{T}T\\ 0&0&0&-\omega\end{pmatrix}. \tag{33}\]  
There are four different clinical scenarios for the steady states. The first is the Dead state represented by all state variables being zero, which means that the tumor is gone, but the immune system is also depleted. This state is undesired from the clinical perspective. The ideal condition is the Cured state having zero tumor cells and enough natural killer cells. However, this state is typically unstable and hence cannot be maintained. The worst scenario is the Grown state manifesting by a large number of tumor cells, representing the final stage of cancer. Therefore, the so-called Coexisting state seems to be the best option as it corresponds to cancer in remission having a small number of \(T\) cells and enough \(N\) cells. If one of the Coexisting states satisfies the local stability criterion, it will be chosen as the target state for the treatment.
## 4 Results and Discussion
### Fade Analysis of the Pigment Color Blocks
From Figures 10d and 12d, an increase in hue (h) from 29 to 34 was observed, while the chroma (C) decreased from 42.5 to 38. Some dark cinnabar was produced, and gelatin decomposition occurred; however, the overall degree of color change was not evident. 5. Azurite and malachite: Both pigments are derivatives of Cu ions, with the molecular formulas Cu(OH)\({}_{2}\)(CO)\({}_{3}\))\({}_{2}\) for azurite and Cu\({}_{2}\)CO\({}_{3}\)(OH)\({}_{2}\) for malachite. Azurite is a companion mineral that forms together with malachite, surrounding the inner malachite, and they have similar chemical compositions [21]. From Figure 10, the hue (h) of azurite insignificantly decreased with a value of 2.1, and the hue (h) of malachite increased to a value of 15.3. From Figure 11, the overall lightness of malachite and azurite was more stable and less affected by external influences. In Figure 12e,f, there was a slight decrease in chroma (C) for both azurite and malachite, with a decrease of 3 and 6.7, respectively. These pigments reflected more to the blue and green wavelengths. In addition, the experimental light source had low yellow and red content, resulting in lower energies of medium and long wavelengths. Thus, the overall color change in the cyan pigment was small after absorbing a large amount of low-energy radiation. However, the hue (h) of malachite tended to be closer to cyan under prolonged light irradiation. The shortwave radiation was absorbed more by the green pigment than by the cyan pigment, resulting in a relatively larger change in hue (h). In summary, red and yellow pigments were prone to chemical reactions and physical structure changes due to prolonged light irradiation, resulting in much more complex color appearance changes and larger data fluctuations compared to the cyan pigments. Figure 15a shows the average color difference \(\Delta E_{00}\) comparison of all models across the entire time series. In terms of overall color difference comparison, especially for light-sensitive pigments, the GWOAD-LSTM model had better fitting long-term trends and more accurate data capture compared to the BPNN, LSTM, and GWO-LSTM models. As shown in Figure 15b, the GWOAD-LSTM model had the smallest color difference and outperformed the previous three models in terms of color gamut prediction, indicating that our proposed model could effectively handle short-term data fluctuations and had higher prediction accuracy. At the same time, there were many factors, such as noise, in the original data, which caused the GWOAD-LSTM composite model to predict the effect of pigment fading with a slight error from the real value, but the overall prediction accuracy was better. Therefore, our proposed GWOAD-LSTM model was feasible in the field of practical pigment fading simulation prediction and rare painting restoration.
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 4 Experimental Results and Discussion
### Ablation Study  
To assess the contribution of each module in our proposed method, we perform ablation studies on the RSOD and NWPU VHR-10 datasets. The corresponding results are summarized in Table 4.
# An Interactive Real-Time Locating System Based on Bluetooth Low-Energy Beacon Network +
## 4 Implementation and Experiments
### Discussions
#### 4.5.2 Contention on Advertising Channels and Interference from Wi-Fi  
We know that Bluetooth beacons are broadcast on the three advertising channels. Since the message relays in the Bluetooth mesh networks are also based on broadcasting, when the number of Mesh Beacons is large, the contention of using the advertising channels may become an important issue. A good solution to this problem is the latest Bluetooth 5.0 [36]. In Bluetooth 5.0, a new physical layer named LE 2M is introduced (note that the physical layer used in Bluetooth 4 is called LE 1M). With LE 2M, its theoretical data rate of 2 Mbit/s can significantly reduce the air time needed to transmit a given amount of data, leading to better spectral efficiency. Moreover, in Bluetooth 5.0 the payload of the advertising packets can be offloaded to the available data channels, leaving only the header data transmitted on the advertising channels. On the problem of possible interference from Wi-Fi communications, Bluetooth 5.0 features an improved channel sequencing algorithm that improves the pseudo randomness of the next hop channel sequencing. This algorithm also improves the co-existence performance of Bluetooth devices in the presence of Wi-Fi devices. When the commercial Bluetooth 5.0 chips are widely available and have been used in beacon systems, we believe that the locating performance of such systems can be greatly enhanced.