esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
In [9; 30], a CAPSULE-NLU model is proposed for joint intent detection and slot filling. The architecture comprises of three capsules: WordCaps, SlotCaps and IntentCaps. Input word representations are learnt from the training dataset using a BLSTM in the WordCaps and then used to predict slots in the Slot caps. The output of the SlotCaps predict the intent of the utterance by using dynamic routing by an agreement algorithm between each capsule pair. The model achieves a 0.950 and 0.973 intent accuracy on the ATIS and SNIPS datasets, respectively. In [10], an INTENT-CAPSNET model is proposed for intent detection, using two capsules: SemanticCaps and DetectionCaps. In the SemanticCaps, input word representations are trained from scratch using a BLSTM and fed into a multi-head attention layer to extract the semantic features. The DetectionCaps use these features by dynamically combining to form higher level representations through unsupervised routing by an agreement algorithm. The model achieves a 0.9621 and 0.9088 accuracy, respectively, for SNIPS and CVA, a Chinese voice assistant dataset. In [8], a BERT-Cap-based model is proposed for intent detection from user queries in English and Chinese. This model leverages pre-trained BERT [31] to optimize the sentence representation and pass it as input to low-level capsules. Through dynamic routing, the low-level capsules capture the rich features of the sentence and forward them to the high-level semantic capsules for intent classification, and it achieved a 0.967 accuracy on one of the Chinese datasets. Models for joint intent detection and slot filling have been largely used for English and other languages where datasets with semantic slots and intent labels are available. Other languages rarely have datasets with slot annotations, and generally, only intent category labels are available [8]. Withstanding these limitations, in the proposed architecture, a two-tiered capsule network-based model was designed having WordCaps and IntentCaps layers with an intermediate dynamic routing mechanism. The IntentCaps in the proposed architecture use the output vector of the WordCaps directly, similar to the DetectionCaps in [10], but differ from the IntentCaps in [9] that use the output from SlotCaps, and it applies a max-margin loss for intent classification. The BERT-Caps model reported in [8] utilized the language model pre-trained on a large NL corpus, as the initial parameters in the sentence encoders, whereas the modelsin [9; 10] used an input representation trained from the queries dataset. Studies reveal that there are stark differences in the syntactic properties of an NL and query corpus, specifically, in the word co-occurrence structure [2]. Thus, word representations extracted from the two corpora may manifest varying patterns owing to this differentiation. In the proposed model, we investigated the impact of using sentence encoding representations from pre-trained models trained from an NL corpus [32] and a contextual representation learnt from the queries dataset, for intent detection in web queries.
# FDCNet: Frontend-Backend Fusion Dilated Network Through Channel-Attention Mechanism
## 3 Our Proposed Method--FDCNet
### The Dilated Block  
In our network, the input image of crowd is downsampled by the max pooling layers and upsamled before fusion. After those processes, the generated feature map is only half of the original input. The max pooling layers control overfitting and maintain invariance. Unfortunately, they reduce the spatial resolution so the spatial information of the feature map is lost. After passing through the max pooling layers, the feature maps are in lower resolution, and have difficulty in producing high-quality density maps.  
In Reference [11], the dilated convolution is proved to maintain the resolution of the feature map better than the scheme of using the convolutional layer, pooling layer, and deconvolutional layer. Although deconvolutional layers can alleviate the loss of information, it would increase additional complexity and execution latency. Dilated convolution is a better choice to alternate the pooling layer. Based on this, we exploited the dilated convolutional layer in the tail end of our framework. The dilated convolution enlarges the receptive field without increasing the parameters or the computation amounts. Meanwhile, the output from dilated layers includes more detailed spatial and global information, and the spatial resolution would not be reduced. Hence, we could get a high-quality density map through the dilated convolutions and improve the estimation accuracy.  
We tried a dilated convolution with a dilation rate of 2 in the tail end of our network. The dilated block is represented in Figure 2b. It composes four dilated layers with the dilation rate 2 and a convolutional layer of \(1\times 1\). The channels of dilated layers are all different and they are all followed by a batch normalization layer and a Relu layer. The \(1\times 1\) convolutional layer is to output the density map with fewer parameters and computation compared with the fully connected layer. The output from the dilated block can contain more detailed information. Finally we can retrieve the final density map of a high resolution.
## 5 A Case Study on Reliable Pipeline Monitoring Using Cyber Physical Sensor Networks
### Architecture for Remote Pipeline Monitoring
Generally, the layout would form linear or hierarchical topology since the coverage area would vary according to the type of industrial pipeline layout depicted in Figure 12. By communicating with each other, they actually provide redundancy to the network architecture. A major factor while installing sensing nodes in an industrial environment is the location to be monitored with the sensing device rather than deploying sensing nodes as per sleek network topology. Network topology does play an important role in data transportation and processing but somehow, in sensitive and critical networks, the location is more important than the design of the network. However, as a general rule, in oil and gas pipeline monitoring systems, nodes are deployed as per requirements and according to the need of the operation unit. The maximum number of hops observed between two designated nodes was one and the distance between each neighboring node was conditional, depending on the requirements and importance of the location. According to the hardware specification of various oil and gas pipeline monitoring equipment, sensing nodes can remain connected to each other for their lifetime once they are installed and  
Figure 12: Sample pipeline configuration levels for oil and gas transfer. powered up. It is important to note that all the nodes are energy constrained and need unlimited power to support their task over the span of several years. In order to save on battery life, the sensing devices are configured to communicate with a minimum delay of 6 sec and 60 min at maximum. In order to save on battery lifetime, it is possible to decrease the wakeup time of sensing nodes and data transmission rate.
## 3 Proposed Methodology
### Stage 3: Model Development
#### 3.3.2 Data-Driven Causal Inference Models  
Besides the SCMs, the proposed workflow also includes ML-based approaches for treatment effect estimation, ranging from traditional linear models to tree-based (R-Forest and BART) and neural network-based models (TARNet, Dragonnet and NN-Dragonnet). In contrast to SCMs, ML models do not directly encode any knowledge about the causal relationships between variables and aim at directly estimating the values of the outcome with and without treatment, given a set of known covariates. Depending on the level of existing knowledge about the underlying causal mechanisms, these values may prove to be more accurate than SCMs for the estimation of causal effects, at the cost of lower interpretability. The decision on the approach to use (SCM, possibly with model training to learn the function at each node vs purely data-driven models) depends on the specific problem and on data availability.  
Figure 2: Causal DAG for osteopenia/osteoporosis. A causal directed acyclic graph (DAG) is a graphical tool used for visually representing the causal connections between a set of variables. In our case, the set of variables are relevant to the clinical study for osteopenia/osteoporosis, as a result of treating breast cancer with aromatase inhibitors, in the context of the REBECCA project.
## 5 Problem Solution
### Dqn  
In each time slot \(t\), each node acts as an agent to observe the current state \(s_{t}\) of the system, including the congestion level and channel state information of all nodes. Then, the agent chooses a suitable action \(a_{t}\) to select the next-hop node. After selecting an action \(a_{t}\), a new state \(s_{t+1}\) is obtained, and the reward \(r_{t}\) corresponding to this action is also computed. The goal of the agent is to find a policy \(\pi(s_{t},a_{t})\), which maximizes the expected discounted cumulative reward \(E\left[\sum\limits_{i=t}^{K-1}\gamma^{i-t}r_{i+1}\right]\)[9]. Therefore, the action-state value function is used to calculate the expected discounted cumulative reward of each relay selection policy and then select the policy \(\pi\) with the largest reward. The state-action value function is defined as  
\[Q_{\pi}(s_{t},a_{t})\triangleq E\left[\sum\limits_{i=t}^{K-1}\gamma^{i-t}r_{i +1}\middle|s_{t},a_{t},\pi\right], \tag{29}\]  
where \(\gamma\in(0,1)\) is the discount factor, which represents the ratio between immediate and long-term reward. \(r_{i+1}\) is the immediate reward at time slot \(i+1\).  
In the next time slot of action selection, not only is the next-hop relay selected with maximum \(Q_{\pi}(s_{t},a_{t})\), but the \(\varepsilon-greedy\) algorithm is also added to explore extra actions. In order to try more possible actions and avoid falling into the local maximum, the agent has \(\varepsilon\) possibility to choose an action randomly. The \(\varepsilon-greedy\) algorithm is denoted as  
\[a_{t}=\left\{\begin{array}{cl}\underset{random}{\text{argmax}}Q_{\pi}(s_{t},a_{t}),&\text{with probability }1-\varepsilon\\ \quad\quad  
\[L(\theta)=E_{(s_{t},a_{t},r_{t},a_{t+1})}\big{(}Q_{target}-Q(s_{t},a_{t},\theta) \big{)}^{2}, \tag{31}\]  
where the target value for each iteration of the network is represented as  
\[Q_{target}=r_{t}+\gamma\max_{a_{t+1}}Q\big{(}s_{t},a_{t},\theta^{lat\sigma} \big{)}. \tag{32}\]  
To focus more on historical experiences and disrupt the correlation between experiences, DQN also uses the mechanism of experience replay. At each time slot, when nodes are trained centrally, the node acts as an agent, storing the training experience \(e_{t}=(s_{t},a_{t},r_{t},s_{t+1})\) into the experience pool, and then forming the sequence \(D=\{e_{1},e_{2}\dots e_{N}\}\). For each training, a small number of samples are randomly selected from the experience pool as a batch for network training, which makes the network converge better.
# A Mapping Model of Propeller RANS and LES Flow Fields Based on Deep Learning Methods
## 2 Related Work  
### RANS Simulation  
Reynolds-averaged Navier-Stokes (RANS) simulation is a classical numerical simulation method widely used in the field of fluid mechanics. Instead of directly solving the Navier-Stokes equation (NS equation) for turbulent flow, the RANS method applies time-averaging to the Reynolds equation [14]. It utilizes assumed Reynolds stress to establish a connection between turbulent fluctuation values and time-averaged values. In addition, it introduces a turbulent flow model to close the Reynolds time mean equations. The RANS time-averaging approach avoids directly solving turbulent flow, thus significantly improving computational speed and efficiency. Consequently, it has become the most commonly employed numerical simulation method in engineering. The RANS turbulence models includes the SA one-equation model and two-equation models. For example, k-\(\varepsilon\) and k-\(\omega\) models are widely used two-equation models. Some studies, including those conducted by [PERSON] [15] and [PERSON] [16], utilized the RANS method for unsteady numerical simulation of flow fields. [PERSON] [17] applied the RANS method to investigate the hydraulic performance of axial flow pumps. [PERSON] et al. [18] conducted numerical simulations on wind turbines, exploring the wind field and wind-sand erosion issues. The RANS method has been applied in practical engineering problems due to its low computational time and the requirement of low grid resolution. When the accuracy of the results is not critical, the RANS method can save computational costs and expedite the simulation process.
# An Edge-Based LWM2M Proxy for Device Management to Efficiently Support QoS-Aware IoT Services
## 3 LWM2M Proxy
### Discover  
The Proxy responds to a Discover request on behalf of the Client (see Figure 4, Step 2). To do so, the Proxy maintains a list of the Clients with their implemented Objects, Object Instances, Resources, Resource Instances, and Attributes. Being deployed at the edge, the Proxy is able to discover the Clients in the network gathering information from the lower layers; and whenever a Client connects to the network, the Proxy retrieves and stores the list of its Objects, Object Instances, Resources, and Resource Instances with their attached Attributes. The Proxy updates this list every time a Server creates or deletes an Object Instance or a Resource Instance and when it changes the notification Attributes of an Object, Object Instance, Resource, or Resource Instance, i.e., when it issues a successful Write-Attributes request or a successful Create, Delete, or Write request that modifies the list of Object Instances and Resource Instances.
# A Vision-Based Method for Simultaneous Instance Segmentation and Localization of Indoor Objects
## 4 Experiments Results and Discussion
### Location Results and Analysis  
#### 4.4.1 Test of Static Positioning Accuracy  
The results of the indoor target's static positioning are illustrated in Figure 8. The tester remains stationary at various locations within the camera's field of view, displaying the type and location coordinates of the target.  
We employ a total station to accurately measure the precise position of the target. The disparity between ground truth and estimated positions is calculated using the following formula.  
\[\text{Error}=\sqrt{(X_{truth}-X_{maa})^{2}+(Y_{truth}-Y_{maa})^{2}} \tag{13}\]  
The Cumulative Distribution graph illustrating the static positioning error is presented in Figure 9, revealing that 50% of the positioning results exhibit an error rate below 0.08 m, while 80% demonstrate an error less than 0.1 m; furthermore, the maximum positioning error is below 0.16 m.  
Figure 8: Results of static positioning.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Feature Points** & **Time (s)** \\ \hline SIFT & 21 & 0.11 \\ SURF & 19 & 0.09 \\ ORB & 13 & 0.02 \\ Ours & 19 & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of different feature point matching algorithms.
## 4 HMBAC Design and Implementation
### Janus Implementation
#### 4.3.2 Frontend API  
The main challenge of the Frontend component, is to implement a secure and authenticated communication channel per stakeholder, allowing users to communicate with the PBC via the Frontend application. In this way, users with valid credentials may login to the Janus services, only via the organisation's authenticated channel. To achieve this, for each stakeholder, an instance is created including information about the node (_Peer Information_), the corresponding stakeholder's gateway (_HF BC Gateway_) and the instance of the CA (_HF Certificate Authority_) where the stakeholder's certificates are stored. Furthermore, _Client-related Authorization Mechanisms_, generate the authentication token issued by the CA to which the user belongs. This token is used to establish connections between the user and the PBC.  
When a user submits a request, it is received by the _Routes_ module and then queued in the _Main Queue_ until served. The _Queue Handler_ is responsible for the storage and retrieval of data to and from the Main Queue. When the Queue Handler authorizes the request to be forwarded, the request is retrieved from the Main Queue, and _Ticket Manager_ generates a ticket for the user. Note that the Ticket Manager constantly checks for expired tickets.  
The Frontend API is a RESTful API developed in Node.js using the Express framework. Since the Frontend API is running on a distinct Kubernetes Pod, its services are accessible to other Pods via the Kubernetes-exposed ports. In order to expose services on the web, an Ingress controller is used, and the communication is TLS encrypted. For increased security, the TLS session is encrypted end-to-end between the Frontend application and the Frontend API. Thus, TLS is terminated on the Frontend Pod itself instead of the Ingress controller of the API.
# Low-Resolution Target Detection with High-Frequency Information Preservation
## 4 Experiments
### Implementation Details  
To evaluate the effectiveness of the high-frequency information preservation (HFIP) block, we conducted experiments on object detection and human pose estimation tasks. All the experiments were trained and tested on an RTX3090 GPU. Training the model for the object detection task took approximately 60 h, while training for the human pose estimation task required around 30 h. For a fair comparison, we standardized our human pose estimation and object detection experiments using the mmpose and mmdetection frameworks by MMLab, with the settings configured to their default optimal parameters. Since mmdetection does not include the YOLOv11 model, we directly used the official Ultralytics YOLO11 code. For the object detection task, Faster R-CNN [25] and YOLOF [1] were trained on the COCO dataset for 24 epochs, and YOLOv5 [26] was trained on the COCO dataset for 300 epochs. YOLOv11 [27] was trained on the COCO dataset for 100 epochs. Specifically, for the Faster R-CNN, we used an SGD [19] optimizer with lr = 0.02, weight decay = 0.0001, and a batch size of 16. For the YOLOv5, we train it with an SGD optimizer with lr = 0.01, weight decay = 0.0001, and a batch size of 16. For the YOLOF, we train it with an SGD optimizer with lr = 0.01, weight decay = 0.0005, and a batch size of 16. For YOLOv11, we trained it with an SGD optimizer with lr = 0.01, weight decay = 0.0005, and a batch size of 16. For YOLOv11, we trained it with an SGD optimizer with lr = 0.01, weight decay = 0.0005, and a batch size of 16. For the human pose estimation task, we used the Adam [28] optimizer with a learning rate of \(5\times 10^{-4}\) for all the models and training for 210 epochs with a batch size of 64 on the COCO dataset.
# Innovative Suspension Structures: The Role of Straight Elements Under Asymmetric Loads
## 1 Introduction
### Analysis of Asymmetrically Loaded Innovative Single-Level Suspension System
#### 2.2.1 Calculation of Asymmetrically Loaded Upper Chord Elements  
It is necessary to note that under asymmetric loads, the calculation of this suspension system becomes more complicated than in the case of symmetric loads [47]. Analogous to the two element suspension structure due to the effect of the asymmetrical variable load \(s\), an additional unknown appears--the horizontal displacement of the central hinge (node \"3\") \(\Delta h_{3r}\). For this reason, the suspension system considered is divided into two parts: the left one, loaded with loads \(g+s\), and the right one, which is loaded only by the permanent load \(g\). The calculation scheme for the upper chord on the left side is presented in Figure 4. Under the action of asymmetric loading, the horizontal displacement will occur not only in node \"3\" but also in node \"2\" of the strut and this chord connection. In addition, node \"2\", will shift perpendicular to the initial axis by the value \(\Delta f_{2r}\) (see Figure 4). Figure 3: Suspension system consists of straight elements (**a**) and bracing system between two suspension structures (**b**, red line). Under the action of loads, the displacements of the elements of the upper chord \"1-2\" and \"2-3\" are \(\Delta f_{1r}\) and \(\Delta f_{2r}\) and the deflection angles of this elements are \(\varphi_{1}\) and \(\varphi_{2}\) (see Figure 4). When reviewing the behavior of the upper chord elements \"1-2\" and \"2-3\" it can be stated that their calculation is analogous to the calculation of the elements of a two element suspension structure. The analysis of the equilibrium of node \"2\" can be obtained using the following dependence:  
\[H_{1,r}^{*}\cong H_{2,r}^{*}+(g+s)_{h}\cdot l_{1 pr}, \tag{16}\]  
where \(H_{1,r}^{*}\) and \(H_{2,r}^{*}-\) projection of tensile forces of elements \"1-2\" and \"2-3\" on line\"1-3\"; \((g+s)_{h}\) --projection of loads \(g+s\) on line \"1-3\"; and \(l_{1 pr}-\) distance between nodes \"1\" and \"2\" (see Figure 4). It can be seen from Equation (16) that due to the effect of the load \((g+s)_{h}\) the tensile forces in elements \"1-2\" and \"2-3\" will not be equal to each other. It is necessary to note that the tensile forces \(H_{1,r}^{*}\) and \(H_{2,r}^{*}\) are calculated as follows:  
\[H_{1,r}^{*}=H_{1}/\cos\varphi_{1r}, \tag{17}\]  
\[H_{2,r}^{*}=H_{2}/\cos\varphi_{2r}, \tag{18}\]  
where \(H_{1}=\frac{(g+s)_{2r}^{2}}{8\Delta f_{j_{1},r}}\); \(H_{2}=\frac{(g+s)_{2r}^{2}}{8\Delta f_{j_{1},r}}\). Figure 4: Deformed scheme of the left (**a**) and right (**b**) side of the upper chord element of the asymmetrically loaded cable–strut system.
## 4 Security and Performance Analysis
### Security Analysis
#### 4.1.4 Security against Man-in-the-Middle-Attacks and Guarantee of Data Privacy  
When the sensor node communicates with the cluster node, the encryption mechanism is used to prevent man-in-the-middle attacks and ensure data privacy. The transmitted message is encrypted into \(C_{si}=E((M_{i^{{}^{\prime}}},K_{{}^{\prime\hskip-1.0 pt-\hskip-1.0 pt\mathit{msg }}}^{*},N_{i^{{}^{\prime}}}),K_{si})\). The cluster node and the base station also use a similar method to prevent similar attacks and to ensure data privacy.  
The attacker cannot obtain the protected data. Furthermore, the cluster node makes use of \(K_{msg}\) to encrypt the complete message, and the message key will be updated for each transaction. Therefore, the attacker cannot imitate the cluster node to transmit a message. The man-in-the-middle-attack can thus be prevented.