esa-sceva/satcom-chunk-collection huggingface dataset:
### The Information-Driven Approach
#### Mobile Agent Approach  
The usage of mobile-agent-based computing paradigm in sensor networks is proposed in [19, 20]. Mobile agent is a special kind of software which can execute autonomously. Once dispatched, it can migrate from node to node performing data processing autonomously, while software can typically only execute when being called upon by other routines. In the context of sensor networks, the mobile agent is defined as an entity of four attributes: _identification_, _itinerary_, _data space_, and _method_, where identification is used to uniquely identify the mobile agent, data space is the agent's data buffer which carries the partially integrated results (this result should provide progressive accuracy as the agent migrates from node to node), itinerary is the route of migration, and method is the processing task (or execution code) carried with the agent.  
In the mobile agent computing model, data stay at the local site, while the processing task is moved to the data sites. By transmitting the computation engine instead of data, the mobile agent model offers several important benefits: 1) Network bandwidth requirement is reduced. Instead of passing large amounts of raw data over the network, only the agent of small size is sent. This is especially important for real-time applications and where the communication is through low-bandwidth wireless connections; 2) Better network scalability can be achieved. The performance of the network is not affected when the number of sensor is increased. Agent architecture can support adaptive network load balancing automatically; 3) Extensibility is supported. Mobile agents can be programmed to carry task-adaptive processes which extends the capability of the system; and 4) Stability. Mobile agents can be sent when the network connection is alive and return results when the connection is re-established. Therefore, the performance of the system is not much affected by the reliability of the network.  
Mobile-agent-based approach is similar to information-driven approach in the sense that it calculates its next hop on-the-fly based on information provided by neighbors at that moment. The calculation is based on the signal energy sensed at the sensor, the geographical local of the surrounding nodes, and the power status. It migrates to a node with more remaining power, stronger signal energy, and closer location. The mobile agent approach provides more stable performance and better fault tolerance than the information-driven approach, however, with the cost of more communication burden.
# The Customized Automatic Processing Framework for HY-2A Satellite Marine Advanced Products
## 4 Case Studies
### Water Vapor  
The customized processing workflow of water vapor is similar to the sea surface temperature. The only difference is the first step in which the workflow needs to read the water vapor dataset from the level-2 HDF file of the HY-2A scanning microwave radiometer. The thematic map is shown in Figure 7 following the remaining six steps.  
Figure 6: A Sea Surface Temperature thematic map of a single HY-2A swath for 25 March 2015 derived from the HY-2A CAPF. The sample points of sea surface temperature are mapped to a 0.25-degree grid. The colors correspond to the temperature scale.  
Figure 7: A Water Vapor thematic map of a single HY-2A swath for 25 March 2015 derived from the Customized Processing System. The sample points of water vapor are mapped to a 0.25-degree grid. The colors show the water vapor concentration.
## 3 Method
### State-Action Space  
The state-action space for the individually operated ESS is described using a one-dimensional model [31]. The VESS is operated as a physical ESS, so the state-action space for the VESS is also presented as a one-dimensional model. However, the individually operated ESS action is only limited by the ESS capacity, although the action range of VESS is determined according to the accumulated actions of all units.  
When the VESS is operated during \(T\), the decision sequence, the state-action space is at \(T+1\), a decision stage including the initial stage, as shown in Figure 2. The state-action space for the RL approach is solved only using the discrete model. Therefore, the ESS operation corresponds to quantization by the unit action step \(\delta\), as shown in Figure 2. Accordingly, all state and action sets are expressed as  
\[\begin{array}{ll}S&=\left\{s^{1},\cdots,s^{i},\cdots,s^{\kappa_{s}}\right\},\\ A&=\left\{a^{-\kappa_{a}},\cdots,a^{j},\cdots,a^{\kappa_{a}}\right\},\end{array} \tag{10}\]  
where \(a_{t}\) is the accumulated action for all units \(a_{t}=\sum\limits_{u\in\mathcal{U}}a_{t}^{u}\). Considering the PS and ES capacity constraints in Equations (4) and (7), \(\kappa_{s}\) and \(\kappa_{a}\) are calculated as  
\[\begin{array}{ll}\kappa_{s}&=\left|\left(C_{ES}^{\max}-C_{ES}^{\min}\right)/ \delta\right|,\\ \kappa_{a}&=\left|C_{PS}/\delta\right|,\end{array} \tag{11}\]  
where \(\left\lfloor\cdot\right\rfloor\) is the floor operation. The discretized ESS operation by the quantization process makes a quantization error, but the error is bound according to the step size [33].  
Figure 2: State-action space model for a virtual energy storage system; PS: power subsystem, ES: energy subsystem, and ESS: energy storage system.  
At each stage \(t\), the current state is defined as the current SoC, \(s_{t}\). The next state, \(s_{t+1}\), is determined by the current state and the selected action \(a_{t+1}\) set by Equation (6) as follows:  
\[<s_{t}=s^{i},a_{t+1}=a^{j}>\ \to s_{t+1}. \tag{12}\]  
Herein, action \(a_{t}\) should be selected from within the feasible action range according to the current state  
\[A_{t}=\left\{a^{j_{\min}},\cdots,a^{j},\cdots,a^{j_{\max}}\right\}, \tag{13}\]  
where \(j_{\min}=\max(-\kappa_{a},1-i)\) and \(j_{\max}=\min(\kappa_{a},\kappa_{s}-i)\). As an example, in Figure 2, the feasible action range for the first action \(a_{1}\) and the second action \(a_{2}\) become \(A_{1}=\left\{a^{-2},a^{-1},a^{0},a^{1},a^{2}\right\}\) and \(A_{2}=\left\{a^{-2},a^{-1},a^{0}\right\}\), respectively.
# Possibility of Bridge Inspection through Drive-By Vehicles
## 5 Effect of Road Roughness on Drive-By Bridge Inspection
### Road Roughness and Bridge Frequency  
Using the same method, the Class B road profile can be added to the short bridge surface. The vehicle is driven through the bridge at a speed of 1 m/s. As the rear axle goes through the bridge's whole length, the acceleration data were collected from the right rear axle.  
The FFT process of the collected acceleration data from the right rear axle is shown in Figure 25.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Driving Distance**} \\ \cline{3-5}  & & **5 m** & **10 m** & **15 m** & **20 m** \\ \hline \multirow{3}{*}{Driving speed} & 10 m/s & 0.14 & 0.036 & 0.003 & 0.021 \\  & 20 m/s & 0.202 & 0.124 & 0.103 & 0.07 \\ \cline{1-1}  & 25 m/s & 0.285 & 0.211 & 0.121 & 0.019 \\ \cline{1-1}  & 30 m/s & 0.334 & 0.266 & 0.123 & 0.116 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Acceleration amplitude for the 28 Hz component in the vehicle’s vibration.  
Figure 24: FFT results of the vehicle acceleration responses when driving from a 20 m offset distance with a speed of 10 m/s.  
The comparison of identified results and reference bridge frequencies are listed in Table 11.  
From Table 11, we could see the drive-by inspection could identify most of the bridge frequencies.
## 3 Strategy and Objective Function for Damage Identification
### Frequency Shift Coefficient (FSC)  
The modal method for damage detection relies on changes in dynamic properties of the structure and particularly natural frequencies. Any changes in the structure, such as the reduction in stiffness, will cause changes in the natural frequencies. One of the important advantages of natural frequency is that it can be easily measured through vibration measurements. Classical measurements schemes can be used for the determination of experimental resonant frequencies. In this context, the Frequency Shift Coefficient is first presented by [PERSON] and [PERSON] [PERSON] and [PERSON] (2013) for damage identification problems. This coefficient uses four sets of natural frequencies. We consider on the one hand so called experimental frequencies denoted \(X\) (issues from a numerical model or an experiment) and on the other hand frequencies from a model, denoted \(A\). In each category, there can be frequencies from a healthy or reference structure, denoted \(h\) and frequencies from an unknown, or damaged structure, denoted \(d\). To be clear, there is a set of experimental healthy frequencies \((h,X)\), a set of experimental unknown frequencies \((d,X)\), a set of frequencies obtained with a healthy model \((h,A)\) and a set obtained with a damaged model \((d,A)\). The frequency shift coefficient is defined as:  
\[FSC=\sqrt{\frac{1}{m}\|\sum_{i=1}^{m}\left(\frac{(\Gamma_{i})_{X}-(\Gamma_{i}) _{A}}{(\Gamma_{i})_{X}}\right)\|}\quad\text{and}\quad\Gamma_{i}=\frac{f_{i}^{ d}}{f_{i}^{h}}, \tag{8}\]  
where \(m\) is number of modes taken into account, \(X\) is the damaged case, \(A\) is the 2D beam model (reference), \(f_{i}^{d}\) is the damaged beam frequencies, \(f_{i}^{h}\) is healthy beam frequencies and \(i\) denotes modes' indices.  
The FSC is then used to qualify \((d,X)\) by updating \((d,A)\) using the shifts with respects to the sets \((h,A)\) and \((h,X)\). Remember that \((h,A)\) was first updated with respect to \((h,X)\). In other words, the location and severity of damage in \((d,X)\) are given by minimizing the value of FSC.
## 2 Theoretical Formulation
### The Formulation in VDI 2081-1  
The VDI 2081-1 standard [22] proposes a formula similar to that in ISO 14163 to define the pressure loss:  
\[\Delta p_{t}=\xi\frac{\rho}{2}v^{2} \tag{4}\]  
where \(\xi\) is the pressure loss coefficient, as it was previously defined for the ISO standard.  
Nevertheless, the formulations of both standards differ in the definition of the pressure loss coefficient, which is defined in the VDI as follows:  
\[\xi=a_{1}\bigg{[}\frac{s}{s+d}\bigg{]}^{b_{1}}+a_{2}\bigg{[}\frac{s}{s+d} \bigg{]}^{b_{2}}\frac{l}{d_{h}} \tag{5}\]  
where \(s\) is the clear gap width between the splitters, \(d\) is the splitter thickness, \(d_{h}\) is the hydraulic diameter of the splitter gap, and \(l\) is the splitter length. The constants \(a_{1}\), \(a_{2}\), \(b_{1}\) and \(b_{2}\) are obtained from the values in Table 1.
# Routing in Mobile Wireless Sensor Networks: A Leader-Based Approach+
## 3 A Leader-Based Routing Approach (LBR)
### Notation and Assumptions  
We consider an architecture with an unknown set of \(n\) resource-constrained mobile sensor nodes and one more powerful mobile sink. We denote this set of sensor nodes as \(V=\{p_{1},p_{2},\ldots,p_{n}\}\). When required for simplicity, we will also refer to a node as \(p\), \(q\), etc. We will denote as \(s\) the sink node. Every node of the system has a unique identifier and a local clock to measure real-time intervals.  
Every node, including the sink node, can move. The mobility of a node is characterized according to two dimensions: velocity and direction. At any time \(t\), a node \(p\) moves towards some direction \(d(p,t)\) with a velocity \(vl(p,t)\leq VL_{\text{max}}\), \(VL_{\text{max}}\) being the maximum velocity of every node \(p\) in the system.  
A node can leave the system, due to a fault or disconnection, or join the system. Connections and disconnections can be caused by mobility, as the node enters and comes out of the communication range of other nodes in the system, or for other reasons. In the following, we formalise this concept.  
Every node uses broadcast communication to exploit the Wireless Broadcast Advantage (WBA) [18]. At any time \(t\), a node \(p\) can communicate directly (i.e., at one hop) with a subset \(N_{p}(t)\subset V\), which are the nodes that are at time \(t\) into a radius \(r\) from \(p\), also referred as the neighbourhood of \(p\). The communication range \(r\) is a common parameter for all nodes in \(V\). For simplicity, we consider that message transmission delays are bounded.  
We define the parameter _link quality_ as a measure of the transmission power (in \(dBm\)), which can be easily extracted in a node \(p\) at the reception of a message by \(p\) from \(q\). We will use the notation \(W_{p\gets q}(t)\) to refer to the link quality for a link \((q,p)\) at time \(t\). We assume that \(W_{p\gets q}(t)=W_{q\gets p}(t)\). We define \(\xi(\mathit{tech})\) as the threshold for the RSSI (Received Signal Strength Indicator) such that a link can be considered to have good link quality.  
When considering link quality, a graph \(G(t)\) representing the connectivity conditions in \(V\) at time \(t\) is obtained. We also define a spanning tree \(T(t)\subset G(t)\). Ideally, \(T(t)\) will include only links with good link quality.  
Observe that partitions in \(G(t)\) are possible, but we assume that \(\forall t\), \(s\in T(t)\). A node \(p\) can be disconnected from \(T(t)\) due to either: (a) \(p\
ot\in N_{q}(t)\)\(\forall q\in T(t)\), or (b) \(W_{p\gets q}(t)\leq\xi(\mathit{tech})\)\(\forall q\in T(t)\).
# Towards Automatic License Plate Detection
## 3 Proposed Method
### The License Plate Localization Module (LPLM)
#### 3.2.1 RGB to HSV Transformation  
The LPLM initially processes the RGB image and transforms it into the HSV domain, as shown in Equations (1)-(3):  
\[Max=max(R,G,B)andMin=min(R,G,B)\]  
\[H=\begin{cases}0&ifMax=Min\\ 60\times\frac{G-B}{Max-Min}+0&ifMax=R,G\geq R\\ 60\times\frac{B-R}{Max-Min}+120&ifMax=G\\ 60\times\frac{R-G}{Max-Min}+240&ifMax=B\\ 60\times\frac{G-B}{Max-Min}+360&ifMax=R,G<B\end{cases} \tag{1}\]  
\[S=\begin{cases}0&ifMax=0\\ \frac{Max-Min}{Min}&otherwise\end{cases} \tag{2}\]  
\[V=Max \tag{3}\]  
where RGB denotes original Red, Green, and Blue components, respectively. The license plate in the PKU dataset has a diverse variety of colors. Therefore, we apply the color segmentation from lines (26)-(38) of Algorithm 1 on each of the HSV components to obtain the LP blob image (\(A\)) as shown in line (38) of Algorithm 1. As the RGB image is transformed to the HSV image, we obtain three channels that we refer to as H_Image_, _Slmage_, and _VImage_ in line (25) of Algorithm 1. In line (26), color segmentation is applied on each of the HSV image channels that were obtained in the above step. For each of the HSV channels, a low Threshold (T_low_) and a high Threshold (_Thigh_) are defined. For each of the HSV channels, a range of low and high Thresholds are applied. As a result, for the Huechannel, its relevant low and high Thresholds are HT_low_ and HT_high_, respectively. During our simulations, we vary the HT_low_ value from 0.02-0.40 and HT_high_ from 0.40 to 0.62. Similarly, for the Saturation and Value channels, their relevant low and high Thresholds are ST_low_, ST_high_, VT_low_, and VT_high_, respectively, as shown in line (32)-(35) of Algorithm 1. For ST_low_, the values are varied from 0.37-0.50, whereas for ST_high_, they are varied from 0.90 to 1.0. For the V channel in the HSV image, the VT_low_ is set to 0.75 and the VT_high_ is set to 1.0. After the Thresholds are set, now the mask images are obtained for each of the H, S, and V channels as shown in lines (30)-(36) of Algorithm 1. For the H channel, the H_mask_ is set to 1 when the H_Image_ obtained in line (25) of Algorithm 1 is greater than or equal to the low Threshold and less than or equal to the high Threshold. A similar mechanism is applied to obtain the masks of the S and V channels, as shown in lines (33)-(36) of Algorithm 1. Finally, a license plate's blob image (\(A\)) is obtained as shown in line (38). This blob image is filled with binary value 1 if, and only if, the values in the H_mask_, _Snusk_, and _Vmask_ are all equal to 1 through logical AND operation. Otherwise, values in blob image are set to value 0. After the segmentation is complete, in the next step, morphological operations are applied as described below.
# Risk-Sensitive Policy with Distributional Reinforcement Learning
## 3 Materials and Methods
### Performance Assessment Methodology
#### 3.3.2 Risk-Sensitive Distributional RL Algorithm Analysed  
The distributional RL algorithm selected to assess the soundness of the methodology introduced to learn risk-sensitive decision-making policies is the _Unconstrained Monotonic Deep Q-Network with Cramer_ (UMDQN-C) [27]. Basically, this particular distributional RL algorithm models the CDF of the random return in a continuous way by taking advantage of the Cramer distance to derive the TD-error. Moreover, the probability distributions learnt are ensured to be valid thanks to the specific architecture exploited to model the random return: _Unconstrained Monotonic Neural Network_ (UMNN) [28]. The latter has been demonstrated to be a universal approximator of continuous monotonic functions, which is particularly convenient when representing CDFs. In practice, the UMDQN-C algorithm has been shown to achieve great results, both in terms of policy performance and in terms of random return probability distribution quality. This second feature clearly motivated the selection of this specific distributional RL algorithm to conduct the following experiments, since accurate random return probability distributions are required to properly estimate the risk. The reader can refer to the original research paper [27] for more information about the UMDQN-C distributional RL algorithm.  
As previously explained in Section 3.2.2, the presented approach requires the choice of a function \(\mathcal{R}_{\rho}\) to extract risk features from the random return probability distribution \(Z^{\pi}\). In the following experiments, _Value at Risk_ (VaR) is adopted to estimate the risk. This choice is motivated by both the popularity of that risk measure in practice and by the efficiency of computation. Indeed, this quantity can be conveniently derived from the CDF of the random return learnt by the UMDQN-C algorithm.  
In the next section presenting the achieved results, the risk-sensitive version of the UMDQN-C algorithm is denoted _RS-UMDQN-C_. The detailed pseudo-code of that new risk-sensitive distributional RL algorithm can be found in Appendix B.  
To conclude this section, ensuring the reproducibility of the results in a transparent way is particularly important to this research work. In order to achieve this, Table 1 provides a brief description of the key hyperparameters used in the experiments. Moreover, the entire experimental code is made publicly available at the following link: [[https://github.com/ThibautTheate/Risk-Sensitive-Policy-with-Distributional-Reinforcement-Learning](https://github.com/ThibautTheate/Risk-Sensitive-Policy-with-Distributional-Reinforcement-Learning)]([https://github.com/ThibautTheate/Risk-Sensitive-Policy-with-Distributional-Reinforcement-Learning](https://github.com/ThibautTheate/Risk-Sensitive-Policy-with-Distributional-Reinforcement-Learning)), accessed on 10 June 2023.
# Feature-Level Camera Style Transfer for Person Re-Identification
## 5 Experiments
### Implementation Details  
The proposed method is implemented on the PyTorch Framework. All experiments are held on two NVIDIA 2080 Ti GPUs. For the IDE baseline, input images are resized to \(256\times 128\times 3\). Random horizontal flip and random erasing (with probabilities 0.5) are used for pre-processing, and the batch size is set to be 64. The network is optimized within 450 epochs (1-60 for pre-training, 61-360 for generative model training and 361-450 for joint training). Adam optimizer is adopted for all networks. For \(G\) and \(D\), the learning rate starts with 0.001 and is divided by 10 every 100 epochs. For \(E_{I}\) and \(E_{C}\), the initial learning rate is set to be 0.00035, and it decays at the 360 th epoch and 400 th epoch with the same decay rate, 0.1. For the PCB baseline, input images are resized to \(384\times 128\times 3\) with horizontal flip, and the batch size is 256. SGD optimizer with a learning rate of 0.1, and a weight decay of \(5\times 10^{-4}\) is used for \(E_{I}\) in the pre-training stage. For the joint training stage, Adam optimizers with a learning rate of \(1\times 10^{-3}\) are used for \(G\) and \(D\). The whole network is trained for 150 epochs (1-20 for pre-training, 21-100 for generative model training and 101-150 for joint training). The learning rates of \(G\) and \(D\) decay by 0.1 every 30 epochs. For MGN baseline, input images are resized to \(384\times 128\times 3\) with random horizontal flip, and the batch size is 256. The SGD optimizer with a learning rate of 0.1 and weight decay of \(5\times 10^{-4}\) is used for feature extractors in the pre-training stage. For the joint training stage, Adam optimizers with a learning rate of \(1\times 10^{-3}\) are applied for both \(G\) and \(D\). The network is trained for 520 epochs (1-320 for pre-training, 321-440 for generative model training and 441-520 for joint training). The learning rates of \(G\) and \(D\) decay by 0.1 every 60 epochs.  
The performance of the proposed method is evaluated on the Market-1501 [32] dataset. **Market-1501** contains 32,668 labeled images with 1501 identities from six camera views. The training set has 12,936 images with 751 identities, from which 1182 images of 80 identities are chosen as the validation set. Images in the validation set are not used in the training stage, and they are used for parameter analysis. In the testing stage, 3368 images from the remaining 750 identities are chosen as query images to retrieve the matching persons in a gallery set of 15,913 images.
# Active Learning for Biomedical Article Classification with Bag of Words and _FastText_ Embeddings
## 2 Materials and Methods
### Text Representation Methods
#### 2.1.1 Bag of Words  
The simplest vector text representation that still remains a standard choice in most text classification applications is the _bag of words_ (BOW) representation [1; 2; 27], with attributes directly corresponding to words, which are also referred to as _terms_. In the most common term frequency (TF) variant, the value of the attribute corresponding to a term is the number of its occurrences in a given document. In the TF-IDF variant, term frequencies are normalized by inverse document frequencies (IDFs) to downweight terms occurring in many documents and upweight more unique terms [31].  
The bag of words representation is order- and context-insensitive--it always treats treats all occurrences of the same term in the same way. Therefore, its capability of preserving semantic information is limited, but it can be directly interpreted, is easy to use, and works well in many applications.