esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Traffic Volume Based on Deep Learning Model for AADT Correction
## 2 Review of Related Studies
### Deep-Learning-Based Traffic Volume Prediction  
Existing studies on traffic volume correction and prediction have primarily relied on historical data for corrections and statistical learning algorithms for both correction and prediction. However, with recent advancements in data collection, management, and analysis technologies, there has been an increasing focus on employing various methods to correct missing data and predict future traffic volumes.  
Various machine-learning-based anomaly detection techniques have been developed, including classification-based, nearest-neighbor-based, clustering-based, and statistical anomaly detection methods. Additionally, deep-learning-based techniques, which integrate dimensionality reduction with anomaly detection, have been introduced [10].  
[PERSON] et al. [18] employed a deep learning approach using the stacked auto-encoder model, applying a greedy layer-wise unsupervised learning algorithm for initial deep network pre-training followed by a fine-tuning process to enhance prediction performance.  
[PERSON] et al. [19] analyzed the effectiveness of explicit models, such as dynamic traffic assignment, and implicit (data-driven) models, including artificial neural networks and Bayesian networks, for short-term prediction in urban traffic networks, which are crucial for intelligent transportation system applications. Explicit models make predictions at 15 min intervals, whereas implicit models present predictions at 5 min intervals.  
[PERSON] et al. [20] developed deep learning models to predict public bicycle rental volumes, evaluating exponential smoothing, ARIMA, and LSTM models and finding that deep learning models showed superior performance.
# A Survey of Deep Learning for Electronic Health Records
## 5 Deep Learning Applications in EHR
### Information Extraction
#### 5.1.2 Relation Extraction  
Relation extraction, in addition to entity recognition, is a significant task in the medical information sector. Its goal is to discover a hidden relationship between entities. The focus of this section is on the link between drugs and drug properties.  
Earlier, [PERSON] et al. [53] explored the performance of three machine learning methods for relationship extraction, namely SVM, RNN-based model, and Rule Induction. Their study confirmed that SVM was used for better performance compared to the other two models when the amount of data was small. However, when the amount of data in the training set grew, the performance of the deep learning network was much higher than that of the SVM model, with the F1 score of the LSTM increasing by almost 20% while the SVM only increased by 8%. Therefore, using deep learning for medical relationship extraction has great potential.  
The research related to relationship extraction using deep learning is growing year by year with excellent results. The JOINT technique described by [51] based on CNN-RNN connected with postprocessing has great performance in the drug-relation extraction task, outperforming the basic CNN-RNN method. The former had an F1 score of 98.53 percent, while the latter had a score of 97.60 percent. The CNN-RNN architecture, which is more inclined to embed in the self-training domain, appears to be better than the Bi-LSTM architecture based on the attention mechanism in their research, as the F1-score of CNN-RNN architecture was 97.2 percent compared to the F1-score of Bi-LSTM architecture, which was 96.44 percent.  
The best model for drug relationship extraction is the recursive hybrid CNN architecture with a focus loss function proposed by [PERSON] et al. [55] in 2020, which has an F1-score of 78.3 percent, and the best model for drug attribute relationship extraction has an F1-score of 98.7 percent; the difference between these two models has exceeded 13 percent. Similar to the CNN-RNN model architecture, the greatest performance on this job is still CNN-RNN model architecture, with the exception that Bi-LSTM based on attention mechanism works exceptionally well.  
Until recently, [PERSON] et al. [54] introduced Bert into medical relationship extraction and constructed the FT-Bert model and FC-BERT based on the BERT model proposed by [PERSON] et al. [83] On the MIMIC-III dataset, the FT-BERT model achieved higher scores in different types of relationship extraction compared to the CNN-RNN and JOINT approaches mentioned above. Especially in the more difficult types of relation extraction where it scores higher by about one percentage point than the first two.
## 2 Research on UAV Post-Disaster Path Planning Problem
### Path Smoothing Algorithm Based on Cubic B-Spline Curves  
In order to ensure smooth and flyable paths while reducing computational time, this study employs cubic B-spline curves for path smoothing. The smoothing technique described in reference [38] is utilized, which primarily involves adding control points to effectively avoid obstacles and generate smooth flight paths.
## 3 Proposed Dehazing Method
### Transmission Medium Estimation Based on Cascaded Multi-scale CNN  
For single image dehazing, estimating the transmission medium is an important task, because it is a crucial part according to the hazy image degradation model in Equation (1). Recently, a multitude of strategies has been developed to generate the transmission map from a hazy image directly. In [14], [PERSON] et al. proposed handcrafted haze-relevant features called _Dark Channel_ under an assumption on haze-free outdoor images. This assumption states that, for haze-free outdoor images, most of the patches have at least one color channel intensity value close to zero for some pixels, except for the sky area patches. Based on this property, the initial dark channel can be expressed as follows:  
\[J^{rgb}_{dcp}(x)=\min_{y\in p(x)}[\min_{c\in\{r,g,b\}}J^{c}(y)] \tag{5}\]  
where \(c\) represents a color channel, \(c\in\{r,g,b\}\) of RGB image. \(p(x)\) is a local patch centered at pixel \(x\). This prior has a high relation with the thickness of haze in the image so that the transmission medium can be calculated directly as: \(t(x)=1-J^{rgb}_{dcp}(x)\).  
Subsequently, to estimate the thickness of haze in a hazy image, [PERSON] et al. [20] built a new color attenuation model that employs saturation and brightness, because, whenever the haze concentration is increased, the saturation is decreased. Based on this model, the haze concentration can be estimated as:  
\[C(x)=I^{v}(x)-I^{s}(x) \tag{6}\]  
Note that \(I^{v}\) and \(I^{s}\) are two image components in **HSV** color space (Saturation and Value, respectively). As C(x) is relative to scene depth d, the transmission medium can be calculated directly using Equation (2).  
Figure 6: Visual comparison of \(AL\) estimation results for haze removal: (**a**) [PERSON]’s method [20]; (**b**) [PERSON]’s method [26]; (**c**) [PERSON]’s method [38]; (**d**) [PERSON]’s method [35]; (**e**) [PERSON]’s method [36]; and (**f**) the proposed method.  
Despite the great success achieved by these haze-relevant feature-based dehazing methods for removing haze from single images, they can be invalid in particular cases. [PERSON]'s method becomes useless for the sky region in the hazy image, and [PERSON]'s model fails to remove haze effectively in the case of thick haze. Differently, inspired by the inherent high performance of CNNs, we propose a two-stage cascaded multi-scale CNN architecture to generate accurate transmission maps for hazy images.  
Figure 7 illustrates the general mechanism of the proposed cascaded multi-scale CNN for generating transmission maps for hazy images. The proposed cascaded architecture consists of two subnetworks (CMCNN\({}_{tr}\) and CMCNN\({}_{t}\)), one for producing rough transmission maps \(t_{r}\) and the other for refined transmission maps \(t\).
## 3 Autonomous Mapping and Navigation with Satellite Imagery and Cadastral Information
### First Stage: Development of the Autonomous Mapping System  
The proposed autonomous mapping module is composed of two main sub-modules:  
1. _Acquisition sub-module:_ This sub-module is in charge of acquiring the images from online databases to generate the final map of the parcel. Concretely, the system will download the images from a public administrative database, _SIGPAC_, using the information provided by the _GPS_ sensor included in the robots. In other related experiments, we installed a submeter precision GPS in the autonomous robots (GPS Pathfinder ProXT Receiver) [22]. The location accuracy obtained in those cases encourages us to integrate that GPS in the current system. However, the GPS integration is out of this paper's scope, since the main objective of this paper is to introduce the algorithms to combine satellite and cadastral images and to show a proof-of-concept. This sub-module provides two images to the \"map creation module\": the satellite image of the parcel and the image with parcel borders.
2. _Map creation sub-module:_ This sub-module will be in charge of extracting all the information from the images: paths, orange trees, borders, and obstacles. With the extracted information, the map (path planning) which the robots should follow will be generated. The initial strategy to cover efficiently all the parcels with the robots will be done in this sub-module. The maps and satellite imagery resolution are set to 0.125 m per pixel. This resolution was easily reached with both services (satellite imagery and cadastral imagery) and was low enough to represent the robot dimensions (around 50 by 80 cm), and the computation burden was not so demanding. With the new Galileo services, higher resolution images can be obtained (with lower cm per pixel rates). Although more precise paths could be obtained, the computational cost of fast marching to generate the paths would also considerably increase.
###### Abstract  
Compared to traditional ad hoc networks, self-organizing networks of unmanned aerial vehicle (UAV) are characterized by high node mobility, vulnerability to interference, wide distribution range, and large network scale, which make network management and routing protocol operation more challenging. Cluster structures can be used to optimize network management and mitigate the impact of local topology changes on the entire network during collaborative task execution. To address the issue of cluster structure instability caused by the high mobility and vulnerability to interference in UAV networks, we propose a capacity-constrained weighted clustering algorithm for UAV self-organizing networks under interference. Specifically, a capacity-constrained partitioning algorithm based on K-means++ is developed to establish the initial node partitions. Then, a weighted cluster head (CH) and backup cluster head (BCH) selection algorithm is proposed, incorporating interference factors into the selection process. Additionally, a dynamic maintenance mechanism for the clustering network is introduced to enhance the stability and robustness of the network. Simulation results show that the algorithm achieves efficient node clustering under interference conditions, improving cluster load balancing, average cluster head maintenance time, and cluster head failure reconstruction time. Furthermore, the method demonstrates fast recovery capabilities in the event of node failures, making it more suitable for deployment in complex emergency rescue environments.  
unmanned aerial vehicle (UAV); collaborative task execution; network topology; clustering algorithm; interference factor; backup cluster head Article
## 3 Contextual Received Signal Strength Approach
### Propagation Model  
The communication between two sensors is hence assumed to be affected by both shadowing and small-scale fading resulting from obstructions and multi-path propagation, respectively [28]. In function of the transmitting power, \(P_{t}\), the received power at distance \(d\) is well known [29] and expressed as  
\[P_{r}(d)[dBm]=P_{t}[dBm]-PL(d)[dB]. \tag{1}\]Antenna gains are included in \(PL(d)\), the path loss at distance \(d\), defined as follows:  
\[PL(d)=PL(d_{0})+10\gamma(\frac{d}{d_{0}})+X, \tag{2}\]  
where \(PL(d_{0})\) is the free space path loss calculated at \(d_{0}\), the close-in reference distance, \(\gamma\) is the path loss exponent, \((2\leq\gamma\leq 6)\), and \(X\) is the composite when fading and shadowing affect the signal simultaneously, known as Nakagami-lognormal fading. Using the properties and theories in [30; 31; 32], the pdf of the Nakagami-lognormal (NLN) power \(u\) is defined by Equation (4.76) in [24], as  
\[f_{NLN}(u)=\int_{0}^{\infty}\frac{m^{m}u^{m-1}}{z^{m}\Gamma(m)}exp(-m\frac{u}{ z})\frac{(10/log_{e}(10))}{\sqrt{(2\pi\sigma^{2}z^{2})}}exp\Big{[}\frac{(10 \varrho_{10}z-\mu)^{2}}{2\sigma^{2}}\Big{]}dz, \tag{3}\]  
where \(m\) is the shape parameter \((m\geq 1/2)\), \(\Gamma(m)\) is the gamma function, \(z\) is the average power, and \(f(z)\) is treated to be lognormal with mean \(\mu\) and variance \(\sigma^{2}\).
# A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
## 3 Introduction to Signal Quality Index for Photoplethysmographic Signals
### Applications
#### 3.2.2 Supporting the Signal Processing Chain  
In this scenario, the algorithm can be integrated into the processing algorithm. For example, in [32; 61], a remote iPPG signal is extracted from an image divided into several subregions. Each subregion provides an iPPG signal and these signals are evaluated by an SQI algorithm (signal to noise ratio (SNR) in this case). The score obtained for each signal is then used to weight the contribution of each subregion to form the final iPPG signal. An SQI algorithm can also be used to evaluate different signal extraction and processing methods. For example, [PERSON] et al. [62] compared different methods of extraction of imaging PPG (PCA, ICA, CHROM [63], Spatial Subspace Rotation (SSR) [64], and Plane Orthogonal to Skin (POS) [62] by evaluating the quality of the signal extracted from a common database using an SNR quality index.
## 2 Parameters Characteristic of High-Precision Reducers
### Angular Transmission Error  
The angular transmission error (ATE) is the sum of the maximum positive and negative differences between the theoretical and actual rotation angle of the rotary module output. It represents the error between the input angle of the shaft rotation and the output angle of the flange rotation of the rotary module. Knowing this characteristic of the rotary module (in various connection designs) is especially important for the uniformity, smoothness, accuracy and repeatability of the output position.  
As [PERSON] et al. [29] write in their paper, the transmission error is generally measured under the conditions of zero load and low speed. However, low speed is a general concept and there is no general standard of speed measurement or proper scientific grounds for its definition. Therefore, it is difficult to obtain consistent transmission accuracy evaluation results for the same reducer.  
ATE is measured at a constant velocity and a very small angular speed without any load to eliminate the hysteresis effect of fast dynamic events. The actual velocity of the output position depends on the input position of the inlet flange and the ATE value in this position [30].  
This dependence can be described as follows:  
\[\omega_{out(\varphi_{in})}=i_{(\varphi_{in})}.\omega_{in}=\Big{(}i_{th}+\Delta i _{(\varphi_{in})}\Big{)}.\omega_{in} \tag{4}\]  
By integrating this equation, we get a change in the output position depending on the ATE:  
\[\Delta\varphi_{ch(\varphi_{in})}=\int~{}\Delta\omega_{out}dt=\int~{}\Delta i_{ (\varphi_{in})}.\omega_{in}dt=\omega_{in}\int~{}\Delta i_{(\varphi_{in})}dt \tag{5}\]  
ATE \(\Delta i\) can also be expressed using the harmonic function:  
\[\Delta i_{(\varphi_{in})}=\sum_{k}\Delta i_{k}.\sin\biggl{(}\frac{2\pi}{T_{k}} \varphi_{in}+\gamma_{k}\biggr{)},~{}~{}~{}~{}k>0 \tag{6}\]  
Figure 2: Hysteresis curve typical for Sumitomo Fine Cyclo/Spinea TwinSpin.  
where \(T_{k}\) is the length of the period on the side of the drive \(\varphi_{in}\) is the angle of rotation of the transmission input flange.  
By adding Equation (6) into Equation (5), the following derivation \(\Delta\varphi_{ch}(\varphi_{in})\) is obtained:  
\[\Delta\varphi_{ch(\varphi_{in})}=\omega_{in}\sum_{k}\Delta i_{k}.\int\ \sin \biggl{(}\frac{2\pi}{T_{k}}\varphi_{in}+\gamma_{k}\biggr{)}dt \tag{7}\]  
where, given the constant speed when measuring ATE,  
\[\Delta\varphi_{ch(\varphi_{in})}=\omega_{in}\sum_{k}\Delta i_{k}.\int\ \sin \biggl{(}\frac{2\pi}{T_{k}}\varphi_{in}+\gamma_{k}\biggr{)}dt=\sum_{k}\frac{T_{ k}.\Delta i_{k}}{2\pi}\cos\biggl{(}\frac{2\pi}{T_{k}}\varphi_{in}+\biggr{)}\gamma_{k} \tag{8}\]
# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 6 Communication Services
### Drone-to-Ground (D2G) Communication  
The GCS offers ground personnel the option to monitor the real-time status of the drone during the flight operation. The system connects a ground computer with the drones during the flight. Telemetry messages are periodically transmitted between the drone and the GCS. For the considered cases in the D2C system, a potential candidate technology is a Long-Range Radio (LoRa). LoRa utilizes the unlicensed Industrial, Scientific, Medical (ISM) frequency bands ranging particularly at 433 MHz, 868 MHz, and 915 MHz, but also 2.4 GHz [38]. It is designed for long-range communication at the expense of low data rates. The long-range is achieved by using the LoRa modulation scheme, which is based on Chirp Spread Spectrum (CSS). The principle of CSS is to spread the signal out by encoding it using a higher rate chip sequence. By spreading the signal to a larger bandwidth, the signal becomes more robust to interference and jamming. The chirp makes it easy to eliminate frequency offsets. The key feature of the LoRa modulation is high robustness and resistance to multipath and Doppler fading as well as signal interference.
# A Conceptual Model for Joint Graphic Representation of
## 2 Review of Methods for Servo-Driven Machine Representation
### Standards of Motion Control Programming Languages
#### 2.2.2 PLCopen for Motion Control  
PLCopen [14] or IEC 61131-3 [15] is a standard that defines several programming languages. By using these, the developers create programs to be interpreted and executed by the programmable logic controllers (PLC) to obtain the desired operation of the machine. Furthermore, it defines the format of a set of commands to describe the movements of the servo axes to be interpreted by motion controllers or PLCs that include this functionality [16].  
In PLCopen terminology, these commands are known as \"function blocks for motion control\". These commands to describe movements are combined with the rest of the program that controls the machine, and they define its behavior, but they are not represented graphically with the mechanics despite their influence on it. In addition, as these commands are combined with other control parts of the machine, it can be very difficult to work out the operation of the machine, even for specialists or the authors of the program itself.  
The interpretation of such commands without knowledge of programming is not feasible, but this direct relation with both the program and the motion of the machine has as a consequence the appearance, in the programs, of information of great interest from the mechanical point of view.  
For example, Figure 3 shows the appearance of two motion commands (in structured text format) in a program segment whose execution would correspond to two consecutive displacements of a moving carriage, such as the one in Figure 1b. Interpreting the sequence of movements from that text is not immediate, and without additional mechanical information, the real positions that the moving parts will reach cannot be known exactly.  
The problem of the interpretation of the program is even greater if the sequence of movements is more complicated or when it deals with coordinated movements of several servo axes, or even synchronized movements such as electronic gears. The complexity increases when virtual servo axes are used, i.e., axes that do not have any servo axis associated with them, but whose theoretical and ideal movement serves as a reference for real movements.  
Common relevant mechanical information cannot be obtained immediately either. Motion parameters such as position, velocity, or acceleration values mixed with variable names, commands types, language syntax elements, etc., may be found. In the proposed example, the last two variables will be calculated during the execution of the program and their value is not shown in the source code. However, they are fundamental to detail the movement completely.  
Therefore, apart from being difficult to deduce the movements by interpreting the program, even with knowledge and experience, the information of interest for the mechanical design is also difficult to extract.
# Stripe-Assisted Global Transformer and Spatial-Temporal Enhancement for Vehicle Re-Identification
## 2 Related Work  
### Visual Feature-Based Vehicle Re-Identification  
Vehicle re-identification methods using CNNs to extract visual features can be mainly summarized into two categories: global feature-based and local feature-based methods. The former extracts global representations to describe a vehicle. For example, [PERSON] et al. [10] presented a multi-branch architecture that extracted color, model, and appearance features to comprehensively characterize a vehicle. Similarly, [PERSON] et al. [11] introduced the DF-CVTC (Deep Feature with Camera Views, Vehicle Types, and Colors) for vehicle re-identification. To incorporate viewpoint information, [PERSON] et al. [12] introduced a method named Viewpoint-Aware Re-Identification (VARID), which employed viewpoint clustering and deep metric learning to acquire discriminative features.  
The local feature-based methods concentrate on capturing intricate details from specific regions through either a uniform partition or a predefined key part approach to identify similar vehicles. For example, [PERSON] et al. [19] exploited a Partition and Reunion Network (PRN) that uniformly divided the feature map based on height and width dimensions to obtain local features for vehicle re-identification. Additionally, the DPGM [14] combined the local features split from the same two directions as PRN with a global feature to represent vehicles. In particular, inspired by the PCB [28] designed for person re-identification, the SAN [23] constructed stripe-based features for vehicle re-identification. Like PRN and DPGM, the SAN divided the global feature map from the height dimension into several average part-level features, called stripe-based features. In contrast to these methods mentioned above, [PERSON] et al. [15] employed an SSD detector to detect 16 vehicle parts and extracted local features from these parts. [PERSON] et al. [16] proposed a Parsing-Guided Cross-Part Reasoning Network (PCRNet), leveraging an image segmentation model to identify predefined local regions and capture details from these regions. However, uniform partitioning leads to a misalignment issue, and predefined key part methods typically rely on an additional detection module, imposing significant annotation and computational burdens. Furthermore, redundancy exists in information between global and local features. Their fusion and use in the inference stage may result in inefficiency, as they incur substantially higher storage costs but yield only marginally improved performance.