esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Fusion Feature Based Object Detection Method for High Resolution Optical Remote Sensing Images
## 5 Deep Feature Extraction with Context Scene Feature Fusion and Detection
### Results and Analysis  
#### 5.5.1 Dataset  
The two high-resolution remote sensing datasets used for object detection are described in Section 4.3.1. This section introduces the remote sensing scene classification datasets used in the training of seeNet networks and the two regular image datasets used for transfer learning. For remote sensing scene classification, there are two public datasets: UCMerced-LandUse [33] and Dataset-WHU-RS [33]. UCMerced-LandUse contains 21 different scene categories, each category containing 100 high-resolution remote sensing images of 256 \(\times\) 256 pixels. The Dataset-WHU-RS dataset contains 19 scene categories, a total of 950 images of 600 \(\times\) 600 pixels. In order to expand the training samples, this paper will simply divide one remote sensing image with 600 \(\times\) 600 pixels into nine 256 \(\times\) 256 pixel sub-images. In the end, the two remote sensing datasets are merged together, and the data of the same category are merged. In addition, fine-grained similar scenes such as sparse density residential, medium density residential area, and intensive residential area are merged. After merging, there were 26 scene classes, averaging about 320 images per class. In addition, in order to further increase the training data, simple horizontal, vertical flip operations were used.  
Next, the regular image scene classification dataset Places used in the transfer learning of seeNet network is introduced briefly. This dataset Places is a large-scale natural scene dataset, containing 205 categories and a total of 2.5 million images.  
The data used in the transfer learning of objNet network were mainly extracted from the ImageNet 2012 dataset, in which positive samples were all images in two categories of airplane and military airplane and some airplane images crawled from the Internet, a total of 12,300 images; for negative samples, we picked a category that may appear in the remote sensing images, such as ships, harbors, mountains, etc., and removed the other 980 categories, such as sharks, hens, caps, etc., which might be useless for object identification in remote sensing images. Figure 17 shows examples of the positive and negative samples of the dataset. For the convenience of the following description, this dataset is called NATURE-PLANE.
# Single-Task Joint Learning Model for an Online Multi-Object Tracking Framework
## 2 STJL Model Applied to Online Multi-Object Tracking  
An enhanced online multi-object tracking technique using a deep-feature model within a tracking-by-detection framework is proposed. By combining the deep feature model with the Kalman filter, it links detection boxes of targets in each frame. The algorithm runs iteratively until the image input ends, producing the tracking results.  
At time \(t\), an object detector scans the input image. Detected objects are passed to the tracking algorithm, which extracts features from detection boxes and uses the Kalman filter to predict target location and motion. The feature distance between the detection box and the tracker is calculated to determine similarity. Based on position and feature similarity, the algorithm links detection boxes to trackers and updates their states. If a detection box is not linked to a tracker, a new tracker is added if a new object is indicated or discarded if the instance is deemed a false positive. The algorithm also removes trackers if objects exit, then moves to the next image at time \(t+1\), repeating the process until the sequence ends, as shown in Figure 2.  
The following subsections detail the STJL model, covering its network architecture, training methods, object state prediction, tracking procedures, and linkage optimization using deep features. We also propose a novel tracker initialization method and explain the framework for evaluating tracker reliability.
## 3 ROR: Reliable Opportunistic Routing for CRSN
### Route Request  
When a node, \(i\), receives a RREQ packet from a node, which we will refer to as the requesting node, it checks the packet identification number to be sure it has not beforehand received the same packet beforehand. The node drops the packet if it was previously received. Else, it will only broadcast this RREQ packet if its eligibility (_EI_) determination operation returns a one. The eligibility determination is a binary operation that is determined as follows:  
\[El=\left\{\begin{aligned} & 1,\begin{cases}d_{sk}<d_{sk}^{req}\\ E_{th}\geq E_{th}^{min}\\ & clc=clc_{rreq}\\ R(t)\geq R_{t}\\ &\text{else}=0\end{cases}\right.\end{aligned} \tag{9}\]  
For the _EI_ operation to return a one, four conditions have to be met, which include the following. Node \(i\) checks to be sure if its own distance to the sink, \(d_{sk}\), is smaller than that of the requesting node, \(d_{sk}^{req}\). If Node \(i\) is closer to the sink, it proceeds to check the next condition, else, Node \(i\) drops the packet. This condition is to ensure that the packet always maintains a forward flow towards the sink. The second condition, \(E_{th}\geq E_{th}^{\min}\), ensures that the remaining energy of a node, \(E_{th}\), is above a minimum value, \(E_{th}^{\min}\). The third condition, \(clc=clc_{rreq}\), helps Node \(i\) check if it has the offered channel available by the requesting node in the \(clc_{rreq}\) field of the RREQ packet. If \(clc_{rreq}\) is available for use from its available list, \(clc\), Node \(i\) proceeds to check the next condition. Else, Node \(i\) refrains from broadcasting the RREQ packet. The last condition, \(R(t)\geq R_{t}\), is to help Node \(i\) confirm if the chosen channel in \(clc_{rreq}\) can support the needed traffic at its position. If the channel in the RREQ payload passes the local rate test at Node \(i\), Node \(i\) retains the channels in \(clc_{rreq}\) as the data channel and will not increment the channel switch count. If, however, there exists in its pool another channel with better quality performance than the channel offered by the requesting node, Node \(i\) replaces the same with its own in the RREQ payload and then increments the channel switch count. In that case, Node \(i\) will have different channels for both upstream and downstream communication. Node \(i\) then caches both the channel(s) and the requesting node's position to the sink. The requesting node will later be known as its upstream LMR node if the path is finally selected by the sink as the chosen path. Before Node \(i\) broadcasts the RREQ packet, it will also replace the requesting node's distance to the sink with its own, increment the hop count and then broadcast the packet. If Node \(i\), however, fails any one of these tests, it refrains from broadcasting the RREQ packet. This strategy, among other advantages, keeps the RREQ packet short, unlike in [28], wherein the RREQ packet size progressively increases with the hop count. This route request process continues until all disjoint possible routes to the sink are discovered.
# Performance Evaluation Method for Intelligent Computing Components for Space Applications
## 3 Evaluation Method
### High-Performance Computing Benchmark
#### 3.3.3 Local Response Normalization Benchmark  
Local response normalization (LRN) is a technique mainly used during deep learning training to improve accuracy, presented in 2012 by AlexNet [24]; its purpose is to perform local normalization on convolution values, and the specific calculation method can be found in Equation (3). LRN mimics the activity of biological neurons by creating a competitive mechanism, introducing competition between feature maps generated by adjacent convolution kernels. This makes significant features in feature maps more prominent in set A while being suppressed in adjacent feature maps, reducing the correlation between feature maps generated by different convolution kernels and enhancing the model's generalization ability. The operation of LRN involves normalizing the pixel values at that point in the channel.  
\[b_{x,y}^{i}=a_{x,y}^{i}/\left(k+\alpha\sum_{j=max(0,i-n/2)}^{min(N-1,j+n/2)} \left(a_{x,y}^{j}\right)^{2}\right)^{\beta} \tag{3}\]  
Using \(a_{x,y}^{i}\) to denote the activity of a neuron computed by applying kernel \(i\) at position \((x,y)\) and then applying the ReLu (rectified linear unit) nonlinearity, \(N\) is the the total number of kernels. The constants \(k,n,\alpha,\) and \(\beta\) are hyper-parameters, the values of which are determined using a validation set; we used \(k=2,n=5,\alpha=10^{-4}\), and \(\beta=0.75\).
## 2 System Model
### Communication Model
#### 2.3.3 Beamforming Strategy Using MUSIC Algorithm  
To enhance communication efficiency, we apply hybrid beamforming based on the MUSIC (Multiple Signal Classification) algorithm. The estimated Direction of Arrival (DOA) is used to compute the beamforming weights. The MUSIC spectrum is given by  
\[P_{\text{MUSIC}}(\theta,\phi)=\frac{1}{a^{H}(\theta,\phi)E_{n}E_{n}^{H}a( \theta,\phi)}, \tag{16}\]  
where \(a(\theta,\phi)\) is the steering vector for azimuth angle \(\phi\) and elevation angle \(\theta\), \(E_{n}\) is the eigenvector matrix of the noise subspace.  
The estimated DOA \((\hat{\theta},\hat{\phi})\) determines the beamforming weight vector:  
\[w=a^{H}(\hat{\theta},\hat{\phi}). \tag{17}\]  
Applying the beamforming weight to the received signal \(X\), the output signal is  
\[y=w^{H}X. \tag{18}\]  
This process enables high-precision beamforming, effectively focusing transmission energy toward intended users, thereby improving signal quality and system capacity.
## 1 Introduction
To minimize transmission values between antenna elements as well as corresponding mutual coupling, a resonator layer was designed and integrated to the cross dipole antenna. Afterwards, 48 different types of resonators were tested with a cross dipole antenna, the obtained results were transferred to the neural network, and the network was then trained. Moreover, a network model was tested and one of the test resonators was fabricated and experimentally investigated.
# Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network
## 2 Material and Methods
### Internet of Things
#### 2.1.2 Communication Protocols  
Communication protocols define the rules to communicate between nodes, allowing them to send data between devices and users in a safe and reliable way. We can divide communication protocols into two types: (1) request-response and (2) publication-subscription.  
The most used protocol based on request-response is the Hypertext Transfer Protocol (HTTP). This is one of the most stable and reliable protocols and allows the transmission of large amounts of data. Its disadvantages are high memory and energy consumptions, making it challenging to use at the edge.  
The most used protocol based on publication-subscription, is Message Queuing Telemetry Transport (MQTT), which is a lightweight protocol that allows scalability and simplifies communication between devices (despite not having an encryption mechanism). It is suitable for sending small amounts of data in low bandwidth and high latency scenarios typical on the edge. It is also optimised to work on devices with memory constraints and has other advantages, namely in terms of privacy issues. Publishers and subscribers do not need to know each other's existence. One subscriber can receive data from many devices, and the publisher and subscriber do not need to be connected at the same time [16; 17].  
Figure 1: Types of computation (adapted from [14]).  
After choosing the types of computation and communication protocols, it is necessary to define how to arrange the nodes of the IoT network and how to connect these nodes. This arrangement of nodes and how they communicate with each other defines the network topology.
# DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT
## 4 Experiments
### Dataset
#### 4.1.2 Relation Extraction Dataset  
For a knowledge graph-based relationship extraction model, we utilize ConceptNet data and TACRED data sets. ConceptNet is a dataset that is used to embed knowledge graphs, and only English data out of the ConceptNet 5.6 dataset is used for embedding learning.  
The TACRED dataset is used not only to learn the relationship extraction model but also to verify the proposed DeNERT-KG model. TACRED data can be obtained through the Linguistic Data Consortium (LSD), consisting of a total of 106,264 sentences. It is separated into train, dev, and test set and contains 68,124; 22,631; and 15,509 sentences, respectively. A relationship extraction dataset made from Coppers is used in the TAC Knowledge Base Population Challenge, containing a total of 42 relationships, including \"no_relation\".  
Table 2 shows the relationship information of TACRED. It has a total of 42 types of relationship information related to people and institutions, including \"no_relation\" that has no relationship. The TACRED dataset is used as a very important dataset for learning and validating the model we propose because it has tagged information as well as relationship information.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Category** & **Train** & **Dev** & **Test** \\ \hline \multirow{4}{*}{CoNLL 2003} & PER & 6600 & 1842 & 1617 \\  & LOC & 7140 & 1837 & 1668 \\  & ORG & 6321 & 1341 & 1661 \\  & MISC & 3438 & 922 & 702 \\ \hline \multirow{4}{*}{EWNERTC} & People & 283,188 & 35,399 & 35,398 \\  & Location & 237,054 & 29,632 & 29,632 \\ \cline{1-1}  & Organization & 60,752 & 7594 & 7594 \\ \cline{1-1}  & Misc & 778,781 & 97,348 & 97,348 \\ \hline \multirow{4}{*}{W-NUT 2007} & Person & 995 & 587 & 560 \\  & Location & 793 & 107 & 244 \\ \cline{1-1}  & Group & 414 & 64 & 235 \\ \cline{1-1}  & Creative-Work & 346 & 238 & 360 \\ \cline{1-1}  & Product & 345 & 208 & 253 \\ \cline{1-1}  & Corporation & 267 & 46 & 88 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of named entity recognition (NER) datasets.
# Weather-Aware Long-Range Traffic Forecast Using Multi-Module Deep Neural Network
## 4 Experimental Results and Discussion
### Experimental Setup
Experiments were performed using a Linux-based workstation having an Intel i7-7700 CPU with 32 GB RAM and a GPGPU of the Nvidia GeForce GTX 1080. The prediction models were programmed using scikit-learn, TensorFlow, and pmdarima packages. Prediction errors are expressed using standard forms such as the mean absolute error (MAE) and mean absolute percentage error (MAPE). They are computed as shown below:  
MAE \[\triangleq \frac{\sum_{i=1}^{n}|A_{i}-F_{i}|}{n},\] (3) MAPE \[\triangleq \frac{100\%}{n}\times\sum_{i=1}^{n}\left|\frac{A_{i}-F_{i}}{A_{i }}\right|,\] (4)  
where \(A_{i}\), \(F_{i}\), and \(n\) denote the real (measured) value, forecast value, and amount of data, respectively.
# A Color Consistency Processing Method for HY-1C Images of Antarctica
###### Abstract  
The HY-1C satellite, as part of China's optical satellite constellation for global ocean monitoring, monitors the ocean and coastal environment by the three broad visible bands of the Coastal Zone Imager (CZI) instrument. However, as a result of the sensor instrument noise, the atmospheric environment during imaging, and the shooting angle, the satellite images often show uneven illumination and inconsistent color between neighboring images. In this paper, according to the characteristics of the HY-1C CZI instrument, we propose a color consistency processing framework for coastal zone images of Antarctica. First of all, the high-frequency and low-frequency information of the image is separated by a statistical filter with simple clustering. The uneven lighting is then replaced by artificial lighting, which is globally uniform. Finally, the color difference between images is corrected by a color transfer method. In order to evaluate the color consistency results quantitatively, a new quantitative evaluation method is proposed. The experimental results for the coastal zone images of Antarctica show that the new processing framework can effectively eliminate the unevenness in the lighting and color. The mosaic results show a good performance in consistent lighting and tones, and the lack of visible mosaic lines proves the effectiveness of the proposed method. The quantitative evaluation analysis confirms the superiority of the proposed method over the Wallis method.  
HY-1C satellite images; Antarctica; mosaic preprocessing; color consistency +
Footnote †: journal: Remote Sensing, 2020, 12, 1143; doi:10.3390/rs12071143
# A Multi-Gateway Behaviour Study for Traffic-Oriented LoRaWAN Deployment
## 2 Fundamentals of LoRaWAN Technology
### LoRaWAN (Long-Range Wide-Area Network)  
LoRaWAN is an open-source protocol developed and supported by the LoRa Alliance community. This MAC protocol offers self-optimization possibilities thanks to a few native mechanisms, such as the adaptive data rate (ADR) or the transmission management based on channel listening called listen before talk (LBT) and adaptive frequency agility (AFA), which are similar to the CSMA channel access protocol [1]. These mechanisms aim to reduce the DC limitation, which corresponds to the maximum occupation time authorized on an ISM channel, and minimize interference and power consumption while ensuring long-range communication.
# Food Processing with UHP Waterjets
## 4 Selected Cutting Applications
### Vegetables
#### 4.4.2 Potatoes, Onions, Carrots, and Sugarcane
They did not explore using a waterjet with sodium chloride to gain the benefits of both in eliminating the whitening effect. [PERSON] and [PERSON] [28] demonstrated that carrot crowning with high-pressure waterjets is highly productive at a rate of 15 to 20 ton/hour for 9 cm diameter carrots at a pressure of 276 MPa. The need for a vision system for accurate crowning was suggested. The waterjet cutting of the crown before the lye peeling bath is a significant advantage that is enabled by the waterjet trimming process, which reduces the time in the lye bath and the good carrot tissue loss. Figure 23: Onion slicing with using a UHP nozzle manifold. Figure 22: Comparison between waterjet- and blade-cut surfaces. [PERSON] et al. [29] studied the harvesting of sugar cane with waterjets and successfully cut sugarcane stalks in the laboratory with 0.36 mm jets at 400 MPa of pressure. It was found that the standoff distance and jet power levels are the most significant factors. They concluded that the requirements for a successful mobile waterjet field unit are not met, rendering harvesting with waterjets impractical. [PERSON] and [PERSON] [30] conducted a simulation study using abrasive waterjets at 15 kW and a pressure of 360 MPa. The orifice and mixing tube were 0.25 mm and 0.76 mm, respectively. They also selected 80-mesh river-sand abrasives for their analysis. The experimental results showed that the system was able to cut sugarcane stalks completely at a much farther standoff distance by reducing the traverse speed. The study also showed that cutting sugarcanes of 30 and 120 mm diameters would require a traverse speed of 4.4 km/hr. and 1.1 km/hr., respectively. The results implied that limitations should be set to no more than a 210 mm standoff distance with a minimum traverse speed of 0.6 km/h. There are many other vegetables that are being processed with waterjets worldwide. For example, waterjet cutters are being used in South and Central America for tipping green beans. Two 0.13 mm orifices powered by a 10 kW intensifier pump will trim about 1600 Kg per hour. Snapping green beans by hand is labor intensive and bruises and bends green beans which diminishes quality. There is currently additional interest in using waterjet technology for cutting asparagus, chunking melons, and capping strawberries.
## 3 Results and Discussion
### Optimization Results Using PSO without and with DG integration
#### 3.2.3 Scenario 3: One DG Placed at Bus 16 Based on an Arbitrary Approach  
For this scenario, one DG unit, whose placement at bus 16 was arbitrarily selected, was installed. The initial voltage magnitude at the point of connection was 0.9501 pu. After simulations with the PSO, the voltage was found to be optimized when the convergence of the solution was attained. The results of the voltage profile before and after the installation of DG are presented in Table 5. It can be found that voltage magnitudes at all buses were found to be within the prescribed limits. It can, however, be seen that the improvement in the voltage was not all that significant compared to that of the second case in which the location of the DG was based on the CF ranking values. As presented in Table 5, while the minimum voltage magnitude for the system under this scenario was 0.9535 pu at bus 18, the maximum voltage magnitude was 0.9981 pu at bus 2. The system network real power loss obtained after the installation of a DG at bus 16 was found to be 1.1265 MW, which represents about a 9.71% increase in power loss with respect to the base case power loss. Consequently, it can be seen that though the voltage profiles at all buses of the system were slightly enhanced, after the integration of a DG at bus 16, a high increase in power loss was obtained. This implies that bus 16 was not a suitable location for the placement of the DG in order to simultaneously improve the voltage magnitudes at all buses in the system and minimize power losses in the system.  
Figure 4: Base case voltage profile for the IEEE 33-bus system.
## 2 Theoretical Model
### Actuator Piston  
The pressure of the fluid in the actuator piston chamber is affected by the flow from the pipe \(Q_{3}\), the position of the actuator piston \(x_{a}\), and the leakage flow \(Q_{l3}\). Hence, the continuity equation of flow can be expressed as follows:  
\[Q_{3}(s)=A_{s}sX_{a}(s)+C_{3}P_{3}(s)+\frac{V_{3}}{K_{oil}}sP_{3}(s) \tag{23}\]  
where \(C_{3}\) is the leakage flow coefficient.  
Equation (23) can also be written as a four-pole form as below:  
\[\left[\begin{array}{c}P_{3}(s)\\ Q_{3}(s)\end{array}\right]=\left[\begin{array}{cc}1&0\\ C_{3}+\frac{V_{3}}{K_{oil}}s&A_{a}s\end{array}\right]\left[\begin{array}{c}P _{3}(s)\\ X_{a}(s)\end{array}\right]=\mathbf{A}_{3a}\left[\begin{array}{c}P_{3}(s)\\ X_{a}(s)\end{array}\right] \tag{24}\]  
The torque \(T_{a}\) from the actuator piston can be expressed as below:  
\[T_{a}(s)=l_{s}\Big{[}P_{3}(s)A_{a}-m_{a}s^{2}X_{a}(s)-b_{a}sX_{a}(s)\Big{]} \tag{25}\]  
\[\frac{P_{3}(s)}{X_{a}(s)}=\frac{1}{A_{a}}\bigg{(}\frac{Z_{s}}{l_{s}^{2}}+m_{a }s^{2}+b_{a}s\Big{)}=\mathrm{G}_{4}(s) \tag{26}\]  
where the equivalent impedance \(Z_{s}\) can be expressed as follows:  
\[Z_{s}=\frac{T_{a}(s)}{\varphi(s)}=J_{s}s^{2}+b_{s}s+K_{s}l_{s}^{2} \tag{27}\]  
In Equation (27), \(l_{s}\) is the moment arm length of the bias spring force and \(\varphi\) is the swash plate oscillation angle.