esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Decomposition Methods
#### 2.1.3 Intrinsic Time-Scale Decomposition  
According to [PERSON] and [PERSON] [47], the intrinsic time-scale decomposition (ITD) is developed for the efficient and precise time-frequency-energy (TFE) analysis of signals. Based on this approach, a nonlinear or nonstationary signal can be decomposed in to a set of proper rotation (PR) components and a monotonic trend, namely a residual signal. Given a signal \(X_{t}\), the decomposition process of ITD algorithm is illustrated as follows:  
1. An operator, \(\xi\), which extracts a baseline signal from \(X_{t}\), is defined. More specifically, \(X_{t}\) can be decomposed as: \[X_{t}=\xi X_{t}+(1-\xi)X_{t}=L_{t}+H_{t}\] (6) where \(L_{t}=\xi X_{t}\) and \(H_{t}=(1-\xi)X_{t}\) represent the baseline signal and a proper rotation respectively.
2. Suppose that \(\{X_{t},\ t\geq 0\}\) is a real-valued signal, while the local extrema of \(X_{t}\) is defined as \(\{\tau_{k},\ k=1,\ 2,\ \dots\}\), and let \(\tau_{0}\) be equal to zero. For convenience, \(X(\tau_{k})\) and \(L(\tau_{k})\) are abbreviated as \(X_{k}\) and \(L_{k}\), respectively. \(\tau_{k}\) is taken as the right endpoint of the interval, containing extrema due to the neighboring signal fluctuation, where \(X_{t}\) is constant. Meanwhile, suppose that \(L_{t}\) and \(H_{t}\) have been defined on the interval, \([0,\ \tau_{k}]\), and that \(X_{t}\) is available for \(t\in[0,\ \tau_{k+2}]\). Thus, a (piece-wise linear) baseline-extracting operator, \(\xi\), is defined on the interval \((\tau_{k},\ \tau_{k+1}]\) between successive extrema as follows: \[\xi X_{t}=L_{t}=L_{k}+\left(\frac{L_{k+1}-L_{k}}{X_{k+1}+X_{k}}\right)(X_{t}-X_ {k})s.t.t\in(\tau_{k},\ \tau_{k+1}]\] (7) where \[L_{k+1}=\alpha\bigg{[}X_{k}+\bigg{(}\frac{\tau_{k+1}-\tau_{k}}{\tau_{k+2}- \tau_{k}}\bigg{)}(X_{k+2}-X_{k})\bigg{]}+(1-\alpha)X_{k+1}\] (8) and the parameter, \(\alpha\in(0,\ 1)\), is usually determined as 0.5.
3. After defining the baseline signal based on Equations (7) and (8), it is possible to define the residual, proper-rotation-extracting operator, \(\psi\), as: \[\psi X_{t}\equiv(1-\xi)X_{t}=H_{t}=X_{t}-L_{t}\] (9)  
Figure 1: Comparison between three-level wavelet transform (WT) and three-level wavelet packet transform (WPT).
# A Study on Design and Control of the Multi-Station Multi-Container Transportation System
## 3 Results
### The Design of Electrical and Controller of the Entire System  
#### 3.2.1 The Overall Design of the Controller of the System  
The transportation system is built with functional blocks as described in Figure 15:  
* The role of the central block:  
Figure 14: Using an additional limit switch to limit the travel distance of the lifting module.  
Figure 13: The structure of the line switching module with sensors.  
* Server receives data of sensor block and operation of conveyor module from control block for further processing.
* Send data after processing at the control block to operate the conveyor module.
* Send data after processing to store in the database.
* Database stores data of containers, conveyor modules and RFID sets. There will be no data lost in the event of a power failure or system shutdown.
* The role of the control block:
* PLC receives data from the server then transmits it back to the remote I/O in order to operate conveyor modules corresponding to stations.
* Acts as an intermediary to receive proximity sensor signals and send them back to the server.
* The role of the sensor block: Including RFID sets and proximity sensors to limit the travel distance of the conveyor and determine the number of containers on a module.
* The role of the motor block: The transmission system for the entire transport system.
* The role of the source block: The power supply for the entire transport system.  
Preliminary operating process of the system:  
1. GUI (graphical user interface): Built on the server, allowing to choose the transport route for a container, helping the user monitor and check for the operation of the transportation system (cargo, conveyor modules, etc.).
2. HMI: Placed at the stations for the users to set up the route of transport for the containers.
3. Conveyor module system: Helps transport containers according to the built-in control logic.  
Figure 15: The overall control block of the entire system.  
* Server, PLC, sensor system, etc.: Help to control the operation of the system automatically.
## 2 Related Work
### Key Findings
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Smart** & **Area** & **Access** & **Sensors and** & **IoT** & **Software** & **Use Cases** & **Fog Computing** & **Network** & **Sustainable** & **Development Goals** \\
**Campus** & **Area** & **Technology** & **Actuators** & **Platform** & **Platform** & **Use Cases** & **Capabilities** & **Training** & **(SDGs)** [107], **KPIs** & **(SDGs)** [107], **KPIs** & **or Results** \\ \hline  & 4058 & & & & & & Automatic & & & \\  & acres & & & & & & vehicle access & & & \\ Wisdom & (16.42m\({}^{2}\)), & & & & & & systems, & & & \\ Campus, & 5263 & & & & & & parking & & & \\ Soochow & staff & & & & & & guidance & & & \\ University & and & & & & & & & service, bus & & \\ (China) & more & & & & & & tracking & & & \\
[71] & than & & & & & & system and & & & \\  & 50,000 & & & & & & bicycle rental & & & \\  & people & & & & & & service & & & \\ \hline IISc & 2 km & & Low-cost & & & & & & & & RSSI and Packet \\ campus & \(\times\) 1 & sub-GHz & ultrasonic & Microcontroller & & & & & & Error Rate (PER) \\ [52] & km & radios & water level & TI & - & & management & & & performance, power \\  & & & & & & panels & & & & budget \\ \hline Ottawa & & & & & & & & & & & \\ City and & & & & & & & & & & & \\ APEC & - & Wu-Fi & - & - & - & - & No & & & & \\ campus [104] & & & & & & & & & & \\ \hline  & & & & & RT simulators for & & & & RT & \\ Universitas & Urban & 800 MHz, & & & & propagation & & & & and \\ Indonesia & 2.3 GHz, and & - & - & - & - & & & & & \\ [106] & area & 38 GHz & & & & & results in a & & & & \\  & & & & & & university & & & & field \\ \hline
**University** & & & & & IoT nodes and & & & & & & \\ **of A** & 28,000 m\({}^{2}\) & LoRaWAN & - & & SSCs & & & architecture & & & \\
**Cornha** & 28,000 m\({}^{2}\) & LoRaWAN & - & (Raspberry & Simulations & for multiple & Yes & Yes (3D) & Planning simulator & and empirical \\
**(This** & & & & & & & outdoor use & & & \\
**word3** & & & & & & & & cases & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._In addition, only a few solutions consider the use of fog computing, and even less made use of network planning tools. In comparison, the solution proposed in this article is one of the few academic solutions that deploys LoRaWAN infrastructure. Moreover, the proposed smart campus is almost the only one conceived from scratch to harness the benefits of fog computing. Regarding the efficiency in the network deployment in terms of cost, coverage, and the overall energy consumption, most of the academic papers do not give any insight regarding the heterogeneous network planning, although in some cases it is specified that it is low cost (without further details). Just a couple of systems give details for the hardware used to build the demonstrator at the level that is described later in this paper in Section 3.
## 2 Study Area and Data Used  
### The Study Area  
Zichang County is located in the north of Yan'an City, Shaanxi Province, China, between longitudes 109\({}^{\circ}\)11'58\" E and 110\({}^{\circ}\)01'22\" E and between latitudes 36\({}^{\circ}\)59'30\" N and 37\({}^{\circ}\)30'00\" N, with a total area of 2405 km\({}^{2}\) (Figure 1). Zichang County is a typical hilly and gully region of the Loess Plateau. The terrain is tilted from northwest to southeast, with an elevation of 933 to 1574 m. Zichang County prevails a warm temperate semi-arid continental monsoon climate, with low temperature and large temperature difference. The annual average temperature within the territory is 9.1 \({}^{\circ}\)C, the annual average precipitation is 514.7 mm. The rivers in the territory belong to the Yellow River system, which is divided into three tributaries: Qingjian River, Wuding River, and Yanhe River.
# Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios
## 2 Materials and Methods
### Datasets Description and Preparation  
#### 2.2.1 The 'Weeds' Public Dataset  
A public 'Weeds' dataset [54] was used for this trial to train the different YOLO models. The 'Weeds' dataset is a collection of weeds growing in lawns and in typical urban backgrounds that can easily confuse object detection models due to the similarity of the weeds with their surroundings. This dataset contains 4203 images with weeds labeled for 11,385 annotations. The 'Weeds' dataset contains approximately 62% of images of weeds with turf backgrounds, 13% of images with third surfaces (different floor patterns) backgrounds, and approximately 24% of images with both backgrounds (results from image analysis on 500 images sample). Weeds identified in this dataset were _Erigeron canadensis_ L (43%), _Sonchus_ spp. (23%), _Taraxcum officinal_ L (weber) (18%), _Oxalis_ spp. (4%), _Census_ spp. (3%) and a small percentage of unknown (results from image analysis on 500 images sample). No sufficient images for each species were provided in the dataset for training multi-class detectors; thus, only one class (Weeds) was assumed for this trial. The labels correlogram in the relationship between the position, width, and height of the dataset's objects (weeds) annotations. Generally, our datasets contain mostly small and stretched objects positioned at the center of the digital image. Before training the model, the images were cropped to obtain a resolution of 640 \(\times\) 640 pixels without applying any resizing and were subjected to the auto-orient function. The auto-orient strips images of their EXIF data to be displayed in the same way as they are stored on disk.  
To assess the model's performance on this dataset, a k-fold cross-validation was performed. K-fold cross-validation is a simple popular method for model evaluation. This procedure generally consists of dividing the dataset into k subsets after a random shuffle, training the model on k-1 subsets, and using the remaining subset to test the model. The evaluation scores produced are considered more reliable as a model performance summary. This dataset was divided into five subsets (two of 804 and three of 805 images); therefore, a five-fold cross-validation was performed. The best-performing models of each YOLO version were then trained on the dataset (train: 3664 images, validation: 359 images, and test: 180 images) and evaluated. The online platform Google Colaboratory (Colab), offered by Google, was used to implement and train the model. Colab, a cloud service based on Jupyter Notebooks, provides a free single 12 GB NVIDIA Tesla K80 GPU.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
\begin{tabular}{c} hsv (Ir: 0.015; s: 0.7, v: 0.4), \\ translate: 0.1, \\ scale: 0.5, \\ flip left-right: 0.5, \\ mosaic: 1.0 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters used for YOLOv5, YOLOv6, YOLOv7, and YOLOv8 models train.
# SOUNET: Self-Organized Underwater Wireless Sensor Network
## 5 Simulation Results
### Analysis of Results
#### 5.3.2 Packet Delivery Ratio  
The packet delivery ratio (PDR) of a node \(i\) is defined by:  
\[\rho_{i}=\frac{D^{i}_{\textit{defined}}}{D^{i}_{\textit{sent}}}, \tag{2}\]  
where \(D^{i}_{\textit{sent}}\) and \(D^{i}_{\textit{defined}}\) are the numbers of _DATA_ packets sent by node \(i\) and numbers of _DATA_ packets delivered to the sink, respectively. Figure 13 visualizes \(\rho_{i}\) by coloring in a way that the circles denoting the coverage of individual nodes become darker with higher PDR. It is shown that SOUNET has darker \(\rho_{i}\) over the whole area while Seaweb and S\({}_{13}\) have dark regions only near the sink. This is because descendants of the lost nodes (normally located at the outer region of the network) keep sending _DATA_ packets even though their lost parents are not able to relay them toward the sink. Rather, the packets cause more collisions to neighbors. On the other hand, SOUNET not only maintains high connectivity even at the edge of the network but also reduces unnecessary DATA transmission by forcing the isolated nodes to sleep.  
Figure 14 shows the average and standard deviation (SD) of \(\rho_{i}\), which are denoted by \(\mu_{\rho}\) and \(\sigma_{\rho}\), respectively, with varying \(\lambda\). For each value of \(\lambda\), the corresponding values of \(\mu_{\rho}\) and \(\sigma_{\rho}\) are obtained by running the whole simulation scenario of Figure 9. SOUNET shows better performance in terms of both \(\mu_{\rho}\) and \(\sigma_{\rho}\). That is, larger number of packets are successfully delivered to the sink and packets are more evenly collected from the entire network. That is because the SOUNET maintains high connectivity by detouring even when unexpected link holes occur. As \(\lambda\) increases, the funnel  
Figure 13: Visualization of \(\rho_{i}\) over the whole area: (**a**) SOUNET; (**b**) S\({}_{13}\); (**c**) Seaweb.  
appears and thus PDR averages of the all schemes become undistinguishable near 10%. On the other hand, \(\sigma_{\rho}\) of SOUNET, which is quite small under low traffic conditions, becomes larger as \(\lambda\) increases since some nodes selectively have larger collisions compared to others and then becomes smaller again as \(\lambda\) increases further since more nodes commonly experience the collisions.
## 3 Methodology
### Training Procedure  
* Datasets: We train on a hybrid dataset combining real (LOL, SID) and synthetic low-light images. For LOL, we use the standard split: 485 images for training and 15 for testing. For SID, we train only on the Sony subset and evaluate using its standard test set to avoid overlap.
* Preprocessing: All images are resized to 256 \(\times\) 256 or 512 \(\times\) 512 and normalized to \([0,1]\).
* Training Details: \[\begin{split}&\bigcirc\text{Optimizer: [PERSON]};\\ &\bigcirc\text{Learning Rate: }10^{-4},\text{ reduced on plateau};\\ &\bigcirc\text{Batch Size: }8\text{-}16\text{ (GPU-dependent)};\\ &\bigcirc\text{Epochs: }100\text{-}200.\end{split}\]
* Hardware: Training is conducted on an NVIDIA RTX-class GPU.  
We implement the model using PyTorch (version 2.2.2), and training takes approximately 24-48 h depending on resolution and dataset size. Full training configurations, including optimizer settings, learning rate schedules, batch sizes, and data augmentation strategies, are summarized in Appendix A.
## 3 Case Study: The UP2030 Project  
In order to illustrate with a real-life case study the methodologies and concepts proposed in this paper, an ongoing and large-scale urban planning and design project is introduced. The UP2030 project aims to lead the urban transition to climate neutrality through a strategic urban planning (SUP) approach ([[https://up2030-he.eu/about-project/](https://up2030-he.eu/about-project/)]([https://up2030-he.eu/about-project/](https://up2030-he.eu/about-project/)), accessed on 27 September 2024). This approach aims to update policies, enhance skills, and prototype solutions for broad implementation. The 5 UP concept outlines a strategy involving updating policies (UP-dating), building stakeholder capacities (UP-skilling), prototyping solutions (UP-grading), scaling up impacts (UP-scaling), and fostering communities of practice (UP-taking). Ten pilots are identified in various cities: Budapest, Granollers, Lisbon, Milan, Muenster, Rotterdam, Thessaloniki, Zagreb, Belfast, and Istanbul. A pilot defines a subarea (district) in the involved city.  
Reflecting the complexity of urban interactions and the importance of inclusiveness and spatial justice, the project focuses on technological and social innovations to redirect urban planning and design toward sustainability [30]. In this context, KPIs allow for the assessment of adopted policies and the progress toward climate neutrality goals. KPIs could be used to guide the selection of climate neutrality strategies to be adopted by policymakers [31]. [PERSON] and [PERSON] [32] proposed a framework consisting of 20 KPIs to monitor carbon footprint. The framework was tested on a university campus and was used to propose areas for improvement and accordingly recommendations. In order to enhance policy effectiveness, evidence-based policymaking is crucial, requiring comprehensive evaluation based on data collection and its analysis [33]. The definition of KPIs and the process of data collection pose challenges to the evaluation of urban policies [34]. Additionally, this evaluation requires expertise, which is often limited. These challenges are being addressed through advancements in data analytics and machine learning techniques, which offer tools for data analysis and facilitate decision-making and policy development [15]. Also, the current availability of open data improves access to information, supporting evidence-based policymaking [17].
# Particle Swarm Optimisation in Practice: Multiple Applications in a Digital Microscope System
## 6 Image Distortion Correction
### The Method
#### 6.3.1 Particle Initial Values  
The range of initialisation values for the homography matrix (\(h_{row,col}\)), polynomial constants (\(k_{1}\) to \(k_{4}\)) and the optical centre (\(O_{x},O_{y}\)) are given in Table 10.
# Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network
###### Abstract
The self-similar characteristics of traffic are considered as prior knowledge for prediction, which is added to the learning process of the neural  
Figure 1: Self-similarity of network traffic. network model, thus increasing the interpretability of the model. The main contributions of this paper are summarized as follows. 1. FPK-Net consists of a CNN and an LSTM based on an attention mechanism. The self-similarity property is incorporated into the training model before model training, which results in improvements in the extraction of traffic features and in the prediction accuracy when dealing with insufficient sample data. 2. The model incorporates the self-similarity property of network traffic as a priori knowledge into the intermediate structure of the deep network; namely, the Hurst exponent is added into the gating of the long short-term memory neural network (LSTM) as a bias term to increase the model's interpretability. 3. Experiments on publicly available datasets verified that the proposed model is consistent with the existing empirical evidence, and has better predictive power than other existing prediction methods. The accumulation of prior knowledge during training meaningfully guides the network traffic prediction, thus significantly improving the performance of the training model. In terms of the absolute coefficient of correction, the proposed model achieved values at least 10% higher than those of traditional statistical models. Thus, the reliability and superiority of the proposed model were illustrated in the article while the results demonstrated that the model is also interpretable.
# An Improved Confounding Effect Model for Software Defect Prediction
## 4 Experiments and Results
### Experiments for the Proposed Prediction Model  
#### 4.3.1 Experiments Set  
To verify the effectiveness of the LR model under controlling the confounding variable, we compared it with traditional LR and LR with the confounding effect removal method, SVM and NN. The removal method was proposed by [PERSON] et al. and can remove the confounding effects of size. We randomly selected 70% of the instances for training and the remaining 30% for testing. Precision and recall rates are indicators for evaluating classification models. The F1 score considers both precision and recall rates, which can be regarded as a harmonic average of them. We employ the widely used F1 score as the main indicator for objectively evaluating each predictive model. Its maximum value is 1, and its minimum value is 0. The criterion for the F1 score is that the larger the F1 score, the better the model performance. We run the experiments on the R platform.  
The description of baselines is as follows:  
1. LR: A two-step logistic regression is widely used in software defect prediction content. First, for each code metric, build a univariate logistic regression against defect proneness; second, metrics with significant correlations (_p_-value < 0.05) are used to establish a multivariate logistic regression to predict the defects.
2. LCERM+LR: Before applying the two-step LR model mentioned above, the LCERM, a method which could remove the confounding effects of the size metric, is applied. The LCERM uses linear regression to fit the size metric and one other metric, and this linear relationship between them is seen as the confounding effect. The removal of the confounding effect is achieved by subtracting the fitted value from the metric value. More descriptions can be seen in [16]. This paper uses the LOC(line of code) metric as the size metric.
3. SVM: To improve the predictive ability of the SVM model, we oversample the defect instances, standardize the original data, and perform principal component analysis transformation. The first five components are applied to the SVM model.
4. NN: To improve the predictive ability of the NN model, we oversample the defect instances, standardize the original data, and perform principal component analysis transformation. The first five components are applied to the NN model.