esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Power Rectennas in Microwave Wireless Power Transmission
## 3 Low-Power-Density Rectenna
### High-Gain Antenna Design  
The theoretical received power \(P_{r}\) at the rectenna can be derived from the Friis transmission equation [78]:  
\[P_{r}=G_{t}\cdot G_{r}\cdot\left(\frac{\lambda}{4\pi d}\right)^{2}\cdot P_{t}. \tag{1}\]  
The environmental RF energy is typically predetermined in ambient energy harvesting. The higher the gain \(G_{r}\) of the receiving antenna, the greater the power delivered to the rectifier, resulting in higher efficiency. Therefore, designing a high-gain antenna is one of the main approaches to improve rectification efficiency.  
The larger the effective aperture of an antenna, the higher its gain; antenna array techniques can be used to obtain greater gain. Figure 13 shows a novel planar substrate-integrated waveguide (SIW)-fed aperture-coupled circularly polarized complementary 4 \(\times\) 4 antenna array, achieving a peak gain of 23.4 dBic and a 3 dB gain bandwidth of 22.8% in the millimeter-wave band [79]. A convex optimization formulation for electrically small antenna arrays with reactively loaded parasitic elements is provided in [80]. In this approach, the objective function is to maximize the antenna gain, while the input admittance is used as a constraint to establish a standard convex optimization problem. The conditions for achieving super-gain are analyzed for different array configurations with various combinations of parasitic elements. A millimeter-wave rectenna system based on a Rotman lens was first proposed in 2019, consisting of antennas, rectifiers, a Rotman lens, and a power combining network. Compared to conventional designs, the Rotman rectenna achieved a 21-fold increase in harvested power while maintaining the same angular coverage, thus simultaneously realizing high gain and wide beamwidth [81].  
Figure 12: A three-dimensional view of the rectifier based on the SISL platform [16].
# Group Pile Effect on Temperature Distributions inside Energy Storage Pile Foundations
## 2 Research Background
### Group Pile Foundation  
In typical deep foundations, a structural column is supported on a group of piles that are joined to a solid cap (see Figure 2). The behaviors of the single pile and the group pile are usually different due to the pile-to-soil interaction. Therefore, the term \"group pile effect\" is introduced to refer to the influence of piles on the behavior of nearby soils and piles. The group pile effect varies with the pile spacing (s) which is the center-to-center distance between adjacent piles. Several analytical and experimental studies were conducted to evaluate the mechanical behavior of the group pile and the pile-soil interaction [22; 23; 24]. In practice, the spacing is determined based on factors such as group pile efficiency, overlapping stresses, and cost of foundation [25]. Typically, it is recommended to use a minimum of 2.5 times the pile diameter as the pile spacing [26]. For the pile foundation proposed here, the group pile effect is expected to influence the temperature magnitude and distribution in both the pile section and the soil. As illustrated in Figure 2, each pile is first heated at the inner surface by compressed air (Figure 2a). Then, the heat starts to transfer outwards and gradually penetrates through the concrete section and soil (Figure 2b). After the heat penetration reaches the centerline between two piles (Figure 2c), the group pile effect starts to be activated and influences the temperature distribution of the soil and eventually the concrete pile on the other side (Figure 2d). Considering the hollowel pile geometry, this paper studies and quantifies the effect of group piles with 1-m outer diameter for pile spacing ranging from s = 2 m to 7 m.
# Efficiency of Using a Simulator for Ultrasound and Laser Dose Calculation in Physiotherapy
## 4 Discussion
### Strengths and Limitations  
In our opinion, one of the key elements of this study's results lies in how the information on the topic was conveyed to the students, that is, face-to-face, and which was rated as \"good\" or \"very good\" in the final survey. Such rating eliminates any potential bias arising from interferences in the information and communication process to the students regarding the learning process for problems resolution, as well as using the simulator (verbal communication). Nevertheless, practicing with the simulator instead of having access to the professor (non-verbal communication) may have hindered students during the thinking process for solving calculation problems. This may be due to a potential deficiency in simulator's design [19; 20]. As a consequence, and despite some studies supporting the prevalence of simulated training compared to traditional training [21], we believe the efficiency of this learning method is based on a combination of traditional elements, such as the teacher's face-to-face presentation of the topic [19], which have been complementedby a more digitalized practice aided by the simulator [22]. Further, we consider that this combination may not replace real practice [23], but it may become a good introduction to it [24].  
A prudent analysis of the results recorded is recommended, since the number of participants was extraordinarily small, as well as the year in which it was applied (the first year of the undergraduate degree). An increased number of subjects taking part in the study is recommended, if possible, from different years of the same degree, as well as the incorporation of several teachers into the information conveyance stage.
## 2 State-of-the-Art and Related Topics
### Urban Traffic Applications  
Current traffic information and predicted traffic information are the base data of ITS, but they are meaningless without practical applications. Route planning and vehicle navigation systems are some of the most popular applications that use real-time and predicted traffic information [35]. The first-generation route planning system only considers the static features of the road network to obtain the shortest path. With the development of real-time traffic information collection technology, dynamic route planning has been proposed to re-calculate the new shortest path with the updated real-time data at each intersection. Such a system provides better travel planning when compared with the static route. However, using only current data may lead to frequent route changes in complex traffic conditions. The newest route planning systems use the predicted traffic information to arrive at the best and most stable route to the destination.  
The other main applications using current and predicted traffic information are traffic management applications. The most common usage is to display current traffic states, the predicted traveling time to land mark locations, and traveling proposals on the traffic information board. Withthis information, drivers can re-plan their travel accordingly. Adaptive traffic signal control is the key technology of traffic management. The reactive traffic signaling control system adjusts the signal phase and cycle lengths according to current traffic data. The predictive traffic signal systems retime the traffic sign according to the predicted traffic information. With the predictive approach, the total waiting time of vehicles is reduced and the efficiency of the traffic network is improved [36].  
Predicted traffic information is not only used by navigation systems and traffic management systems, but also by other urban applications such as parking management and so on. For the wide usage of traffic prediction, there are continuous research interests in this field.
# Binaural Auralization of Room Acoustics with a Highly Scalable Wave-Based Acoustics Simulation
## 3 Scalability of Wave-Based Room-Acoustics Solver on an HPC Cloud Environment
### Results and Discussion  
First, we show how fast the present parallel wave-based solver enables us to compute room acoustics. Figure 5a presents the computational times for Problems 1 and 2 when using 1-64 nodes with the cloud HPC environment. For each problem, the results show the computational times on the time-marching scheme part (Time-marching) and all computational parts (Total). The wave-based solver presents a marked computational performance for both problems. The total computational times are only 217 s and 613 s for Problem 1 and Problem 2 when using 64 nodes. The computational time of Problem 2 is about 2.8 times longer than that of Problem 1. The computational time ratio corresponds to the ratio of the problem size. The wave-based solver is practical even using the baseline node: The computational times are 10,360 s and 14,555 s, respectively, for Problems 1 and 2. Additionally, the computational times of the time-marching scheme part and all computational parts are almost identical up to the use of 16 nodes. The computational time difference between the time-marching scheme and all computational parts is visible when using 32 and 64 nodes. The current code of the parallel solver loads a surface element mesh file by all computation processes simultaneously. It causes conflict among processes and delays in file loadings. Resolving the conflict is an issue that demands future study.  
Figure 5b presents the speedup \(S\) in both problems, including ideal speedup values evaluated as \(X/b\). For both problems, the speedups in the time-marching scheme parts increase linearly, fitting well to the ideal \(S\). When using 64 nodes, the speedup values for Problems 1 and 2 are, respectively, 59.7 and 31.5, which are close to their ideal values of 64 and 32. On the other hand, speedups on all computational parts were, respectively, 47.8 and 23.1 for Problems 1 and 2 when using 64 nodes because of the conflict in surface element mesh loading explained above. Although the conflict engenders slight performance reduction, the parallel performance remains high. We conclude that the present parallel wave-based solver which uses a cloud HPC environment is an effective and practical room-acoustics simulation tool.  
Figure 4: Coefficients of variance for distribution of DOF and communication data per communication among all subdomains for each parallel condition.  
Figure 5: Results of scalability tests of (**a**) computational time and (**b**) speedup \(S\).
## 2 Materials and Methods
### Test System Design and Test
#### 2.3.6 Evaluation Indicators  
For this test, precision, recall, and average precision mean (mAP) were selected as the indices to be used in evaluating the detection of tea buds. These indices are usually applicable to boundary box-based recognition algorithms and have been commonly used to evaluate the results of YOLO algorithms [15]. The closer that the values of these three indices are to 1, the better the performance.  
1. Precision represents the accuracy of the prediction result and, in this case, represents the probability that the tea image predicted to be positive will actually be positive, as shown in Equation (1) as follows: \[precision=\frac{TP}{TP+FP}\times 100\%\] (1)
2. Recall is used to measure the coverage of tea bud data, indicating the proportion of tea images actually identified as positive examples among those identified as positive examples by the model, as a percentage of the total number of positive examples in the data set, as shown in Equation (2). \[recall=\frac{TP}{TP+FN}\times 100\%\] (2)
3. mAP stands as one of the primary metrics for assessing the performance of object-detection algorithms. It integrates the accuracy and recall of various classes and computes their average values, as demonstrated in Equation (3). It is important to note that mAP can be calculated under different IoU (Intersection over Union) thresholds. For instance, [EMAIL_ADDRESS] refers to the mean average precision computed at an IoU threshold of 0.5, while [EMAIL_ADDRESS]:0.95 represents the average of mean averageprecisions calculated at multiple IoU thresholds ranging from 0.5 to 0.95 with a step size of 0.05. This methodology provides a more comprehensive evaluation of the performance of object-detection algorithms.  
\[mAP=\frac{\sum_{i=0}^{n}AP(i)}{n} \tag{3}\]  
In our model analysis, the loss function gauges the gap between predictions and actual labels. We used four key loss functions in object detection: Box loss (comparing predicted and actual bounding boxes), Validation (Val) Box loss (same as Box loss but on the validation set), Objectness loss (assessing the accuracy of object presence predictions), and Validation Objectness loss (similar to Objectness loss, but on the validation set). These functions output non-negative values, quantifying prediction errors. Lower loss values indicate higher prediction accuracy and model robustness.
# GPS/BDS RTK Positioning Based on Equivalence Principle Using Multiple Reference Stations
## Validation and Analysis
### Kinematic Experiment
#### 3.2.2 Positioning Results of Different Satellite Systems  
To evaluate the performance of MBRTK for different satellite systems, the number of observed satellites and redundancy for the scheme-three, scheme-four and scheme-five are shown in Figure 8. In this contribution, G and C are denoted as system identifications for GPS and BDS, and R- is denoted as redundancy. The number of GPS and BDS satellites observed at one epoch over the whole observation segment is more than 6 and 9 mostly, and the total number of GPS/BDS satellites is more than 15. Meanwhile, the average redundancy is 7.6, 13.1, and 26.7 for the three different schemes, respectively. We can see that combined GPS/BDS system can significantly improve the redundancy of kinematic positioning, indicating combined GPS/BDS positioning has a strong ability to test the observations for modeling errors and improve the positioning accuracy.  
Figure 9 shows the positioning errors of TBR9 after the first convergence for scheme-three, scheme-four, and scheme-five in N, E, and U components, respectively, and the success rate is marked on the graph. The statistics of three schemes are listed in Table 5. The RMS values of three different schemes are (5.5, 2.3, and 6.3) cm, (3.4, 1.6, and 5.7) cm, and (3.0, 1.5, and 4.6) cm in N, E, and U components, while the corresponding standard deviation (STD) values for three schemes are (3.7, 2.3, and 5.5) cm, (2.5, 1.5, and 4.7) cm, and (1.5, 1.4, and 3.9) cm. We can conclude that both the RMS and STD values of combined GPS/BDS system has a better positioning accuracy. Compared with RMS values of scheme-three and scheme-four, the RMS values of scheme-five have a performance improvement of approximately (45%, 35%, and 27%) and (12%, 6%, and 19%) in N, E, and U components, respectively. The STD values of scheme-five also have better consistency than those of scheme-three and scheme-four. Besides, the success rate of scheme-five is 94.4%, which has an availability improvement by approximately 33% and 10% compared with the success rate of scheme-three and scheme-four, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Schemes** & **Reference Stations** & **Systems** & **Model** \\ \hline One & SDWA & GPS + BDS & SBRTK \\ Two & SOTH & GPS + BDS & SBRTK \\ Three & SDWA + SOTH & GPS & MBRTK \\ Four & SDWA + SOTH & BDS & MBRTK \\ Five & SDWA + SOTH & GPS + BDS & MBRTK \\ Six & SDWA + SDWB + SOTH & GPS + BDS & MBRTK \\ \hline \hline \end{tabular}
\end{table}
Table 4: The description of six positioning schemes.  
Figure 7: The car trajectory of kinematic experiment for three reference receivers.
# TRAP: A Three-Way Handshake Server for TCP Connection Establishment
## 2 Background
### SYN Flood  
In the classic server-client TCP connection establishment during the three-way handshaking process, the client will have to send a SYN packet to the server. When a server receives a SYN packet from a client, it reserves its memory for the establishment of a TCP connection with the client, and then sends back a SYN-ACK packet.  
As the resource (e.g., kernel memory, network adapter cache) on the server is limited; if a client never sends back an ACK packet, the allocated memory cannot be recycled or reused by any other clients before the timeout occurs. If a server receives a large amount of SYN packets from many other clients, but not any SYN-ACK packets, it will exceeds a server's resource limit, causing it to be unable to handle any new clients. We call this scenario a SYN flood attack. Figure 2 is an example illustration of a SYN flood attack scenario.  
Figure 1: TCP three-way handshake diagram. TCP, Transmission Control Protocol; SYN, Synchronize; ACK, Acknowledgement.  
Moreover, a malicious attacker can send forged SYN packets with fake IP addresses (IP spoofing), hiding it from the server. With its source IP address changing, traditional methods like blocking the offending IP address will have little effects.  
Among all forms of DDoS attacks, a SYN-flood attack is the most prevalent one, because both are easy to implement and have a low cost to perform [11]. Moreover, it is, by standard, difficult to mitigate due to the design flaws in the TCP standard.
# Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review
## 1 Introduction
It introduces various vegetation indices, such as NDVI, EVI, SAVI, LAI, and others, that aid in assessing the well-being of vegetation [9], detecting stress factors [10], and contributing to sustainable land management practices. Furthermore, this review also delves into the utilization of remote sensing for crop yield estimation and drought condition monitoring, with specific emphasis on the manifold drought indices and their diverse applications. Overall, this comprehensive review provides a comprehensive overview of the Argane forest stands, remote sensing technology, and their multifarious applications in mapping and monitoring vegetation health and assessing productivity and susceptibility to drought-induced stress. It lays the groundwork for an in-depth investigation into the utilization of remote sensing for the examination of _Argania spinosa_ in its ecosystem.
# Efficient Retrieval of Music Recordings Using Graph-Based Index Structures
## 2 Music Retrieval Application
### Motivating Retrieval Scenario  
In our article, we deal with a query-by-example retrieval scenario using a real-world music collection from a music publisher. This collection consists of the complete audio catalog of the Carus publishing house, a leading sheet music publisher of sacred and secular choral music. Beyond sheet music, Carus also produces and publishes audio recordings, mainly for choral pieces of Western classical music. Carus' complete audio catalog is a medium-sized music collection of nearly 400 h (more details in Section 2.3). Internally, we implemented a web-based interface for browsing this dataset, as illustrated by the screenshot shown in Figure 1. Here, the user can specify a query in the form of a YouTube link of a music recording, e.g., an interpretation of [PERSON]'s _Verleih uns Frieden_ (_Grant us peace_) by an amateur choir. Such YouTube videos are often poorly annotated. Therefore, in our scenario, we use a content-based retrieval approach, where we take a 20-s audio snippet from the YouTube recording as a query. Then, the system retrieves recordings from the Carus collection that are based on the same musical piece as the query. Following [9; 17], we denote different recordings of the same piece of music as \"versions.\" In the case of the [PERSON] piece, the retrieval system returns two CD articles from the Carus catalog, which both include a version of that piece by professional musicians (the Kammerchor Stuttgart under the direction of [PERSON]). The user may listen to the retrieved versions or click on the cover images to access more information on the linked webpage of the publisher. Rather than describing this web-based tool in further detail, it serves as a motivating scenario for our retrieval experiments while indicating our study's practical relevance.  
Figure 1: Illustrated screenshot of an internal version-retrieval web-interface for the Carus catalog.
## 4 Experiments
### Results and Analysis  
#### 4.2.1 Overall Results  
The overall results of our proposed model and baselines are presented in Table 3. The results show that the base model outperforms most of the baselines, while the full model further improves the performance. Our full model \(GCNTRec_{s}\) improves the performances by 0.094, 0.053, 0.038, 0.02, and 0.02 AUC points compared to the best results in the baselines for the MOOCs, Book-Crossing, Movielens-1m, Steam, and Amazon Beauty datasets, respectively. Furthermore, for the other metrics, our full model also surpasses previous best models by a significant margin. We used critical difference diagrams for all three metrics. The critical difference diagrams for AUC, precision and \(F_{1}\) are shown in Figures 3-5. From these three critical difference diagrams, we can clearly see how on average our full model had the best algorithms over the five datasets.  
GCNTRec and its variant methods based on the same dataset have different experimental results, and their results all roughly show the same trend, while \(GCNTRec_{g}<GNTRec_{gt}<GONTRec_{s}\) and \(GCNTRec_{g}\) has better results than other comparison algorithms in some datasets, reflecting that the graph convolutional neural network has a good extraction effect on the feature of the item. The result \(GCNTRec_{g}<GCNTRec_{gt}\) shows that the model combined with the transformer encoder further extracts the implicit connections between items in the sequence. The \(GCNTRec_{gt}<GONTRec_{s}\) shown in the experimental results further illustrates the improvement effect brought by adding the item degree encoding which makes the model have a stronger expressive ability to generate a more efficient node representation vector.
# NTS-YOLO: A Notcurnal Traffic Sign Detection Method Based on Improved YOLOv5
###### Abstract  
Accurate traffic sign recognition is one of the core technologies of intelligent driving systems, which face multiple challenges such as insufficient light and shadow interference at night. In this paper, we improve the YOLOv5 model for small, fuzzy, and partially occluded traffic sign targets at night and propose a high-precision nighttime traffic sign recognition method, \"NTS-YOLO\". The method firstly preprocessed the traffic sign dataset by adopting an unsupervised nighttime image enhancement method to improve the image quality under low-light conditions; secondly, it introduced the Convolutional Block Attention Module (CBAM) attentional mechanism, which focuses on the shape of the traffic sign by weighting the channel and spatial features inside the model and color to improve the perception under complex background and uneven illumination conditions; and finally, the Optimal Transport Assignment (OTA) loss function was adopted to optimize the accuracy of predicting the bounding box and thus improve the performance of the model by comparing the difference between two probability distributions, i.e., minimizing the difference. In order to evaluate the effectiveness of the method, 154 samples of typical traffic signs containing small targets and fuzzy and partially occluded traffic signs with different lighting conditions at nighttime were collected, and the data samples were subjected to the CBAM, OTA, and a combination of the two methods, respectively, and comparative experiments were conducted with the traditional YOLOv5 algorithm. The experimental results showed that \"NTS-YOLO\" achieved a significant performance improvement in nighttime traffic sign recognition, with a mean average accuracy improvement of 0.95% for the target detection of traffic signs and 0.17% for instance segmentation.  
YOLO; deep learning; traffic road signs; detection; recognition +
Footnote †: journal: Applied sciences