esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental/Numerical Evaluation of Steel Trapezoidal Corrugated Infill Panels with an Opening
## 3 Results
### Strength and Stiffness  
The force-drift angle curves of studied specimens are shown in Figure 13. These curves are obtained using the envelope of the force-displacement curves due to cyclic loading. Figure 14 shows variations of the tangent stiffness with the drift angle for studied specimens. These figures show that the performance of both specimens with an opening in terms of strength and stiffness is similar. Figure 13 indicates that the ultimate strength of both specimens with an opening is approximately 21% smaller compared to the specimen without an opening tested by [PERSON] et al. [9]. Furthermore, as seen in Figure 14, the initial stiffness of both specimens with an opening is nearly 65% smaller compared to the specimen without an opening. Moreover, it is seen that an abrupt decline occurs in the stiffness of studied specimens at the onset of buckling, which is more remarkable in the specimen without an opening, and then the stiffness gradually decreases.  
Figure 12: Specim tested by [PERSON] et al. at the end of loading. Adapted from ref. [9].  
Figure 13: Force-drift angle curves of studied specimens.  
The strain-drift angle curves of both tested specimens are indicated in Figure 15. The strain values presented in Figure 15 for the beam and columns are values measured by axial strain gauges. In addition, the strain values presented for the plate are the principal strains, which are obtained using values measured by triaxial strain gauges. The von Mises criterion was used as the yield criterion. It is shown in Figure 15 that yielding occurs first in the steel shear plate, then in the beam, and finally in the columns. This arrangement is the desired one due to the capacity design criteria. Referring to Figure 15, the plate, beam and columns of specimen 1 yield at almost 0.12%, 0.98% and 1.2% drift angle, respectively. In addition, the plate, beam and columns of specimen 2 yield at almost 0.12%, 1.1% and 1.6% drift angle, respectively. Therefore, the beam and columns of specimen 2 yield at a larger drift angle compared to specimen 1. In fact, the demands generated in the surrounding frame are reduced for specimen 2, and this can be taken into account as a positive point for specimen 2.  
Figure 14: Stiffness performance of studied specimens.  
Figure 15: Inelastic behavior of the panel, beam and column.
## 5 Performance Evaluation
### Baseline Algorithm and Metrics  
In order to qualitatively and effectively evaluate the performance of our algorithm, we use the following baseline algorithms for comparison.  
1. Randomized algorithm. The random algorithm randomly selects edge network nodes for microservice deployment in response to each application request. If the initially selected node lacks the necessary resources, the algorithm repeats the selection process until a suitable node is found. However, the random algorithm here is not simply random but also involves the process of selecting the optimal solution.
2. Genetic algorithm. Generate a set of initial solutions that satisfy the constraints using a random algorithm. Subsequently, the optimization objective is used as a fitness function to evaluate the quality of each solution. Perform iterative crossover, mutation, and selection operations to search for the best task offloading solution. The genetic algorithm here includes selection strategies.
3. Fixed partition granularity. Using a fixed granularity to partition all CNN tasks without considering the characteristics of the task and the condition of the edge server.  
The following metrics are used to evaluate the performance of our partitioning and offloading algorithm.  
1. Average delay: The average delay of all tasks from request to completion, which is defined in Equation (9).
2. Energy consumption: The energy consumed during transmission and computation processes, which is defined in Equation (14).
3. Combined metric: Combined metric based on delay and energy consumption, which is defined in Equation (15).
# Potential of Sub-GHz Wireless for Future IoT Wearables and Design of Compact 915 MHz Antenna
## 4 Simulation Results
### Impedance Bandwidth Improvement  
In this section, a matching network is introduced to meet the impedance bandwidth requirement for the 915 MHz ISM Band. The main requirements for the matching network are that it provides the required impedance coverage with minimal loss over a specified bandwidth of at least 26 MHz from 902 to 928 MHz. Several matching network topologies are possible such as L-type, T-type, and \(\pi\)-type topologies. A low-pass, \(\pi\)-type matching network topology was chosen for this work as it has several desirable characteristics for this application. Firstly, it enables the Quality Factor (Q-factor) of the matching network to be specified by the user, unlike the L-type network. This in turn provides control of the impedance bandwidth. This topology also provides large impedance coverage, has good harmonic rejection capability, and has relatively small losses of a few tenths of a dB when high Q-factor microwave-grade components are selected [69]. The \(\pi\)-type matching network model is depicted in Figure 9 with the antenna feed denoted by SMA connector Port P\({}_{1}\) at Point A. A short section of grounded coplanar waveguide (GCPW) connects the input to the matching network. The GCPW uses the same substrate as discussed previously relating to Figure 6. The matching network is composed of three reactive elements: a source-side shunt capacitor \(C_{1}\), a series inductor \(L_{1}\) and a load-side shunt capacitor \(C_{2}\). The quality (Q-factors) of the matching network components from American Technical Ceramics [56] model the losses of the components. Wire Link \(WL_{1}\) is short-circuited using a soldered connection by default and therefore has negligible effect on the matching network. Its purpose is to enable direct measurement of the antenna input impedance \(Z_{\text{IN}}\) when removed.  
Figure 8: Simulated \(  
The function of the matching network is to match the antenna impedance \(Z_{\text{IN}}\) to the source impedance \(Z_{\text{S}}=50\)\(\Omega\) of the radio front-end over a bandwidth greater than 26 MHz. The matching network was designed and optimized using ANSYS Designer [70]. The optimization consisted of minimizing the input reflection coefficient or \(1S_{11}\)! over the ISM band from 902 to 928 MHz by varying the values of \(C_{1}\), \(C_{2}\) and \(L_{1}\). The final values for the matching network components are listed in Table 6.
# Structural Damage Detection through Dual-Channel Pseudo-Supervised Learning
## 4 Experimental Verification on a Four-Story Steel-Frame Structure
### Experimental Results  
To accurately assess the effectiveness and generalizability of the proposed method in this paper, we conducted a comparative analysis of the detection accuracy among five different deep learning models, namely, the CNN, LSTM, multi-head CNN, CNN-LSTM, Transformer, Conformer, CNN+Conformer, and Conformer+Conformer models, using a four-story single-span steel-frame structure. The damage detection accuracy results are summarized in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Floor/ Direction** & **CNN** & **LSTM** & \begin{tabular}{c} **Multi-Head** \\ **CNN** \\ \end{tabular} & **CNN-LSTM** & **Transformer** & **Conformer** & \
\begin{tabular}{c} **CNN+Con-** \\ **former** \\ \end{tabular} &
\begin{tabular}{c} **Conformer+** \\ **Conformer** \\ \end{tabular} \\ \hline First floor, x direction. & 80.98 & 84.45 & 87.44 & 89.36 & 89.52 & 91.18 & 93.53 & 94.33 \\ First floor, y direction. & 77.78 & 82.10 & 85.73 & 83.17 & 87.01 & 93.37 & 94.28 & 98.93 \\ Second floor, x direction. & 76.01 & 86.59 & 84.45 & 83.49 & 86.43 & 91.93 & 92.41 & 94.12 \\ Second floor, y direction. & 82.05 & 84.93 & 88.46 & 88.35 & 87.23 & 93.48 & 94.92 & 99.57 \\ Third floor, x direction. & 81.20 & 83.60 & 84.72 & 84.82 & 84.61 & 88.67 & 89.63 & 92.84 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Damage detection accuracy across different floors and directions (%).  
Figure 10: Acceleration response curves of the D.P.0 damage pattern. (**a**) Acceleration on the south side of the fourth floor. (**b**) Acceleration on the north side of the fourth floor.  
As shown in Table 7, hybrid models (i.e., CNN+Conformer and Conformer+Conformer) achieve average accuracies of 93.05% and 96.64%, significantly surpassing the Conformer (91.71%), Transformer (87.60%), CNN-LSTM (86.63%), multi-head CNN (85.79%), LSTM (83.92%), and CNN (78.43%) models. Notably, the hybrid models consistently outperformed the single models in all directions at each floor. This consistent high accuracy suggests that the DCPS learning strategy significantly improved the model's performance. Furthermore, the superior performance of hybrid models highlights the benefits of combining advanced network architectures with the DCPS learning strategy.
## 5 Discussion
### Answers to Research Questions  
After presenting the literature, the methodology, and the results and acknowledging the limitations of the proposed approach, it is time to answer the research questions that were presented in Section 1.  
**RQ1.** How can provider fairness in terms of user coverage and diversity be achieved in recommender systems?  
The literature provides a formalization of the problem at hand, alongside a solution. The literature models the problem as a quadratic constraint mathematical programming problem, which is NP-Hard and hence finding the optimal solution is not feasible. As a result they propose a heuristic solution for solving diversity constraints. However, as the latter is not adequately described, we introduce a new heuristic approach for the solution of diversity constraints.  
**RQ2.** Are the results of the proposed methodology satisfactory?The results of the proposed heuristic approach not only achieve the targets of sufficient user coverage and diversity per item provider, but are also very close in terms of predicted ratings with the recommendation lists provided by the solution of the unconstrained problem, which acts as an upper bound for the heuristic solution.  
**RQ3.** What are the limitations of the proposed methodologies?  
There are several limitations with the proposed approach that stem from the formalization of the problem. Specifically, the definition of diversity fails to capture the dissimilarity among users to whom an item is recommended. Instead, it is observed that the most recommended items of a provider influence the diversity significantly more than the dissimilarity among the users to whom an item is recommended, and this fact is exploited by the heuristic approach that has been developed. Hence, the problem definition and in particular the quantification of diversity should be redefined. As a result, new solutions to the problem should be developed.  
Moreover, the proposed approach does not take into consideration the position of an item in the recommendation list, which is of utmost importance in synchronous recommender systems.  
**RQ4.** How can the aforementioned limitations be abated?  
In order to address the problematic definition of diversity, we redefine it under the context of Section 5.2. Specifically, two new definitions are introduced, discussing their pros and cons. Moreover, new high-level heuristic approaches are proposed for the solution of the newly-defined diversity constraints.
# Recommendations for Different Tasks Based on the Uniform Multimodal Joint Representation
## 6 Conclusions  
We propose a framework for multimodal joint representation learning of pins on CCSNs. The obtained representation contains the information of user interests, which is useful for recommender systems and user modeling. We modeled boards and users with the FV and propose a series of recommendation methods for different recommendation tasks, including a novel board thumbnail recommendation defined by us and based on our pin recommendation. The experimental results show that the obtained representations perform better in terms of interpreting pin-level interests than unimodal representations with lower dimensions, and our recommendation methods based on our multimodal representation are effective in terms of recommending pins, board thumbnails, board categories, and boards.  
Conceptualization, H.L. and S.D.; methodology, H.L. and S.D.; software, B.Y. and D.Z.; validation, B.Y. and D.Z.; formal analysis, H.L. and S.D.; investigation, B.Y. and D.Z.; resources, B.Y.; data curation, H.L. and D.Z.; writing--original draft preparation, H.L., S.D. and B.Y.; writing--review and editing, L.W. and M.J.; visualization, H.L.; supervision, L.W.; project administration, M.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Dimension** & **Top-5 MRR** & **MRR** \\ \hline Category Based & 1 & 2.12\% & 3.85\% \\ \hline Image + LR & 33 & 16.08\% & 18.61\% \\ Text + LR & 33 & 15.93\% & 17.95\% \\ MAE + LR & 33 & 16.85\% & 19.39\% \\ ICMAE + LR & 33 & 17.62\% & 20.27\% \\ Ours + LR & 33 & 17.58\% & 20.13\% \\ \hline Image + Mean Vector & 4096 & 33.66\% & 35.97\% \\ Text + Mean Vector & 300 & 25.93\% & 27.49\% \\ MAE + Mean Vector & 1778 & 34.31\% & 36.48\% \\ ICMAE + Mean Vector & 1898 & 35.02\% & 37.23\% \\ Ours + Mean Vector & 2048 & 35.76\% & 37.88\% \\ \hline Image + FV & 8192 & 35.19\% & 37.50\% \\ Text + FV & 600 & 24.39\% & 25.64\% \\ MAE + FV & 3556 & 35.81\% & 37.97\% \\ ICMAE + FV & 3796 & 37.76\% & 39.84\% \\ \(Ours+FV\) & **4096** & **38.97\%** & **41.22**\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of board recommendation.
## 5 Performance Evaluation and Comparison  
### Performance Evaluation  
Figures 9 and 10 show the spectral efficiency and energy efficiency responses of all the realized spectrum access techniques for small cells in a single building (i.e., \(L\) = 1) and for an ultra-dense deployment of small cells in multiple buildings (i.e., \(L\) \(>\) 1) respectively. In general, an increase in \(L\) increases the spectral efficiency linearly and energy efficiency exponentially for all techniques. Moreover, unlike the spectral efficiency performance, for large values of \(L\) (i.e., for the dense small cell deployment), the energy efficiency performance of all techniques does not vary considerably from one another. This is due to the fact that as \(L\) gets large, the capacity of small cell UEs dominates that of macro UEs as can be found from (10), (21), (32), (42) and (52) such that the ratio of the transmit power of small cells to the capacity achieved by them gets eventually almost fixed as given by (12), (23), (34), (44), and (54). Hence, unlike the spectral efficiency, it signifies that there is a limit to the density of small cells such that the energy efficiency cannot be improved considerably with an increase in the density of small cells. Note that even though dedicated access (DedA) and StSR benefit from the less or no coordination signaling overhead in the backhauls and associated complexities, DySP, LSA, and LAA pay that off by providing with significant outperformance in capacity, spectral efficiency, and energy efficiency responses. Figure 9: Performance responses of all the realized spectrum access techniques for small cells in a single building (i.e., \(L\) = 1): (**a**) The spectral efficiency and (**b**) energy efficiency. Note that except LAA, one of the major concerns with other techniques is the number of TTIs during which the transceiver 2 of all small cells are assigned with the shared spectrum. In general, an increase in the number of TTIs increases the capacity achieved by the transceiver 2, and the maximum
# Sustainable Urban Water Management in China: A Case Study from Guangzhou and Kunming
## 6 Water Sensitive Solutions for Guangzhou and Kunming
### Solutions for Guangzhou
#### 6.1.4 Encourage Private Investment  
The government should support private sectors investment, in order to benefit from new technology and alleviate economic pressure. For instance, the government can cooperate with a technological software company to develop a mobile application for the public to download information on the flood risk map and an early warning system. This program, which utilizes a global positioning system, can alert individuals in advance of flooding and implement certain flood mitigation steps to assist them in preparing. Additionally, advertisements can be inserted in this program to generate revenue by selling the firm or government's professional edition [55]. Additionally, the Guangzhou government may provide preference to construction companies who are willing to construct commercial buildings such as shopping malls with BGI-compliant features such as green roofs and green walls [56].
## 3 Channel Proximity Discovery
### QoS Business Model  
In the proposed proximity discovery method, end-users should be influenced to select a TV channel that another proximate user is watching than joining with a new channel that the proximate router currently does not forward. Therefore the approach needs to be incentivised for the benefit of the end-user.  
Assume the ISP guarantees QoS (DiffServ EF) for a set of HDTV channels and the SDTV channels are served under the best-effort service. Those HDTV channels are charged for a user at a certain cost per program and all the SDTV channels go under his/her monthly subscription. Then the first user can select a HDTV channel assuming others will join subsequently to share the cost (the first user confidence will grow up with the popularity of the system). The second user can see the proximity of the first user and may join knowing that they can definitely avail of a discount. The system needs to record the number of users per channel at a proximate router and apply a pricing model which reduces the cost of collectively accessing the content.  
The FI is evolving as an in-network capable service-oriented architecture and network operators are expected to implement various services in addition to transporting the data as of today (_i.e._, aggregation routers will be integrated with service functionalities as well). Therefore in IPTV networks deploying _ProxyTV_, providers can allow subscribed users to query multicast routing information of selected edge routers (e.g., may be at the DSLAM). Video head-end can deploy PNS and feed the information of resolved TV channels to local delivery points.
# Ad-RuLer: A Novel Rule-Driven Data Synthesis Technique for Imbalanced Classification
## 4 Experiment
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **SMO** & **ADA** & **TL** & **B-SMO** & **K-SMO** \\ \hline \multirow{4}{*}{LR} & GM & **0.0380** & 0.2324 & **0.0371** & 0.2070 & **0.0020** \\  & F1 & 0.0856 & **0.0039** & 0.1386 & 0.0840 & 0.0644 \\  & AUC & **0.0108** & **0.0010** & **0.0173** & **0.0328** & **0.0020** \\  & Prec & 0.2754 & **0.0039** & 0.2324 & **0.0273** & 0.0506 \\ \hline \multirow{4}{*}{RF} & GM & 0.6784 & 0.9219 & 0.7671 & **0.0371** & **0.0039** \\  & F1 & 0.2754 & 0.1601 & 0.3139 & **0.0195** & **0.0195** \\  & AUC & 0.3223 & 0.2135 & 0.3223 & 0.1727 & 0.1309 \\  & Prec & 0.9219 & 0.3223 & 0.7695 & 0.7695 & 0.5566 \\ \hline \multirow{4}{*}{XGB} & GM & 0.4316 & 0.2754 & 0.3222 & **0.0371** & **0.0371** \\  & F1 & 0.6250 & 0.1309 & 0.4316 & 0.1614 & 0.1602 \\ \cline{1-1}  & AUC & 0.4922 & 0.3429 & 0.4922 & 0.3139 & 0.1933 \\ \cline{1-1}  & Prec & 0.6250 & 0.2324 & 0.7695 & 0.4922 & 0.1055 \\ \hline \hline \end{tabular}  
* Approach abbreviations: SMO, SMOTE; ADA, ADASYN; T-L, Tomek-links; B-SMO, Borderline-SMOTE; K-SMO, KMeansSMOTE; LR, Logistic Regression; RF, Random Forest; XGB, XGBoost; GM, G-mean; F1, F1 score; Prec, Precision.  
\end{table} 
Table 3: The Wilcoxon Signed-rank test results performed on all datasets with Ad-RuLer serving as the control algorithm. If the \(p\)-value for a particular method is less than 0.05, it indicates a significant performance difference between that method and Ad-RuLer. exhibits a significant difference from KmeansSMOTE in terms of G-mean. Compared to other oversampling algorithms, although Ad-RuLer's average ranking in G-mean, F1 score, and AUC is higher than other resampling algorithms, the significant differences cannot be determined from the current analysis. Given the conservative nature of the Nemenyi post hoc test, the significance of Ad-RuLer's performance relative to other resampling algorithms remains to be conclusively established. Therefore, future experiments involving a broader range of datasets are necessary to robustly test these relationships.
## 6 Solution Types/Algorithms
### Evolution Based Algorithms  
Evolution based multi-objective optimization algorithms use a population based approach in which more than one solution participates in an iteration and evolves a new population of solutions in each subsequent iteration [11]. These algorithms are easy to implement and do not require any derivative information. Therefore, evolution based algorithms have a wide-spread applicability and have been extensively used to solve multi-objective optimization formulations relating to wireless sensor networks. For example in [9], sensor node deployment problem has been formulated as a multi-objective optimization problem. The authors suggested to use multi-objective evolutionary algorithm to find an arrangement of sensor node to maximize the area of coverage, minimize the net energy consumption, maximize the network life time and minimize the number of deployed nodes while maintaining the desired connectivity level. An evolution based multi-objective optimization algorithm has been used in [182] for guarding a central base from enemy attacks, searching out and destroying enemy units. The authors analyzed the effectiveness of their proposed algorithm and argued that it can evolve to complete the multi-objective task, each time with the loss of one sensor. Energy problems in traditional sensor nodes can be solved by using energy harvesting micro electro-mechanical systems (EH-MEMS). Multi-objective design optimization of EH-MEMS has been suggested in [183]. The authors used evolutionary based algorithm to find the trade off between energy harvesting capability and the overall volume of the device. An evolution based approach has been used in [184] to develop a multi-objective optimization formulation for determining the optimal number of sensors, locating and setting their orientation parameters in an amorphously generated 3-D terrain. This formulation has been used to find a tradeoff between maximizing the observability of the region of interest, maximizing the stealth of the sensors, and minimizing the cost of the sensors used. In [185], an evolution based algorithm has been used to minimize energy consumption and to increase life time of wireless sensor networks based on cooperative multiple input multiple output systems. A multi-objective optimization formulation based on evolutionary algorithm has been used in [186] for maximizing coverage ratio, minimizing the number of active sensors and maximizing network life time or diminishing energy consumption.