esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Description  
### Mission Scenario Analysis  
There are heterogeneous UAV swarms consisting of distributed subsets in the mission area. Each subset is composed of several reconnaissance UAVs, attack UAVs, and reconnaissance-attack UAVs. The procedure of task assignment includes target allocation and subtask assignment. The swarm accomplishes the initial assignment, and then the UAVs cooperatively perform tasks. If a UAV discovers a new target, it relays the target information to others within a limited range according to hierarchical communication topologies, which triggers dynamic task assignment and then updates each UAV's task sequence.
## 2 Methodology  
### Wind Tunnel Testing Equipment and Parameter Settings  
Snowdrift tests were conducted using the DC wind tunnel (shown in Figure 2) at Beijing Jiaotong University. The wind tunnel dimensions are 1.2 m (W) \(\times\) 1.5 m (H) \(\times\) 10.0 m (L). The free-stream wind speed ranges from 2 to 20 m/s, featuring smooth speed control. The main components of the wind tunnel include the wind flow area, the tunnel body, and the data acquisition and control system. The reference wind speed of the test flow field is measured and monitored using a fixed-spot micro pressure meter. The DC wind tunnel is equipped with the wind load and the corresponding testing devices, which include pressure measurement, force measurement, vibration measurement, and other functionalities that are capable of meeting diverse test conditions requirements.  
The simulated wind field within the wind tunnel was characterized using a Cobra Probe wind speed measurement system from Turbulent Flow Instrumentation Pty. Ltd. (TFI, Tullamarine, VIC, Australia), integrated with a three-dimensional traverse system (accurate to 0.1 mm) to ensure accurate and reliable wind field measurements. The wind pressure on the structural surfaces was measured using electronic pressure scanning valves from Pressure Systems Inc. (PSI, Hampton, VA, USA), with pressure acquisition facilitated by eight PSI ESP-64 HD miniature pressure scanning modules, enabling high-speed synchronous pressure measurements at 512 points.  
Unlike previous studies that employed a self-designed wind tunnel with a test section height of 0.6 m and a geometric scale of 1:12 [23], our experiments utilized a validated, large-scale wind tunnel (1.5 m height) at Beijing Jiaotong University (Figure 2), achieving a refined geometric ratio of 1:150. This design minimizes boundary layer interference and enhances flow field accuracy, as evidenced by turbulence intensity stabilization at 11%, consistent with Class B terrain specifications (Figure 3). Additionally, our setup incorporated more wind speed sampling points across vertical profiles, enabling high-resolution analysis of near-surface velocity gradients critical for snow particle transport.
# Decision Support Models for Operations and Maintenance for Offshore Wind Farms: A Review
## 7 Discussion  
### Factors to Consider  
In the literature the factors influencing the planning and cost of maintenance are identified as (a) the component degradation and occurrence of failures, (b) availability of maintenance crew, spare parts and vessels, (c) transportation and vessel routing, (d) the weather, (e) economic parameters such as the electricity price, and (f) the maintenance strategy.  
1. failure modeling, (i) different stochastic processes with failure rates are used by [11; 12; 14; 16; 17; 19; 20; 21; 22; 26; 30; 32; 33; 34; 37; 39]; (ii) damage accumulation or time dependent failure rates are included in [10; 17; 21; 22; 26; 30; 31; 32; 35; 36; 37; 38; 40]
2. The availability of crew, parts and vessels is (i) considered as a (limited) resource, sometimes dependent on the failure type, by [12; 13; 16; 17; 20; 21; 22; 25; 26; 31]; (ii) while [15; 19] optimize in their analyses for the number of vessels.
3. Vessel routing and transportation has been studied by [10; 25; 41; 46; 53; 52].
4. The weather is used as an input to the decision support models by [11; 12; 14; 16; 17; 19; 20; 21; 22; 24; 26; 30; 33; 34; 41; 63; 101]; weather generation in the models is based on (i) Markov models by [12; 16; 19; 30; 56; 57; 59; 62; 64]; (ii) autoregressive by [11; 14; 20; 60; 61]; (iii) re-sampling by [31]; (iv) probability distributions by [33; 34; 61]; and (v) a Langevin process by [65].
5. For cost calculations (i) production losses due to downtime (indirect costs) are considered by [11; 12; 20; 22; 30; 36]; (ii) maintenance costs in form of vessel hire, spare parts or worker salary (direct costs) are considered by [11; 14; 16; 17; 20; 21; 22; 23; 30; 31; 36; 67].
6. The types of maintenance are discussed in the following Section 7.2.
## 3 Methods
### Characteristics of the Selected Studies
#### 3.3.2 Scope of Use  
The studies were conducted for various applications, including measurement of respiratory volumes [9; 27; 34; 35; 37; 44; 45; 46; 47; 48; 49; 50], monitoring of respiratory activity in infants [11; 40; 51], sleep studies in adults [12; 52; 53; 54; 55; 13], and tracking of selected human activities [36] and smoking [57; 58]. The studies [37; 38; 48] exclusively focused on the calibration of RIP devices, and the studies [11; 23; 55] analyzed the performance of RIP exclusively in children. The study [59] utilized RIP to detect stiffening of the aorta by measuring pulse wave velocity (PWV). In [39; 43], experiments were performed to determine the validity of RIP for high-BMI subjects. The studies [41; 42; 60; 61; 62; 63] solely focused on machine learning techniques for automated detection or prediction of non-ideal conditions. Studies on all applications of RIP identified in our search were included in this work having met the inclusion and exclusion criteria and passed the quality assessment.
## 3 Methodology
### Blending Fusion Prediction Model Building  
To address the issue of inaccurate recognition of the failure modes of STRC shear walls by a single model, this study employs the Blending fusion framework to fuse multiple ML models to improve overall performance. Blending is an ensemble learning fusion framework that allows for the flexible combination of base learners and meta-learners, using disjoint datasets for training different layers, thus avoiding the problem of data leakage [42].  
Blending is defined as multiple base learners are known (\(g_{1},g_{2},g_{3},\ldots,g_{t},\ldots,g_{n}\)), and each \(g_{t}\) has the same weight. For the classification problem:  
\[G(x)=\operatorname*{argmax}_{1\leqslant k\leqslant K}\sum_{t=1}^{T}[g_{t}(x)=k] \tag{6}\]  
where \(G(x)\) represents the fused model, and \(K\) denotes the label category value. The base learners in the Blending framework should have good predictive performance but differ in the model structure. This is because models with good predictive performance can enhance the overall prediction accuracy of the ensemble model, while the diversity among models determines the generalization ability of the ensemble model. On the other hand, the meta-learner should be relatively simple to avoid model overfitting. Therefore, in this study, within a two-layer Blending framework, Catboost, RF, and KNN are chosen as base learners, while LASSO is selected as the meta-learner, resulting in a multi-model fusion model named the Blending-CRKL model. The process flowchart for this fusion model in predicting the damage pattern of STRC shear walls is shown in Figure 7, with the specific steps outlined as follows:  
1. Dataset segmentation and GAN augmentation. In this study, the initial collected STRC shear wall database was randomly divided into a 70% training set and a 30% testing set. The training set was used for model training, while the testing set was used for model validation. To fully train the model, the 242 new samples generated by GAN were merged with the training set samples, resulting in a new training set containing 347 samples. And 10% of the samples of the new training set are randomly selected as the validation set.
2. Training process of base learners. The Blending fusion model utilizes Catboost, RF, and KNN models as base learners, training them separately on the new training set and making predictions on the validation and test sets. The predicted values of the validation set by the Catboost, RF, and KNN models are used as the training set for  
Figure 6: GAN generator and discriminator network structure: (**a**) generator; (**b**) discriminator.  
the next layer, while the predictions on the test set are used as the test set for the next layer.
3. Training process of the meta-learner. In the training process of the LASSO meta-learner, a new training set is formed using the predicted values of the base learners on the validation set, and the LASSO model is trained on this new training set. The final prediction results are obtained by testing the model on a new test set, which consists of the predicted values of the base learners on the test set.
# QoS-Aware Scheduling Algorithm Enabling Video Services in LTE Networks
## 5 Result and Discussion
### Video Throughput  
Figure 5 presents the average data rate for video streams. The use of all algorithms is associated with a rise in average throughput as the user count increases. This is due to better resource distribution equity for users. The PrOEXPRule technique is superior to the other techniques at optimising throughput. The algorithm proposed in this research has  
Figure 4: Packet Delay of Video Flow.  
Figure 5: Packet throughput for video traffic.  
been verified to increase QoS fulfilment to satisfy user needs. When the user count is in the 5-20 range, the proposed algorithm reached 465,873.44000 bps, meaning that there is a sharp increase in average data rate that gradually decreases as the user count approaches the 20-25 range. When more than thirty users are connected to the network, the data rate using the proposed PrOEXPRule increased to 2,855,111.65333 bps. The corresponding figures for EXP-PF, PF, EXPRule, and MLWDF are 1,265,368.66667, 449,340.96000, 2,017,684.76000, and 1,707,329.85333 bps, respectively.
## 3 Results
### Scaffold's Geometrical Parameters and Main Findings
#### 3.5.7 Biomechanical Properties
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Studies** & **Methods** & **System Used** & **Main Findings** \\ \hline \multirow{9}{*}{[PERSON] et al. 2009 [33]} & Compression and bending tests of Ca-P ceramics implanted in the muscle for 8 weeks. The same mechanical tests were also performed on the same BCP ceramics, which were not implanted in muscle as a control. The samples were implanted from the femoral muscles, and the surrounding soft tissues were carefully stripped. The sample was loaded to failure at a loading rate. Electrical resistance strain gauges were mounted on two lateral surfaces to measure longitudinal elastic strain in compression. The loading rate was 1 mm/min. The three-point bending test was conducted at a crosshead speed of 0.5 mm/min. \\ \hline \multirow{9}{*}{[PERSON] et al. 2007 [35]} & Three-point bending test on repaired mandibles, comparing the groups with BMSCs/b-TCP and autograft. Load was applied in the occlusal-to-interior direction to the midpoint of the unsupported length, approximately in the middle of the repaired defect. Deformation was measured using a strain gauge at a loading rate of 0.5 mm/min. The bending stress was calculated with the equation, bending stress \(\%\)3 LF \(\sim\) 02 WT2p, where L, F, W, and T are the test span (mm), the bending strength load (KN), the width (mm), and the thickness of the specimen (mm), respectively. [PERSON]’s modulus was achieved from the linear region of the stress-strain curve as the slope. \\ \hline \multirow{9}{*}{[PERSON] et al. 2019 [30]} & Pullout and removal torque tests were performed to evaluate the mechanical fixation of the implants in BCP and HA scaffolds. The testing was performed using a compression speed of 1 mm/min. \\ \cline{1-1}  & Abbreviations: PLGA = Poly(lactic-co-glycolic acid); TCP = tricalcium phosphate; BMP = Bone Morphogenetic Protein; TCP-BMP = Ircalcium phosphate with Bone Morphogenetic Protein; DPNCC = Adipose-derived nucleus cells; HA/TCP = hydroxyapatite/tricalcium phosphate; INSTRON = brand of testing system; kN = Kilonewton; lbf—Pounds-force; BCP = biphasic calcium phosphate; BMSCs = Bone Marrow Gent Cells; b-TCP = beta-tricalcium Phosphate; KN = Kilonewton; N = Newton; mm/min = millimeters per minute; MPa = Megapascal. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Table 6 shows a comparative summary of bone regeneration findings by animal model and defect site.
## 3 Results
### Experimental Data Analysis
#### 3.4.3 Ablation Experiment  
To assess the impact of each individual module, this study conducts a series of ablation experiments for comparative evaluation, providing valuable insights and methods for understanding the role of specific modules or combinations of modules within the overall model.  
The outcomes achieved by the ablation investigations are presented in Table 1. The changes in evaluation metrics such as mean average precision (mAP), precision (P), recall (R), and F1-score for the YOLOv9 baseline model and the proposed improved model for deepfake face detection are shown in Figure 8.  
Building upon the YOLOv9 baseline model, this paper updates the feature extraction module and adds the CBAM attention mechanism to the backbone architecture, resulting in the YOLOv9-ARC deepfake face detection model. Enhancements to the feature extraction module improve the network's capability to comprehend relevant features, reducing the adverse effects of noise, artifacts, and other elements present in low-quality face images within the dataset. Although this modification slightly decreases precision (P), recall (R), and F1-score, the mean average precision (mAP) improves by 2.1%.  
Additionally, by adding the CBAM attention mechanism to the baseline model, the detection model places greater emphasis on key information in fake face images, reducing the weight given to irrelevant information. As a result, the mAP, precision (P), recall (R), and F1-score are improved by 1.4%, 6.8%, 8.3%, and 7.5%, respectively.  
In addition to performance improvements, efficiency metrics also reflect the advantages of the proposed model. Specifically, the YOLOv9-ARC model reduces the inference  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **mAP** & **P** & **R** & **F1** & **Inference Time (ms)** & **FLOPs (G)** \\ \hline YOLOv9 & 0.716 & 0.738 & 0.873 & 0.800 & 45 & 130 \\ \hline YOLOv9-AKConv-RepNCSPELAN4 & 0.737 & 0.716 & 0.842 & 0.773 & 38 & 126 \\ \hline Ours (YOLOv9-ARC) & 0.751 & 0.784 & 0.925 & 0.849 & 32 & 128 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation experiment results with efficiency metrics.  
Figure 8: **Experimental results graph.** This diagram shows the changes in evaluation metrics, such as average accuracy, precision, and recall, for the YOLOv9 baseline model and the improved model proposed in this paper, applied to the forged face detection task.  
time from 45 ms to 32 ms per image, indicating a 28.9% improvement in processing speed. Despite the architectural enhancements, the FLOPs only slightly decrease from 130 G to 128 G, demonstrating that the model maintains computational efficiency while delivering stronger detection performance.  
Through the improvements in these aspects, the detection model's accuracy in detecting deepfake faces is enhanced, leading to increases of 3.5%, 4.6%, 5.2%, and 4.9% in the final evaluation metrics.
# A Review of Multifunctional Antenna Designs for Internet of Things
## 2 Materials and Methods
### Smart Home Appliances  
Smart homes represent the combination of IoT technology with residential living, offering the ability to remotely control various home functions via a smartphone or computer [54]. This integration has led to remarkable progress in making our living spaces more comfortable, secure, energy-efficient, and convenient. By incorporating an array of technologies such as sensors, interactive interfaces, monitoring systems, and intelligent appliances, smart homes are interconnected environments that facilitate both automated and manual management of domestic activities [54]. These technologies, controlled by advanced information and communication systems and enhanced by machine learning algorithms, allow smart homes to analyze the behaviors and preferences of their inhabitants. This capability enables the home environment to adapt its operations to the specific needs and habits of its residents, thereby elevating their overall quality of life. Furthermore, smart homes are adept at optimizing energy consumption, ensuring that appliances and other household features operate more efficiently [55]. This not only contributes to a health-ier and more eco-friendly living space but also promotes a more sustainable approach to residential living.
# Modeling Driver's Real-Time Confidence in Autonomous Vehicles
## 4 Test and Evaluation
### Scenario Two  
The background of scenario two is the same as scenario one. Scenario two was built to verify the quantification of driving confidence of the VC model when vehicles change lanes frequently. So, the rest of the traffic participants are operating in a steady state to reduce the impact on the ego.  
Figure 5: Real-time VC value in scenario one.  
Figure 6: Verification score for scenario one.  
The ego vehicle takes the first lane change action at simulation time 10, and the lane change causes a current decrease in driving confidence. Furthermore, the vehicle stays in the current lane for about 200 simulation time. During this period, the confidence value successfully recovers to 100. This part of the result demonstrates that a single general lane change will not seriously impact driving confidence. Then the following behavior of the ego is set in violation of the usual regulations. It takes five continuous lane changes from 240 to 400 intervals 40 simulation time. Although the confidence value will start restoring at the end of each action, its recovery cannot catch up with the fast and frequent changing lane actions. The driving confidence suffers a major shock after the series of actions down to around 40. The simulation result of scenario two can be seen in Figure 7. The brown and red lines in the figure represent the same meaning as the result of scenario one. The brown line is for normal environmental conditions and the red is for rain and low visibility.  
As can be seen in Figure 7, each lane change produces the same decrease in confidence according to Equation (15) under the same condition. Observe the result separately, significant differences can be found in the recovery rate for different confidence value states. Comparing the two results reveals the same phenomenon as the previous result that the complexity of the environment plays a role in determining changes in driving confidence to some extent. The verification score from 15 volunteers can be seen in Figure 8.  
Both the results of Figures 6 and 8 indicate to a certain extent that the VC model's simulation of driver confidence values possesses a degree of credi  
Figure 8: Verification score for scenario two.  
Figure 7: Real-time VC value in scenario two.  
ble to obtain the driver's actual confidence state, the model is only an abstract numerical reflection. Based on the feedback from the testers regarding the model's performance, we believe it can be further optimized. Additionally, as the testers all have limited experience with autonomous driving, the model may largely reflect the confidence state of passengers who have just started using autonomous vehicles.
# Temperature Impact on Engineered Cementitious Composite Containing Basalt Fibers
## 3 Result and Discussions
### Results of the Indirect Tensile Strength Test  
A tensile test was performed to verify the crack opening performance under the complex stress state. The splitting tensile strength of the fiber-reinforced and control cylinder specimens was made according to the ASTM C496 standard. The specimens were  
Figure 4: The result of the change in compressive strength of fiber-reinforced and fiber-free fibrous specimens exposed to elevated temperatures up to 400 \({}^{\circ}\)C.  
tested after exposure to five temperature conditions: room temperature, 100 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, 300 \({}^{\circ}\)C, and 400 \({}^{\circ}\)C. The tensile strength of the BF-reinforced and control samples were 4.25 MPa and 3.1 MP, respectively. The results of the tensile strength tests, for the control and fiber-reinforced ECC mixes, are illustrated in Figure 5. The results in each point were obtained from the average of triplicate specimens.  
Figure 5 shows that BF-reinforced ECC had a higher tensile strength than the control samples. Higher tensile strength was achieved due to the addition of fiber resulting in advantages such as an increased ductility, better crack resistance, and energy absorption capacity. With the increasing temperature up to 300 \({}^{\circ}\)C, the tensile strength increased, while a significant reduction in the strength capacity was recorded when the temperature was raised from 300 to 400 \({}^{\circ}\)C. Quite a similar pattern was observed for the tensile strength of control and BF-reinforced ECC. From the obtained results in Figures 4 and 5, it can be observed that the rate of increase in compressive strength and tensile strength due to the elevated temperature did not follow the same trend despite their correlation [84].  
The results show that the tensile strength of samples containing BFs was higher than samples without fibers at all temperatures tested. In addition, the largest difference in tensile strength between the control and BF-reinforced samples was at 300 \({}^{\circ}\)C. The increase in the tensile strength of fiber-containing samples, compared to control ones, could be due to the phenomenon of fiber bridging and the combination and friction of fiber combinations. With increasing temperature up to 300 \({}^{\circ}\)C, hydration of waterless cement grains improved due to the internal autoclave conditions. No microcracks were observed in the interfacial transition zone (ITZ) of the ECC samples up to 200 \({}^{\circ}\)C. As the temperature rose to 400 \({}^{\circ}\)C, the width and length of microcracks were found to greatly increase in intensity. The reason for the formation of microcracks was that the cement composition shrunk with water loss, while the aggregates expanded due to increasing temperature resulting in crack generation in the ITZ.
# An Intelligent Access Channel Algorithm Based on Distributed Double Q Learning
## 4 Simulation Results and Discussion  
### Parameter Settings  
We set the parameters related to simulation in Table 1 as follows:  
Based on the simulation setup, we set up the five channels and 50 subframes. The jammer occupies the three channels every time. Each subframe has 100 slots. The above assumes each time slot transmission of a packet and each user has a 100-packet transmission. Therefore, the user in a MAC frame only need to finish the communication to successfully transmit a subframe. The channel number is far less than the number of users. Users can also communicate.  
In this paper, simulation experiments are carried out to verify the performance of the proposed algorithm (DDQL) under traditional interference linear frequency sweep interference and multi-channel intelligent blocking jamming. As shown in Figure 2a, linear frequency sweep jamming is a dynamic jamming that periodically scans the target channel according to the MAC frame by narrow-band strong power jamming signals. It is simple and efficient, and widely used in the actual battlefield environment. The number of simultaneous blocked channels of the parameter jammer channels \(Z\) is 1. As shown in Figure 2b, the multi-channel jamming blocking channel is an upgrade of linear frequency sweep interference. The jammers can sense the channel used by the communication users in the last transmission subframe, so as to focus on interference in the next subframe, and take the MAC frame as periodic interference, and increase the number of jamming channels \(Z\) to 3.