esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Models for Composite Frames with Simple Connections
### Validation Studies
#### 2.2.1 Bolted-Angle Connections Test  
The simple connections resist progressive collapse through catenary action, and therefore the collapse robustness of the gravity frames relies largely on the tensile behavior of the simple connections. [PERSON] and [PERSON] (2016) tested 14 bolted-angle connections under tension in order to address this issue. Each specimen comprised 4 angles and a number of bolts (12 for Type A specimens and 18 for Type B and C specimens). The angles were connected by the bolts through thick steel plates, which were not allowed to fail since focus was placed on the behavior of angles and bolts. The thick plates in the vertical direction were also used as loading plates, which were clamped to the testing machine and to which a monotonic tensile force was applied.  
Specimen A90-8-50-I, in which web cleat connections were considered, was selected for validation study because all the parameters which may have a significant impact on the tensile behavior of the web cleat connections in A90-8-50-I and the prototype structure were identical, including the size of the angles (L90 \(\times\) 8) and bolts (Grade 8.8 M20) and the distance between the centerlines of horizontal bolts and the vertical loading plates (50 mm). The yield and ultimate strengths of the angles were 323 MPa and 473 MPa, respectively. A numerical model of specimen A90-8-50-I was developed using the proposed modeling approach, as shown in Figure 3b, and the same loading scheme and boundary condition with the experimental test were adopted when performing the simulation. The comparisons between the experimental and simulation results, including the load-displacement curves and failure modes, are shown in Figure 4. As shown in Figure 4a, the model yields good overall load-versus-deflection responses, managing to capture the initial slopes, hardening attributable to catenary action, peak loads, and deformations at failure. Furthermore, Figure 4b reveals that the proposed model also captures the observed failure mode well, including the deflections and fracture of the angles. Therefore, the proposed model is able to represent the behaviors of the web cleat connections subjected to monotonic tensile force well.
# Design of a Fixed IF Down-Conversion Double-Balanced Mixer for UHF Band Applications
## 4 Simulation and Experimental Results
### Double-Balanced Down-Conversion Mixer Noise Calculation and Simulation
#### 4.2.3 Noise Due to Load  
Noise due to load \(R_{L}\) is \(4 KTR_{L}\) and the output is differential; total thermal noise down-converted to IF will be as follows:  
\[\overline{V_{n,R_{L}}^{2}}=8 KTR_{L}. \tag{24}\]
## 3 Node Distribution with LoS Links
### Non-Homogeneous Poisson Process and Outage  
Suppose that there are \(N\) nodes in the transmission area \(\mathcal{A}\). Since the \(N\) nodes are uniformly distributed in \(\mathcal{A}\), when the distance from a transmitter to Node \(i\) (\(\in\{1,...,N\}\)) is \(r_{i}\), its probability density function (PDF) is given by  
\[f_{r_{i}}(x)=\frac{2x}{R^{2}}, \tag{4}\]  
where \(0\leq x\leq R\). We define a Bernoulli random variable that indicates the existence of an unblocked LoS path between the transmitter to Node \(i\):  
\[U_{i}=\begin{cases}1,&\text{w.p. }P_{\text{LoS},i}=e^{(-\beta r_{i})},\\ 0,&\text{w.p. }1-P_{\text{LoS},i}=1-e^{(-\beta r_{i})}.\end{cases} \tag{5}\]  
When \(U_{i}=1\), the mmWave link to Node \(i\) is blockage-free and reliable, and Node \(i\) is called a _LoS node_. In contrast, \(U_{i}=0\) means an outage of the link. We note that the blockage events are assumed to be independent for different links (i.e., \(U_{i}\) and \(U_{j}\) are independent for \(i\
eq j\)), as in [29; 30; 31; 44; 45].  
While the spatial distribution of nodes follows the homogeneous Poisson process with intensity \(\lambda\), the distribution of the LoS nodes is modeled by _non-homogeneous Poisson process_ (NHPP) [48] because of the distance-dependent probability function in (5). Based on NHPP, the probability that there are \(k\) LoS nodes in \(\mathcal{A}\) is  
\[\Pr[K=k]=\frac{Q^{k}e^{-Q}}{k!}, \tag{6}\]  
where \(K\leq N\) for the total (both LoS and NLoS) number of nodes \(N\) in the area in Equation (1). In addition, \(Q\) is the intensity of the NHPP, which is a function of \(R\), \(\Phi\), \(\lambda\), and \(\beta\):  
\[Q=\int_{0}^{R}\int_{\frac{-\Phi}{2}}^{\frac{\Phi}{2}}\lambda e^{-\beta x}xd \theta dx=\frac{\Phi\lambda\left[1-(1+\beta R)e^{-\beta R}\right]}{\beta^{2}}. \tag{7}\]  
The transmitting node experiences an outage, when there is no LoS node (i.e., all \(U_{i}\)s are zero). Thus, the probability of per-hop outage, where no LoS node exists for a given transmitter, is given by  
\[P_{out}=\Pr[K=0]=e^{-\frac{\Phi\lambda}{\beta^{2}}\left[1-(1+\beta R)e^{-\beta R }\right]}. \tag{8}\]  
From the assumption of mutually independent blockage events, as in [29; 30; 31; 44; 45], we assume that the outage events in multiple hops are independent to each other.
# Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs
## 3 System Architecture
### Internet of Things (IoT) Systems  
The IoT system has cellular-like backbone network architecture as shown in Figure 1. We assume that a CR-LPWAN is composed of IoT gateway (or base station) and IoT devices (or sensor nodes), which are uniformly distributed over the network area. These IoT devices can be assumed to adjust an arbitrary transmission power level for sustaining each data transmission. We also assume that there is at least one data transmission path from any IoT device to the gateway. The gateway node eventually collects all information acquired by each of the IoT devices. The collected information would finally be sent to the data server. In CR-LPWANs, each IoT device is responsible for determining its actions based on the local event readings. The local readings on each IoT sensor node need to be exchanged between the sensor nodes and central network server in order to broaden the sensing information on the entire network.  
In addition to the event readings, IoT devices may exchange additional information with the network server. This information includes control data for spectrum allocation, and spectrum-handoff-aware channel determination, which depends on the specific topology. The typical duty cycle in CR-LPWANs, where IoT sensor devices are located geographically at random, depends on the sensing target. Path availability between the sensor nodes and the network server, and coverage intensity in CR-LPWANs can actually rely on the effective duty cycle.
# Real-Time, Deep Learning Based Wrong Direction Detection
## 2 Background
### Vehicle Tracking
#### 2.2.1 Contour-Based Tracking  
The main idea behind contour-based tracking is to define the boundaries of a vehicle. [PERSON] et al. [14] proposed pose detection of a vehicle by using optical flow and camera parameters. According to the authors, part of the system tracks the blobs and tries to correct the errors from the foreground object detection module by estimating the three-dimensional (3D) world coordinates of the points on the vehicles. The system then groups those points together to segment and track the individual vehicles. The authors emphasized that their proposed technique has occlusion detection and multi-vehicle tracking [15].
# Innovative Approach Integrating Machine Learning Models for Coiled Tubing Fatigue Modeling
## 6 Results Analysis and Interpretation
### MAPE (Mean Absolute Percentage Error)--Figure 9
* Training: XGBoost and Decision Tree have a significantly lower training MAPE (13.99 and 13.93, respectively) indicating better percentage error performance during training.
* Testing and validation: CatBoost and XGBoost demonstrate slightly better performance in testing, but in validation sets, XGBoost and Decision Tree demonstrate better performance.
## 3 Results and Discussion
Numerous authors point out that there is a threshold value for the application of biochar soil amendment when it comes to crop productivity. It was shown that the use of biochar at higher rates does not produce statistically significant effects on crop yields [64]. [PERSON] et al. [19] observed a significant increase in the mass of Virginia mallow at an early stage of growth if biochar was added to soil. Furthermore, the study showed that a combination of biochar and ash from biomass, added at an optimum rate, improves the germination capacity of seeds, while increasing the potassium content of the overground parts of plants and reducing their concentrations of phosphorus and calcium [19]. Biochar has been also studied as method to reduce the greenhouse gas emissions from agriculture soils [65; 66; 67]. [PERSON] et al. [66] found that biochar reduced soil N\({}_{2}\)O emissions by 54% in laboratory and field studies. The biochar feedstock, pyrolysis conditions and C/N ratio were shown to be key factors influencing emissions of N\({}_{2}\)O while a direct correlation was found between the biochar application rate and N\({}_{2}\)O emission reductions. Table 5 presents the results reflecting the total contents of selected macro- and micro-elements in overground parts of Virginia mallow plants, in both the control and experimental samples, 14 days after the experimental culture was established. In each case the samples analysed were characterised by having the highest potassium, calcium and phosphorus contents. The lowest levels of macro- and micro-elements were identified in plants grown from seeds conditioned with biochar. This may have been caused by the faster development and intensified metabolic processes, associated with a greater nutrient requirement, at the early stage of growth [36; 68]. The samples differed most significantly in their potassium content. Potassium is absorbed extremely rapidly by plants, second only to nitrogen, though by young ones possessing the fast-growing meristematic tissue responsible for the development of roots and stems in particular. This element is an essential plant nutrient; in the case of a potassium deficiency it is sent to apical meristems and young leaves first of all. At the early stages of plant development, phosphorus is necessary for the normal growth of the root system, and calcium is an important factor in both the regulation of cellular metabolism and structural functions, as well as being a universal carrier of information [68; 69; 70; 71].
## 4 Self-Healing Approaches
### Machine-Learning Algorithms
#### 4.3.1 Monte Carlo Tree Search  
Monte Carlo tree search (MCTS) is an algorithm used in decision-making processes, especially in situations where the outcome of an action is uncertain. It works by constructing a tree of possible future game states and then simulating many random games from those states to determine which actions will most likely lead to successful outcomes.  
MCTS can be used to decide how to recover from failures in a complex system. For example, suppose that an extensive computer network experiences a loss in one of its nodes. The self-healing system would need to determine the best course of action to recover from this failure, considering factors such as the system's current state, the possible causes of the loss, and the likely effectiveness of different recovery strategies. MCTS is described by [1] as a method for making optimal decisions when resolving artificial intelligence problems. It combines stochastic simulation and the accuracy of a tree search. The algorithm builds the search tree of the node through a substantial number of random samples, then formulates greater insight into the system and extracts the datasets to calculate the optimal strategy. For example, when faced with unexpected risks in the power grid scenario, MCTS choose the optimal strategy configuration through sampling and estimation results. As the number of samples increases, the obtained strategy will be closer to the optimal approach. MCTS can be used in the context of self-healing by constructing a tree of possible recovery strategies, simulating the effects of each strategy on the system, and then selecting the strategy that is most likely to result in successful recovery. The simulation process can consider factors such as the likelihood of further failures, the time required for each strategy to take effect, and the potential impact on other system parts. MCTS, a powerful tool for self-healing systems, enables the system to make informed decisions in complex and uncertain environments. However, it is essential to note that MCTS is only effective as the quality of the models used to construct the tree may require significant computational resources to run effectively in large-scale systems.
# Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events
## 3 Noise in Compressive Sensing Measurements
### Source Noise  
This noise is associated with the sampling star field itself. The star sources can be contaminated with flux from surrounding stars. In addition, other sky background noise can leak into the source star flux measurement. For our analysis, we will assume the source noise is of Gaussian distribution. We can characterize it by  
\[y=A(x+n_{b}) \tag{7}\]  
Figure 4: Sample differenced image.  
Figure 3: Sample observed image.  
where \(n_{b}\) is Gaussian noise. In this case, noise gets folded into the measurement matrix, \(A\)[9]. Incorporating this for differenced images, as stated in Equation (3), we obtain  
\[y_{diff} =A(x_{o}+n_{b})-A(x_{r})\] \[=A(x_{diff}+n_{b})\] \[=A(x_{diff})+A(n_{b}) \tag{8}\]  
where \(x_{diff}=x_{o}-x_{r}\).
## 1 Introduction  
The primary clinical treatment for brain tumors is to perform maximum surgical resection, requiring precise segmentation to provide accurate guidance for surgery [1; 2; 3]. However, manual labelling is a time-consuming, labour-intensive task that relies heavily on neurosurgical expertise. In recent years, tremendous developments in deep learning have opened up opportunities for automated and accurate brain tumor segmentation [4; 5]; this technique relies on a large number of annotated images for model training, which is another challenge in medical image analysis [6].  
To address the challenge of the shortage of annotated data, self-supervised learning has emerged as an option, which strives to enable models to learn the representations' information from unannotated data [7; 8]. Contrastive learning is an important branch of self-supervised learning; it is based on the intuition that different transformed versions of the same image have similar representations, and transformations from different images have different representations. By bringing similar representations closer together in the representation space and dissimilar representations further apart, the model exploits a contrastive loss function for effective pre-training [9; 10; 11; 12]. Therefore, in this paper, a novel brain tumor segmentation method aiming to achieve label efficiency is proposed (see Figure 1 for an overview). Specifically, for the scenario with limited labeled data, a pre-training strategy based on contrastive learning is presented. This strategy includes a positive and negative pairs construction method applied to brain tumor images and a representation learning system for feature learning on unlabeled data.  
A self-attention mechanism [13], which is an alternative feature extraction unit to convolution, has caused a revolution in the field of computer vision. Vision Transformer [14] was the first to introduce a standard transformer in NLP to vision, achieving excellent results in image classification. Based on this, Swin Transformer [15] proposed a shifted-window mechanism and hierarchical architecture, which introduced inductive bias for the transformer and outperforms convolution in several vision tasks, such as classification and segmentation [16; 17; 18; 19]. Therefore, a transformer has also been introduced to the medical image analysis community [20; 21; 22; 23; 24]. Inspired by this, in the presented method, a brain tumor segmentation network based on a self-attention mechanism is proposed, which has an encoder-decoder structure design and employs skip connection.  
The main part of this paper is as follows; related work is described in Section 2, details of the method are presented in Section 3, and experiments and result analysis are in Section 4.
# A Room Monitoring System Using Deep Learning and Perspective Correction Techniques
###### Abstract  
Studies on room monitoring have only focused on objects in a singular and uniform posture or low-density groups. Considering the wide use of convolutional neural networks for object detection, especially person detection, we use deep learning and perspective correction techniques to propose a room monitoring system that can detect persons with different motion states, high-density groups, and small-sized persons owing to the distance from the camera. This system uses consecutive frames from the monitoring camera as input images. Two approaches are used: perspective correction and person detection. First, perspective correction is used to transform an input image into a 2D top-view image. This allows users to observe the system more easily with different views (2D and 3D views). Second, the proposed person detection scheme combines the Mask region-based convolutional neural network (R-CNN) scheme and the tile technique for person detection, especially for detecting small-sized persons. All results are stored in a cloud database. Moreover, new person coordinates in 2D images are generated from the final bounding boxes and heat maps are created according to the 2D images; these enable users to examine the system quickly in different views. Additionally, a system prototype is developed to demonstrate the feasibility of the proposed system. Experimental results prove that our proposed system outperforms existing schemes in terms of accuracy, mean absolute error (MAE), and root mean squared error (RMSE).  
deep learning; Mask R-CNN; object detection; people counting; perspective correction +
Footnote †: journal: Applied Sciences
# Internet of Bikes: A DTN Protocol with Data Aggregation for Urban Data Collection
## 8 Performance Improvement of IoB-DTN Based on Data Aggregation Mechanism
### Spatiotemporal Aggregation  
In spatiotemporal aggregation, the data packets are aggregated if they satisfy the two conditions of the aggregation area and the aggregation period mentioned above. We evaluated four values of IoB-STA variant: 20 m-2 s, 100 m-2 s, 20 m-5 s and 100 m-5 s.  
Figure 34 shows the average delivery rate of IoB with spatiotemporal aggregation. As expected, we observe that using higher values of both aggregation parameters offers better performance. In addition, we can notice that using a high value of the aggregation period gives better results than lower one. This indicates the efficiency of temporal aggregation approach. This result impacts the throughput, as presented in Figure 35.  
The average delivery delays are shown in Figure 36. It is clear that the use of small aggregation parameters offer lower delays. It is important to note that using a small buffer size provides the best outcomes in terms of delivery delay.  
Figure 37 depicts the average protocol cost of IoB-STA. We notice that more the aggregation parameters values are higher more the protocol cost is better. This is an expected consequence since more data packets will be aggregated into same packets which decreases the communication cost.  
As for the number of aggregated packets into the same packet, as shown in Figure 38, we note that using the smallest aggregation values (20 m-2 s) offer the best results since they lead to aggregating fewer packets which improves the energy consumption of sensor nodes.  
Figure 33: Average number of aggregated packets per packet for IoB-TA.  
Figure 34: Average delivery rate for IoB-STA.  
Figure 35: Average throughput for IoB-STA.  
Figure 36: Average delivery delay for IoB-STA.  
Figure 37: Average protocol cost for IoB-STA.  
Figure 38: Average number of aggregated packets per packet for IoB-STA.
## 3 Results and Discussion
### Isothermal Calorimetry
The time at which the first derivative curve reached its maximum value was used to determine the initial setting time, and the increase in calorific value was the highest at this point. After the initial setting time, the first derivative value began to decrease. The time at which the first derivative dropped to zero was defined as the final setting time. The hydration rate reached the highest point at this time. The obtained maximum values of the first derivative (initial setting time) of nS0, nS2, and nS4 are expressed by t\({}_{i0}\), t\({}_{i2}\), and t\({}_{i4}\), and the values are 11.05, 13.68, and 14.16 h, respectively. The obtained zero values (final setting time) of the first derivative of nS0, nS2, and nS4 are expressed by t\({}_{i0}\), t\({}_{i2}\), and t\({}_{i4}\), and the values are 14.13, 16.74, and 17.65 h, respectively. The setting time results mainly relate to the test methods, concrete mixtures, and curing conditions. Regarding test methods, [PERSON] et al. [22] found that compared with the Vicat needle method (ASTM C191), the setting time determined by the isothermal calorimetry method is much higher. The trend of results from the Vicat needle method was consistent with those from the isothermal calorimetry method [22]. There was a linear relationship between the two methods [22]. Regarding concrete mixtures, [PERSON] et al. [32] found that polycarboxylate  
Figure 3: A typical curve of first derivative of the heat evolution curve (d(q)/d(t)). Figure 2: Isothermal calorimetry curves of all UHSCs for 72 h. Heat flow curves (**a**) and cumulative heat curves (**b**). superplasticizers can retard cement hydration and increase the setting time. This is because the surface dissolution and product nucleation sites are inhibited by the adsorption of polycarboxylate molecules, resulting in a delay of the hydration kinetics [33; 34]. In summary, due to the combined effect of the testing method and superplasticizer addition, the setting time of UHSC in this study is significantly high (setting time after 11 h). In future research, setting time measurement with a Vicat needle method will be done for UHSC with nano-silica.
# Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast
## 4 Outlook  
This section introduces some new perspectives in the application of the RO technique in NWP and TC forecast. It is worthwhile to exploit other potential features of the GNSS signal, particularly to increase the frequency and amount of RO soundings. There are trends toward increasing the amount of RO soundings and expanding the new features of the RO. Section 4.1 illustrates three emerging strategies to overcome the insufficiency in the amount of RO data, including multi-system-compatible RO receivers, commercial cube satellite constellation, and the airborne RO technique. Section 4.2 introduces two advanced RO features, including the polarimetric RO technique and multi-feature receiver.