esa-sceva/satcom-chunk-collection huggingface dataset:
### Contribution to Study  
The prediction of wettability has been made by several researchers using numerous methods. However, some gaps such as testing with only water- or oil-wet samples, using classical mathematical approaches, and its inapplicability on a field scale have been identified, leading to this research. Wireline logs offer the best approach to measure wettability in the field due to them being ubiquitous in every well. The reported success of applying machine learning techniques to predict several reservoir parameters makes its use a viable candidate in predicting NMR logs from wireline logs. Hence, the main contributions of this research are:  
1. The integration of a new technique based on laboratory reported results on Amatt-USBM and NMR T\({}_{2}\) logarithmic mean (T\({}_{2}\)LM) based wettability to develop a new mathematical model for establishing a relationship between field and laboratory wettability measurement.
2. The development of a systematic field methodology to predict wettability using NMR T\({}_{2}\)LM and water saturation.
3. Engineering new features from existing log data capable of improving the model prediction of NMR T\({}_{2}\)LM.
4. The application of deep ensemble super learner to establish a relationship between wireline logs and NMR T\({}_{2}\)LM and predict NMR T\({}_{2}\)LM. This predicted log could subsequently be used with the developed methodology and mathematical model to predict reservoir wettability.
# Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks +
## 4 The CHaChA Clustering Approach
### Initial Clustering and Channel Assignment
In our evaluation in Section 6, all mesh interfaces were assigned fixed IP addresses, and we assumed that applications have the required prior knowledge. In practice, however, numerous strategies are conceivable. For example, automatically configured link-local IPv6 addresses of the primary and secondary interfaces could be used, which CHaChA provides to other applications as part of the knowledge about the cluster membership of nodes. Alternatively, CHs could take on extended DHCP and DNS functionality to implement an appropriate addressing and naming scheme for the various subnets. **Handling errors in the phase sequence:** Possible errors in the phase sequence can be detected independently by the distributed nodes. For example, in the competition for MCH in phase 0, there is a theoretical risk of obtaining multiple MCHs as a result of continuous losses of CENT broadcast messages, especially if the parameters CENT_PERIOD and CENT_THRESH are chosen too small. However, with appropriate parameterization, this risk can be minimized. Based on the reception of MCH control messages of different sender addresses, a possible error case can be easily detected by each node. Subsequent problems can be detected by an exceeded (PHASE_X_TIMEOUT) within the phases. This could be caused by MCH failure or node disconnection, resulting in phase transitions not being initiated in time or not being received in parts of the network. Similarly, the channel selection sequence in phase 5 could be interrupted as a result of CH failure, so that a transition to phase 6 also does not occur. For error handling, nodes jump back to phase 0 as CFNs. If the reason was only a temporary disconnection to the MCH without affecting the phase sequence in the rest of the network, nodes can subsequently join one of the resulting clusters. On the other hand, if the MCH fails (or is permanently isolated), a new clustering is automatically started in the affected parts of the network. These and other mechanisms for handling failures and network changes are discussed in Section 4.4.5.
## 2 Testing Process  
The experimental setup utilized in this study involved an RLJW-2000 (Changchun City Chaoyang Test Instrument Co., Ltd., Changchun, China) electric hydraulic servo rock triaxial compression testing machine, as depicted in Figure 1. A total of 30 sandstone samples were employed, with three samples utilized per test. The experimental design comprised ten test sets, including five sets of uniaxial loading tests and five sets of cyclic loading and unloading tests. Confining pressures of 0 MPa, 5 MPa, 10 MPa, 20 MPa, and 30 MPa were selected. The rock samples were prepared in conformity with the specifications for rock samples outlined in the \"Standard Test Methods for Engineering Rock Masses\" (GB/T 50266-2013) [38], resulting in standardized cylindrical samples measuring 50 \(\times\) 100 mm. The bottom surfaces of the samples were required to possess a roughness not exceeding 0.05 mm, to be perpendicular to the axis, with a maximum inclination angle not exceeding 0.25 degrees. There should be no visible defects on the surface of the samples. The processed sandstone samples are shown in Figure 2.  
Figure 1: The RLJW-2000 electric-hydraulic servo rock triaxial testing system.  
The samples underwent cyclic loading and unloading tests under varying confining pressures of 0 MPa, 5 MPa, 10 MPa, 20 MPa, and 30 MPa. The unloading points prior to reaching the peak were determined at different confining pressures, specifically at 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90% of the peak axial strain attained during uniaxial loading. During the testing procedure, a minimum of two cycles of loading and unloading were conducted during the stress softening stage, while another two cycles were performed in the residual stress stage. The primary objective of these tests was to acquire data related to strain, stress, elastic modulus, and other relevant parameters of the rock material during the cyclic loading and unloading process. These data serve as a foundation for the calculation of dissipated energy and plastic strain.
# Incorporating Code Structure and Quality in Deep Code Search
## 5 Evaluation
### Compared Method  
We compare our method with DeepCS. DeepCS embeds a code snippet and the corresponding description into four vectors: the method name vector, the API sequence vector, the token vector, and the description vector. DeepCS uses BLSTM and maxpooling to embed method names, API sequences, and descriptions. For code snippet tokens, DeepCS simply uses an MLP. The vectors of method name, API sequence, and tokens are fused into one vector through a fully connected layer.  
We improved DeepCS by introducing program slice and attention mechanism. To understand the impact of introducing program slice and attention mechanism, we designed several comparison methods based on DeepCS:  
1. _P-DeepCS_: Program slice based DeepCS (P-DeepCS) uses program slice instead of API sequence in DeepCS. This is to explore the impact of introducing program slice.
2. _A-DeepCS_: Attention based DeepCS (A-DeepCS) uses attention layers instead of maxpooling layers in DeepCS. This is to explore the impact of introducing attention mechanism.
3. _PA-DeepCS_: Program slice and attention based DeepCS (PA-DeepCS) use program slice to represent method body and use attention mechanism to weight embedded vector. However, when fusing the vectors of method name, program slice, and tokens, PA-DeepCS still uses a fully connected layer.
4. _SQ-DeepCS_: SQ-DeepCS is our proposed method. The key difference of PA-DeepCS and SQ-DeepCS is that SQ-DeepCS adopts an attention layer to concentrate the vectors of method name, program slice, and tokens.  
We also formulate experiments in the context of the code search techniques mentioned in Section 1:  
1. _NCS_: NCS extracts some specific keywords as semantic features of code snippets, such as method name, method invocations, and enums. NCS combines these keywordswith fastText [35] and conventional IR techniques, such as TF-IDF. The encoder of NCS adopts unsupervised training mode.
2. _At-CodeSM_: At-CodeSM used Tree LSTM [36] to process the abstract syntax tree of code snippets. At-CodeSM extracted three features of code snippets: method name, token, and AST. For tokens of code snippets, At-CodeSM use LSTM and attention mechanism to obtain the vector of tokens. When fusing the vectors of method name, AST, and tokens, At-CodeSM uses a fusion layer.
# An Experience Transfer Approach for the Initial Data of Iterative Learning Control
## 2 Problem Description
### Problems Raised  
In the industrial production or process control, when parts of the equipment or the materials of the processed parts are changed, the experience gained from original iterative learning control is no longer applicable. In general, learning should be resumed from zero initial data. In this re-learning process, there would be a negative impact on production efficiency and cost. For the mass production, this negative impact can be ignored, considering that tens of thousands of products are produced. However, for he small batch production, it is intolerable to ignore the negative impact caused by the continual changing of production process and equipment, which poses new challenges to the application of traditional ILC. How to effectively use the original control experience data of ILC in the new tasks becomes an important issue.  
If the existing iterative learning control experience of the original system is directly taken as the initial iterative learning control value of the new system, the effect is generally better than that of learning from zero initial data. However, it is just a simple direct inheritance of experience. It lacks the necessary pre-adjustment according to the specific differences between the old and new systems. So, there is still room for further optimization in the setting of the initial iterative control data according to the experience obtained from the former ILC.  
In general, when the parameter in the system is changed, e.g., the power of driving motor, the damping of the pipeline, system lag, and so on, the difference between the old and the new system can be checked via the equipment manual or be measured through tests. This is similar to the situation of an experienced technician who is asked to deal with a new material. The technician can compare the performance difference of new material with the former ones via testing or reading the users' manual of the new material. Then the difference could be used in the adjustment of the working experience. As a result, the technician could soon be able to make qualified products with the new material. In this paper, this process is called iterative learning control with experience transfer.  
In this paper, the initial data of ILC are studied. The experience gained from the ILC in the original system is inherited and transformed to decrease the iteration turns in the ILC of new system, as shown in Figure 1. As the method proposed in this paper only changes the initial data of ILC, there is no change to the algorithm itself. So, the convergence property of the ILC with experience inheritance and transform is not changed too.
## 4 Experiments
### Quantitative Results
#### 4.2.4 Number of Dialog History  
We experimented with the amount of dialog history to evaluate the impact of dialog history on the model performance of the two major metrics (i.e., MRR and NDCG). The results in Figure 4 are the average of the MRR and NDCG performance for each dialog turn in the validation set and show that as the amount of dialog history information increases, the MRR tends to gradually improve, but the NDCG score deteriorates. This quantity  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **AVG \(\downarrow\)** & **NDCG \(\uparrow\)** & **MRR \(\uparrow\)** & **R@1 \(\uparrow\)** & **R@5 \(\uparrow\)** & **R@10 \(\uparrow\)** & **Mean \(\downarrow\)** \\ \hline ReDAN [10] & 2 & 61.86 (2) & 53.13 (3) & 41.38 & 66.07 & 74.50 & 8.91 \\ LTMI \(\star\)[24] & 2 & 60.92 (3) & 60.65 (2) & 47.00 & 77.03 & 87.75 & 4.90 \\ MVAN \(\star\) (ours) & 1 & **63.15** (1) & **63.02** (1) & **49.43** & **79.48** & **89.40** & **4.38** \\ \hline ReDAN+ \(\dagger\)[10] & 3 & 64.47 (2) & 53.74 (3) & 42.45 & 64.68 & 75.68 & 6.64 \\ LTMI \(\star\)\(\star\)[24] & 1 & **66.53** (1) & 63.19 (2) & 49.18 & 80.45 & **89.75** & **4.14** \\ MVAN \(\star\)\(\dagger\) (ours) & 2 & 63.22 (3) & **66.28** (1) & **53.87** & **82.08** & 89.65 & 4.61 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of different methods of combining discriminative and generative models on VisDial v1.0 (test-std). \(\dagger\) denotes ensembles and \(\star\) indicates that the model was trained using multi-task learning.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **AVG \(\downarrow\)** & **NDCG \(\uparrow\)** & **MRR \(\uparrow\)** & **R@1 \(\uparrow\)** & **R@5 \(\uparrow\)** & **R@10 \(\uparrow\)** & **Mean \(\downarrow\)** \\ \hline MCA [22] & 4 & 72.47 (4) & 37.68 (4) & 20.67 & 56.67 & 72.12 & 8.89 \\ Visdial-BERT [17] & 1 & 74.47 (2) & 50.74 (2) & 37.95 & 64.13 & 80.00 & 6.28 \\ VD-BERT [18] & 1 & **74.54** (1) & 46.72 (3) & 33.15 & 61.58 & 77.15 & 7.18 \\ MVAN (ours) & 1 & 73.07 (3) & **56.06** (1) & **44.38** & **68.50** & **81.18** & **5.98** \\ \hline P1\_P2 \(\dagger\)[21] & 4 & 74.91 (2) & 49.13 (5) & 36.68 & 62.96 & 78.55 & 7.03 \\ LTMI \(\dagger\)[24] & 4 & 74.88 (3) & 52.14 (4) & 38.93 & 66.60 & 80.65 & 66.53 \\ MReal-BDAI \(\dagger\)[21] & 2 & 74.02 (4) & 52.62 (2) & 40.03 & 68.85 & 79.15 & 6.76 \\ VD-BERT \(\dagger\)[18] & 1 & **75.35** (1) & 51.17 (4) & 38.90 & 62.82 & 77.98 & 6.69 \\ MVAN \(\dagger\) (ours) & 2 & 71.40 (5) & **65.16** (1) & **52.88** & **79.50** & **88.55** & **4.27** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of dense annotations fine-tuning on the VisDial v1.0 test-std dataset. \(\dagger\) denotes ensembles. The models train with discriminative decoder are also used for ensembling.  
analysis shows that history information decreases the NDCG score but substantially boosts the other metrics.
## 4 Discussion
### The Contradiction between Phytochemical Diversity and Genetic Diversity  
Previous study has shown significant differences in phytochemical diversity among different ecotypes of _Rubia tinctorum_, which were greatly influenced by environmental factors [67]. In the present study, phytochemical diversity in flavonoid biosynthesis pathways was observed among different ecotypes of sandrice under cold stress. Specifically, DL and AEX exhibited similar accumulation patterns of flavonoid metabolites in response to cold stress, such as the up-regulation of kaempfer, quercetin, dihydroquercetin, and isorhamnetin in the downstream pathways. In contrast, DK showed a specific up-regulation of astragalain. These findings indicate that DL and AEX exhibit higher phytochemical diversity when compared to DK in response to cold stress, possibly due to their adaptation to high-altitude and high-latitude environments with lower temperatures. These findings aligned with a previous study that reported different flavonoid accumulation patterns in response to low-temperature stress among two _F. tataricum_ landraces [27], potentially influenced by long-term domestication and evolutionary adaptation to local temperature conditions [68; 69]. In addition, the diversification of flavonoid biosynthesis was also observed among different ecotypes of sandrice under drought stress (unpublished data).  
On the other hand, previous population genetic analyses classified DK and DL within the _Central_ lineage, while AEX was categorized into the _Eastern_ lineage [43]. In this study, DL and DK also exhibited similar transcriptional responses to cold stress, distinct from AEX, which may indicate lineage-specific adaptations. Combined with the metabolism data, a conflict between genetic diversity (as evidenced by lineage distinctions) and phytochemical diversity (observed in flavonoid profiles) under cold stress was observed, highlighting the complex interplay between genetic regulation and environmental adaptation. Genetic lineage appears to influence broad transcriptional responses, while environmental factors such as cold stress can significantly alter metabolic pathways independent of genetic background. This underscores the necessity for integrated approaches combining genetics, transcriptomics, and metabolomics to comprehensively understand and manipulate plant responses to environmental stresses for agricultural and industrial purposes.
# A Novel Approach to Enhance the Energy Efficiency of a NOMA Network
## 2 Related Works and Motivation
Finally, it was shown that effectively integrating resource allocation and grant-free transmission within NOMA will provide a better EE and throughput than using them separately [31]. This paper deals with resource and user allocation problems for NOMA systems with wireless power transfer to ensure fast rates and energy efficiency. We compare the performance of our proposed USFMA with two other algorithms: The first one being the User Subchannel Matching Algorithm (USMA) [32], which is considered as a many-to-many matching algorithm. The principle of USMA is that each user selects and proposes their preferred sub-channel. Based on the allowed number of users per sub-channel, each sub-channel makes a decision of accepting or rejecting any user offer. When all users have made an offer, a round of proposals is performed, and once all users are assigned to sub-channels, the iteration is ended and user allocation is complete. The criteria and selection algorithm is described in detail in Table I in [32]. The second algorithm is Channel State Sorting-Pairing Algorithm (CSS-PA) [33]. As known, SIC is sensitive to the channel state, and hence the Signal to Interference Plus Noise (SINR) difference between paired users should be large enough to alleviate error propagation. Thus, CSS-PA pairs a user with a good condition with a user with a bad channel condition, which enhances user fairness and increases system capacity  
The contributions of this work can be summarized as follows:  
* We propose a new user allocation algorithm called User Sub-channel Fair Matching Algorithm (USFMA), benefiting from existing user allocation algorithms and combining their advantages. Unlike USMA, we propose an optimum channel gain compensation, sorting, and selection that can enhance the overall system capacity and performance. This algorithm has a lower computational complexity than the Exhaustive Search Algorithm (ESA) and can ensure user fairness. Moreover, the complexity of the USFMA will not increase sharply when increasing the number of superimposed users. * Optimization of the energy efficiency of NOMA systems. We propose using the DC programming method to allocate power for end users superimposed on the corresponding sub-channel. The main idea is to utilize DC programming to convert non-convex problems into convex problems. * Simulations of the proposed algorithm in Matlab. The simulation results confirm that NOMA systems surpass OFDM systems. Additionally, the USFMA is better than the existing USMA and CSS-PA. Therefore, using DC programming to allocate power for end users can improve the system's energy efficiency. The paper is organized as follows: The system model is described in Section 3. We formulate the problem and propose the objective optimization function in Section 4. The proposed resource allocation scheme is addressed in Section 5. The performance of the proposed method is evaluated in Section 6. We finally conclude our work in Section 7.
# Implementing Smart City Technologies to Inspire Change in Consumer Energy Behaviour
## Appendix A Technical Parameters of 3G and WiMAX  
\begin{tabular}{c c c} \hline \hline
**Technology** & **3G** & **WiMAX** \\ \hline Channel bandwidth & 850 MHz/900 MHz/1900 & 1.25 MHz/1.75 MHz/3.5 \\  & MHz/AVS (1700-2100 MHz) & MHz/5 MHz/7 MHz/8.75 \\ Data transmission speed & 384 kbtps\(-\)2 Mbps & 1 Mbps-75 Mbps \\ Receiver sensitivity & \(-\)60 dbm & \(-\)90 dbm \\ Spectral efficiency & \(<\)1.25 bps/Hz & \(<\)37 bps/Hz \\ Modulation & QPSK/BPSK & QPSK, 160 QAM, 64 QAM \\ Frequency band & 5 MHz & 2 GHz\(-\)11 GHz \\ Duplexing & CMDT/TDD & TDD, FDD \\ Multiple access & TDMA/CDMA & SOFDMA \\ Standard & 3 GPP & IEEE 802.16 \\  & Class 1\(-\)33 dbm & \\ Transmitter power & Class 2\(-\)27 dbm & 23 dbm \\  & Class 3\(-\)24 dbm & \\  & Class 4\(-\)21 dbm & \\ Chip rate & 1.22 Mcps & 7-14 ksps \\ Number of users & 50\(-\)150 & 200\(-\)600 \\ \hline \hline \end{tabular}
###### Abstract  
The present work proposes a parametric finite element model of the general case of a single loaded dental implant. The objective is to estimate and quantify the main effects of several parameters on stress distribution and load transfer between a loaded dental implant and its surrounding bone. The interactions between them are particularly investigated. Seven parameters (implant design and material) were considered as input variables to build the parametric finite element model: the implant diameter, length, taper and angle of inclination, Young's modulus, the thickness of the cortical bone and Young's modulus of the cancellous bone. All parameter combinations were tested with a full factorial design for a total of 512 models. Two biomechanical responses were identified to highlight the main effects of the full factorial design and first-order interaction between parameters: peri-implant bone stress and load transfer between bones and implants. The description of the two responses using the identified coefficients then makes it possible to optimize the implant configuration in a case study with type IV. The influence of the seven considered parameters was quantified, and objective information was given to support surgeon choices for implant design and placement. The implant diameter and Young's modulus and the cortical thickness were the most influential parameters on the two responses. The importance of a low Young's modulus alloy was highlighted to reduce the stress shielding between implants and the surrounding bone. This method allows obtaining optimized configurations for several case studies with a custom-made design implant.  
dental implant; finite element analysis; parametric study; implant design; implant material; low Young's modulus alloy
# Compact Ultra-Wideband Series-Feed Microstrip Antenna Arrays for IoT Communications
## 3 Results
### Seven Elements Array  
The seven-element array shown in Figure 4b has total dimensions of 14 \(\times\) 30.5 mm\({}^{2}\), and was also subjected to a set of measurements, with the results being shown below.  
The comparison between the simulated and measured S\({}_{11}\) curves is shown in Figure 8. It is possible to verify that the array has a measured bandwidth (S\({}_{11}\)\(<-\)10 dB) of approximately 8.3 GHz, from 24.48 GHz to 32.48 GHz, slightly lower than the simulation, centered in the 28 GHz region. In this respect, both curves are similar, and practically across the entire band it is possible to find a value of S\({}_{11}\) below \(-\)15 dB.  
Regarding the radiation patterns, these are shown in Figure 9, for different frequencies. It is possible to verify that in the \(\phi\) = 0\({}^{\circ}\) plane, both curves are very close, allowing to clearly identify the main radiation lobe, whereas in the \(\phi\) = 90\({}^{\circ}\) plane there are more differences, but it is still possible to define the main lobe clearly on two of the three frequencies. Possibly the impact of the connector on this structure is more evident, functioning as a reflector element.  
Figure 10 shows the evolution of both gain and total efficiency over frequency for the seven-element array. It is possible to verify that in terms of the efficiency, it remains relatively stable throughout the band, with a value slightly higher than 80%. Concerning the gain, it already presents more visible variations over the frequency, however being around 9 dBi, in some points rising a little almost reaching 11 dBi, and in others falling slightly; however, in general, results are good, given the reduced dimensions and high operating frequencies.  
Figure 8: Simulated and measured reflection coefficient of the 7 elements antenna array.  
Figure 7: Variation of the Gain and Efficiency of the 5-element array over the frequency.