esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Processing of \(k\)-Hop Reachability Queries on Directed Graphs
## 5 Conclusions  
One of the fundamental operations in the directed graphs is the processing of kHRQs. The existing approaches rely on the index covering part of the reachability information, which necessitates performing costly graph traversal operations and which may be inefficient for answering kHRQs. To address this issue, we propose to construct a compact two-hop distance label based on a small set of vertices, allowing for efficient kHRQ answering without performing graph traversal. Specifically, we propose a linear-time algorithm for quickly generating a smaller vertex cover based on which we generate two-hop distance labels. Furthermore, we propose a degree-constraint two-hop distance label that can bind the query performance with a given threshold, d, in the worst case. We conduct a comprehensive experimental evaluation and compare our approach to the state-of-the-art methods _ESTI_ and _kReach_. Our results show that our approach is over 1000 times faster than _ESTI_ and _kReach_ for the BerkStan dataset.  
For future work, we plan to design a more efficient algorithm for index construction to obtain a smaller index size compared with VCRea and _VCRea_+. Here, the key problems that need to be solved include (1) how to design a better algorithm to find a smaller cover set and (2) how to control the value of \(d\) so that our approach can be adaptive to different graphs.  
Conceptualization, X.T. and J.Z.; methodology, X.T. and K.L.; software, K.L.; validation, J.Z. and Y.S.; formal analysis, J.Z.; investigation, X.T.; resources, X.T.; data curation, K.L.; writing--original draft preparation, X.T. and J.Z.; writing--review and editing, X.T. and X.L.; funding acquisition, X.T. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by grants from the Natural Science Foundation of Shanghai (No. 20 ZR1402700) and from the Natural Science Foundation of China (No.: 61472339, 61873337).  
Not applicable.  
**Data Availability Statement:** All datasets used in this study are publicly available and are discussed in Section 4. They are also available from the corresponding author.  
The authors declare no conflict of interest.  
Figure 5: Comparison of index size and index construction time, where (**a**) shows comparison of index size (MB) and where (**b**) shows that of index construction time (ms).
## 3 Results and Discussions
### Anaerobic Digestion Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Substrate**} & \multicolumn{4}{c}{**Exponential (First Order) Model**} & \multicolumn{4}{c}{**Cone Model**} & \multicolumn{4}{c}{**Modified Gompertz Model**} \\ \cline{2-13}  & \(\mathbf{B_{\max}}\) & \(\mathbf{k}\) & \(\mathbf{\lambda}\) & **RMSE** & \(\mathbf{\mathrm{R^{2}}}\) & \(\mathbf{B_{\max}}\) & \(\mathbf{k}\) & \(n\) & **RMSE** & \(\mathbf{\mathrm{R^{2}}}\) & \(\mathbf{B_{max}}\) & \(\mathbf{R_{m}}\) & \(\mathbf{\lambda}\) & **RMSE** & \(\mathbf{\mathrm{R^{2}}}\) \\ \hline WHDS & 67.1 & 0.248 & 0.132 & 2.747 & 0.982 & 70.94 & 0.324 & 1.535 & 2.784 & 0.982 & 65.1 & 10.35 & 0.0 & 3.33 & 0.974 \\ only & & & & & & & & & & & & & & \\ Mix-1 & 148.3 & 0.448 & 0.0 & 2.017 & 0.997 & 153.5 & 0.648 & 1.530 & 2.791 & 0.995 & 146.6 & 42.13 & 0.0 & 4.96 & 0.984 \\ Mix-2 & 176.2 & 0.337 & 0.0 & 4.100 & 0.993 & 190.7 & 0.532 & 1.227 & 3.568 & 0.995 & 173.0 & 42.50 & 0.0 & 8.23 & 0.967 \\ Mix-3 & 166.1 & 0.355 & 0.0 & 8.508 & 0.962 & 206.0 & 0.417 & 0.798 & 5.417 & 0.985 & 162.6 & 37.77 & 0.0 & 12.99 & 0.912 \\ Mix-4 & 273.0 & 0.207 & 0.0 & 10.03 & 0.983 & 341.5 & 0.215 & 0.990 & 10.88 & 0.981 & 262.7 & 36.20 & 0.0 & 14.72 & 0.962 \\ SY only & 172.7 & 0.258 & 0.0 & 5.967 & 0.985 & 202.9 & 0.311 & 1.046 & 6.613 & 0.980 & 168.2 & 27.99 & 0.0 & 9.47 & 0.962 \\ \hline \hline \end{tabular}  
* \(B_{\max}\) is the maximum possible cumulative biomethane potential produced after an infinite number of days, in mL/gVS\({}_{\rm added}\), \(k\) is the biomethane production rate constant in d\({}^{-1}\), \(\lambda\) and \(n\) represent the initial delay period in the degradation process in days and the shape factor respectively, RMSE represents the root mean square errors and R\({}^{2}\) represents the coefficient of determination.  
\end{table} 
Table 4: Values of the parameters of the biomethane kinetic models (Equations (3)–(5)). Figure 8: Fitted kinetic model curves for cumulative biomethane potentials from the anaerobic digestion of different substrates; **(A)**: Variation of biomethane potentials of the degradation of the substrate of WHIDS (wet hydrolyzed DAF sludge) only with time; **(B)**: Variation of biomethane potentials of the degradation of the substrate of Mix-1 with time; **(C)**: Variation of biomethane potentials of the degradation of the substrate of Mix-3 with time; **(D)**: Variation of biomethane potentials of the degradation of the substrate of Mix-3 with time; **(E)**: Variation of biomethane potentials of the degradation of the substrate of Mix-4 with time; **(F)**: Variation of biomethane potentials of the degradation of the substrate of stockyard ($Y$) only with time.
## 4 Kernel Density Estimation Probability Density Function  
Kernel density estimation does not require any assumptions about the prior distribution of random variables but only necessitates determining the input variables, kernel functions, and the optimal window width [23]. By employing the kernel density estimation method, a continuous probability density curve can be obtained based on the predictedtraffic demand for the future en route sector. The fundamental idea of kernel density estimation is to estimate a reasonable density function through this method. The kernel density estimator is shown in Formula (8).  
\[\hat{f}(x)=\frac{1}{nh}\sum\limits_{i=1}^{n}k\left(\frac{X_{i}-x_{0}}{h}\right)= \frac{1}{n}\sum\limits_{i=1}^{n}k_{h}(X_{i}-x_{0}) \tag{8}\]  
In Formula (8), \(k()\) is a kernel function, \(k_{h}=k(x/h)/h\).  
Compared with the density estimation method using the Gaussian kernel function, the Epanechnikov function also exhibits a bell-shaped profile, which significantly reduces the computational workload and enhances operational speed. The Epanechnikov kernel function is optimal in terms of mean square error with minimal efficiency loss. In this paper, the Epanechnikov kernel function is selected, and the optimal window width is determined through cross-validation during kernel density estimation. The form of the Epanechnikov kernel function is shown in Formula (9):  
\[k(x)=\frac{3}{4}\left(1-x^{2}\right)I(|x|\leq 1) \tag{9}\]  
In Formula (9), \(I()\) is an indicative function. When the condition in brackets is true, the value of \(I()\) is 1, and when the condition is false, the value of \(I()\) is 0. Cross-validation function expression is shown in Formula (10).  
\[CV_{f}(h)=\frac{1}{nh^{2}\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}\overline{ k}(X_{i}-X_{j})}-\frac{2}{n(n-1)}\sum\limits_{i=1}^{n}\sum\limits_{j\
eq i}^{n}k_{h} (X_{i}-X_{j}) \tag{10}\]  
In Formula (10), \(\overline{k}(v)=\int k(u)k(v-u)du\) is a convolution kernel function derived from \(k()\). As long as the concrete form of \(k()\) is given, the concrete expression of \(\overline{k}(v)\) can be obtained. In this way, the probability density function and the probability density curve of the future en route sector traffic demand can be realized by the above method.
# Quality of Experience in 6G Networks: Outlook and Challenges
## 5 Quality of Experience in 6G Networks
### Relevant Considerations  
In the context of networks, it is typical that security is discussed with respect to the data communicated and stored. In several projected 6G services, mostly where experience depends on a form of sensory immersion, the network's and service's access to the user data is considerably expanded. Measures for managing the secure and privatized handling of this data will be necessary and will need to be made in a way that will not impact the user experience.  
The immersion aspect of the services further entails the consideration of user safety, both physically and mentally. Cyberphysical or tactile interaction implies physical involvement in the experience, and the notion of safe robotics or safe AI would need to have matured by then. Meanwhile, consideration for the mental state of the users becomes an added requirement.  
Specific steps are already taken towards these objectives. The IEEE P7000 effort, for example, seeks to investigate the means of ethical design, especially where AI is considered. Other examples include the subtle interfaces in Instagram's \"You're all caught up\" function and Apple's \"screen time\", both of which show an increasing awareness of technology's addictive affect.
## 1 Introduction
For example, to avoid long propagation delays, vector based routing schemes consider node's proximity information between the sender and the sink node, and nearness to the pipeline center in the holding time. It is projected that the packet forwarding through these nodes reduces the end-to-end delay. However, all communication through these nodes will deplete their energy and result in a void energy hole problem. Therefore, the energy fairness should be achieved among all the nodes within the vector as well as in the network. Hence, the energy factor must be considered in the holding time computation to increase network lifetime. Nevertheless, the nodes with sufficient energy do not guarantee the shortest path (with small end-to-end delay) between sender and sink. On the other hand, better forwarding decisions or precise holding time estimation can be attained if an updated network state (complete or partial network) information is available at each node in the network. This network state information availability is possible through the exchange of control packets, which again impacts the bandwidth, energy, and inflates the error rate. Hence, any forwarding scheme for UASN must consider the constraints and provide the mediated solution. Additionally, the difference between holding times estimated by all the immediate neighbors should be larger than the propagation delay between them to properly suppress the unnecessary packet forwarding. Otherwise, many copies of the same packet will be forwarded in the network. It is a well established fact that the acoustic signal consumes more energy and experiences a very long propagation delay and channel error in the underwater environment [10]. The propagation delay and energy consumption increases drastically if the farthest acoustic node in the network needs to communicate data towards the sink that placed at the fixed location. In order to efficiently collect data, different schemes in literature adopt sink mobility. Mobile sink, also called mobile station, can be any node that moves in the aquatic environment either autonomously, e.g., autonomous underwater vehicle (AUV) [11], over the anchored rope, vessel, etc. The mobile sink is considered to have sufficient available resources to roam in the network (frequent refueling and/or recharging). Hence, any routing scheme proposed for the UASN must also be analyzed in the mobile sink UASN scenario to verify its effectiveness. The organization of the remaining manuscript is as follows: Previous work related to the proposed ESEVBF is reviewed in Section 2. In Section 3, holding time and the working principle of ESEVBF is described. Simulation analysis in terms of energy consumption, end-to-end delay, the number of copies of data packets, packet delivery ratio, and average hop count, in the underwater network without and with sink mobility, is performed in Section 4. Finally, the discussion is concluded in Section 5.
## 3 Results
### Comparison of PV Potential in Individual Sections
#### 3.2.1 Comparison of PV Potential of the Four Modules in Same Section  
Figure 8 shows the evaluation results of the PV potential of modules based on the travel time in section 4, which is a flat road. The number in the yellow circle is the section number and the arrow indicates the direction of vehicle motion. The numbers in orange circles indicate the positions of the sun at 9:00, 12:00, and 15:00, which aids in visually understanding the angle of sun incidence to the module attached to the vehicle by showing the position of the sun over time. Considering the hourly position of the sun in the study area, the position of the graph is shown on the right, lower right, and lower left.  
At 9:00, the potential solar power output for each module is in the order C, A, B, and D. Module C, which is attached to the left side of the vehicle, faces southeast toward the sun, and at 9:00 a.m., the sun is at a low altitude; therefore, the sunlight enters the module almost vertically. Therefore, the PV potential of module C was the maximum. The inclination of module A was 0\({}^{\circ}\), and the angle of incidence was small owing to the sun's low altitude. As module B has an inclination of 30\({}^{\circ}\) in the northeast direction, its light receiving efficiency is less than that of module A. As module D faced northwest, opposite to the direction of the sun, its PV potential was the minimum.  
Contrastingly, because the sun is located in the southwest direction at 15:00, the PV potentials of the remaining three modules, except module A, which is unaffected by the azimuth, exhibit a large difference. For module B attached to the rear wind shield of  
Figure 8: Comparison of PV potentials of four modules at different time in section 4 (Korean labels in the map indicate the building name).  
the vehicle, the PV potential was approximately zero because the module's azimuth and direction of sunlight were the same. Additionally, at 15:00, solar rays are directed to the northeast, whereas module C is directed to the southeast; therefore, its PV potential is considerably lower than that at 9:00.  
The altitude and insolation of the sun are highest at 12:00. Therefore, the angle of incidence of the sun on module A is almost vertical, and its PV potential is the maximum. Additionally, the PV potential of the remaining modules were in the order C, B, and D. Herein, the azimuths of each module are southeast, northeast, and northwest. While module C is not in a vertical relationship, the incident angle of sunlight is closer to the vertical; hence, it exhibits the second-highest power potential.
# Availability of Primary Closure for Resection of Oral Cavity Cancer
## 3 Results
### Factors Associated with Primary Closure in Stage I and II Patients  
Among the 85 patients, a total of 44 patients had stages I and II. For these patients, the case of flap reconstruction and the case of primary closure were compared and analyzed.(Table 5) Mann-Whitney test and chi-square test were analyzed, and the results showed a statistically significant difference in the operation time and hospitalization period. Tables 6 and 7 show the results of three-way ANOVA for operation time and Period of hospitalization, respectively. Interactions were verified for the presence or absence of neck dissection, reconstruction method (flap or primary closure), and stage. For each result, only the reconstruction method had a statistically significant effect on the operation time and hospitalization period.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Df** & **SS** & **MS** & **F** & \(\mathbf{p}\) \\ \hline Neck Dissection (ND) & 1 & 87.21 & 87.21 & 1.2704 & 0.267153 \\ Reconstruction Method (RM) & 1 & 741.7 & 741.7 & 10.8039 & 0.002266 * \\ Stage & 1 & 13.92 & 13.92 & 0.2027 & 0.655215 \\ ND\(\times\)RM & 1 & 10.34 & 10.34 & 0.1506 & 0.700227 \\ ND\(\times\)Stage & 1 & 4.89 & 4.89 & 0.0712 & 0.791087 \\ RM\(\times\)Stage & 1 & 0.09 & 0.09 & 0.0013 & 0.971476 \\ ND\(\times\)RM\(\times\)Stage & 1 & 0.03 & 0.03 & 0.0004 & 0.984249 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Three-way ANOVA of Table 5; dependent variable: operation time.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Clinical Characteristic** & **Primary Closure** & **Flap** & \(\mathbf{p}\)**Value** \\ \hline Gender (\%) & & & \(p\) = 0.265 \\ Male & 5 & 15 & \\ Female & 11 & 13 & \\ Neck dissection & & & \(p\) = 0.489 \\ Surgery with SND & 8 & 20 & \\ Surgery with mRND & 1 & 0 & \\ Surgery without neck dissection & 7 & 8 & \\ Stage & & & \(p\) = 0.052 \\ I & 13 & 13 & \\ II & 3 & 15 & \\ Operation time (h) & 4.02 & 8.13 & \(p\) = 0.00006 * \\ Lesion site & & & \(p\) = 0.076 \\ Upper gingiva & 2 & 4 & \\ Lower gingiva & 3 & 8 & \\ Tongue & 5 & 5 & \\ Buccal mucosa & 1 & 4 & \\ Floor of the mouth & 2 & 3 & \\ Palate & 3 & 4 & \\ Period of hospitalization & 13.4 & 22.3 & \(p\) = 0.001 * \\ \hline \hline \end{tabular}
\end{table}
Table 5: Clinical characteristics of stage I, II Patients—primary closure vs. flap.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Df** & **SS** & **MS** & **F** & \(\mathbf{p}\) \\ \hline Neck Dissection (ND) & 1 & 472,644,184 & 472,644,184 & 3.6333 & 0.0646435 \\ Reconstruction Method (RM) & 1 & 2,331,294,720 & 2,331,294,720 & 17.921 & 0.0001518 * \\ Stage & 1 & 110,204,244 & 110,204,244 & 0.8472 & 0.3634855 \\ ND\(\times\)RM & 1 & 103,550,227 & 103,550,227 & 0.796 & 0.3782159 \\ ND\(\times\)Stage & 1 & 7,604,504 & 7,604,504 & 0.0585 & 0.8103238 \\ RM\(\times\)Stage & 1 & 29,660,224 & 29,660,224 & 0.228 & 0.6358957 \\ ND\(\times\)RM\(\times\)Stage & 1 & 2,208,208 & 2,208,208 & 0.017 & 0.8970644 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Three-way ANOVA of Table 6; dependent variable: Period of hospitalization.
# Soft Iterative Decoding Algorithms for Rateless Codes in Satellite Systems
## 3 Efficient Soft Iterative Decoding Algorithms for HARQ with Rateless Codes in Satellite Systems
### HARQ with Cross-Layer Rateless Codes  
#### 3.3.1 Operational Principle  
Figure 5 shows a block diagram of the satellite system using a cross-layer code using rateless codes. At the transmitter, the encoding process at the MAC layer is performed by the LT encoder, and after the fragmentation, PHY layer encoding with the LDPC encoder and modulation processes are cascaded sequentially. The binary information, **I**, with a size of \(k\) is encoded by the systematic LT  
Figure 4: The flow chart of the parallel soft iterative decoding algorithm for the proposed rateless LDPC used for the HARQ scheme.  
code in the MAC layer, yielding an output bit stream \(\mathbf{u^{\prime}}\) with an arbitrary size of \(n\). To generate parity words of the LT code, we use the following polynomial degree distribution [17].  
\[\begin{array}{rcl}\Omega(x)&=&0.007969x+0.493572x^{2}+0.166220x^{3}+0.072464x^{ 4}+0.082558x^{5}\\ &&+0.056058x^{8}+0.037229x^{9}+0.055590x^{19}+0.025023x^{65}+0.003135x^{66}. \end{array} \tag{19}\]  
In the fragmentation module, \(\mathbf{u^{\prime}}\) is divided into several packets each of length \(K\); each packet represented by \(\mathbf{u}\) is encoded by an LDPC encoding module, yielding an LDPC codeword \(\mathbf{c}\) of length \(N\) which is modulated and transmitted through the channel.  
At the receiver, the LDPC decoder starts decoding once it receives soft information from the demodulator, and passes the final estimation to the MAC layer, whether or not the parity check equation is satisfied. If a sufficient number of LDPC decoded packets are collected by the LT decoder in the MAC layer, then the LT decoder is activated. If the decoding is successfully completed, then the ACK signal will be sent to the transmitter. Otherwise, the collection of the LDPC decoded packets will continue for the next decoding attempt. In a typical tandem way of decoding, final estimation on the information is made from the hard or soft decoding of the LT code in the MAC layer.
## 2 Results and Discussion
### Classification of Cretan VOO Samples
The aroma profile of the monovarietal Koroneiki VOOs examined was mainly characterized by the presence of C6 LOX alcohols, aldehydes, and esters as well as the contribution of C5 LOX compounds similarly to literature reports for VOOs of the same cultivar [6; 50]. VOCs concentrations in the monovarietal Koroneiki VOOs of the current study varied significantly either as total content, or as total group content, or even as content of most individual VOCs. Differences were considered significant even within the same farming type (organic or conventional) or production region, revealing the complexity of the VOO aroma profile. The latter is in accordance with reported findings on variable profiles expected due to the effect and synergism of various biotic and abiotic parameters, such as agricultural practices, altitude, harvest time, and conditions employed from harvest-to-oil production and storage [51]. Nonetheless, VOCs identified in the current study as positive contributors (e.g., (\(2E\))-hexenal, hexanal, hexan-1-ol, 1-penten-3-one, hexyl acetate, and hexenyl acetate), in terms of amount and/or importance of contribution to overall profile (relevant to OTV), have been also recognized as such for monovarietal Koroneiki VOOs studied [6; 50]. Moreover, terpenic compounds useful indicators for geographical and botanical VOO differentiation have been reported to be present in the aroma profile of Koroneiki VOOs [6; 50], which is in line with current findings; limonene, copaene, and a farnesene were present in all monovarietal Koroneiki VOO samples studied. The application potential of the method could be improved by analysing a larger dataset of EVOO samples and/or samples from oncoming harvest years from the same areas. We are planning to expand our data set in this direction, analysing samples produced in subsequent harvest years. In addition, we plan to use this technology to compare Cretan vs. non-Cretan VOOs, a comparison that could be of legislating and commercial significance.
## 3 Methodology
### Performance Metric
The score \(S_{2}\) is obtained for each interval of anomalies from equation  
\[S_{2}=\frac{1-\exp\left(k_{3}\frac{R_{f}-A_{t}}{A_{t}-A_{s}}\right)}{1+\exp \left(k_{3}\frac{R_{f}-A_{t}}{A_{t}-A_{s}}\right)} \tag{2}\]  
where the indices \(A_{s}\) and \(A_{t}\) translate, respectively, the first and last index of the anomaly window, \(R_{f}\) is the first reading detected inside the window, and \(k_{3}\) is a parameter that was set to 10 in this case study. \(S_{2}\) scores the performance in classifying the anomalies, the speed of detection, and false negatives. The equation was adjusted to start the penalty depending on the extension of the anomaly and to decrease more sharply in the middle of it. The score \(S_{2}\) is equal to 0 when the anomaly is not detected, reflecting the non-detection of a true anomaly. Figure 7 shows the evolution of \(S_{2}\) as a function of \(R_{f}\), for an anomaly consisting of 24 points, which, for the tests carried out, would correspond to an anomaly lasting 24 h. When the \(R_{f}\) is less than 12, that is, the anomaly detection is performed in the first 12 h, there is a small penalty, and only when the detection delay goes beyond that threshold  
Figure 6: Illustrative example of the evolution of the \(S_{1}\) metric as a function of the number of unknown points classified as abnormal (\(n_{va}\)), with \(n_{up}=8112\). does the algorithm's performance start to be penalized. Parameter \(k_{3}\) allows for more or less flexible in the promptness/tardiness in identifying known sets of anomalies. Finally, the value of the algorithm's performance metric is given by  
\[S_{final}=S_{1}\frac{\sum_{i=1}^{n}S_{2,i}}{n}, \tag{3}\]  
where \(S_{2,i}\) is the values of \(S_{2}\) for the anomalous range \(i\). The equation computes the average of the anomaly detection scores (\(S_{2}\)), as different anomalies with different extensions might exist, penalized by the classification of unknown points as anomalies (\(S_{1}\)). In the end, the intention of the final metric value, \(S_{final}\), is to reflect a balance between the detection of anomalies and the incidence of false positives. For example, \(S_{final}\approx 1\) means that all anomalies were detected quickly and the number of points from which the algorithm classification could not be assessed remained low.