esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Spatial-Temporal Self-Attention Network for Traffic Flow Prediction
## 4 Methodology
### Model Training Strategy  
This research utilises Huber loss [65] as the objective function. In contrast to the MAE and MSE loss functions, the Huber loss function combines the strengths of both while being less sensitive to outliers, thus exhibiting higher robustness. This makes it more suitable for predicting spatial-temporal sequence data. The Huber loss function is defined as follows:  
\[\mathcal{L}_{\delta}\big{(}\mathcal{Y},\hat{\mathcal{Y}}_{i}\big{)}\begin{cases} \frac{1}{2}\big{(}\mathcal{Y}-\hat{\mathcal{Y}}_{i}\big{)}^{2},&if\;\big{|} \mathcal{Y}-\hat{\mathcal{Y}}_{i}\big{|}\leq\delta\\ \delta|\mathcal{Y}-\hat{\mathcal{Y}}_{i}|-\frac{1}{2}\delta^{2},&if\;\;\big{|} \mathcal{Y}-\hat{\mathcal{Y}}_{i}\big{|}>\delta\end{cases} \tag{17}\]  
where \(\mathcal{Y}\) denotes the ground truth, \(\hat{\mathcal{Y}}_{i}\) denotes the predicted value for the \(i\)th sample, and \(\delta\) is a threshold used to control the smoothing range.
## 2 Related Works
### Contributions  
Our study addresses the limitations highlighted in previous research by introducing a more comprehensive and integrated framework for smart agriculture that combines IoT platforms with advanced machine learning techniques such as pseudo-labeling. Specifically, we propose a novel approach that integrates pseudo-labeling into the semantic segmentation of time-series data, providing a more accurate and efficient method for data analysis in agricultural environments.  
One of the key contributions of this research is the development of an automated pseudo-labeling process that significantly reduces the manual effort typically required for labeling large agricultural datasets. This allows for the rapid expansion of labeled datasets, thereby improving the scalability and efficiency of machine learning models used for agricultural decision making. By iteratively refining pseudo-labels, our approach ensures higher accuracy in model training, thereby improving predictions related to irrigation management, crop health monitoring, and equipment maintenance.  
Our method also incorporates the use of real-time data processing techniques facilitated by edge and fog computing to address latency issues and network limitations that are common in remote agricultural settings. By processing data closer to the source, our system enhances the real-time responsiveness required for critical agricultural operations such as automated irrigation and pest detection. This not only increases operational efficiency but also optimizes resource use, promoting agricultural sustainability.  
Moreover, we contribute to the field by providing a more sophisticated analysis of time-series data, particularly through the integration of semantic segmentation. Unlike traditional methods that analyze entire datasets, our approach allows for more granular analysis based on short-term data patterns, improving the precision and relevance of predictive models. This is particularly important in agriculture, where environmental conditions can change rapidly, and timely responses are important in maintaining productivity.  
In summary, our contributions can be summarized as follows:  
* A novel pseudo-labeling framework that automates the labeling of agricultural time-series data, enhancing scalability and model accuracy.
* Integration of edge and fog computing to address latency issues and improve real-time decision making in smart agriculture.
* Advanced semantic time-series data segmentation techniques, allowing for more precise and context-aware predictions in agricultural systems.
* A holistic approach that combines IoT platforms with machine learning to create a robust, scalable system for managing complex agricultural environments, thereby supporting the development of intelligent and sustainable farming practices.  
By addressing the gaps in current research, our study offers a significant advancement in the application of agricultural machine learning and IoT technologies, providing opportunities to develop more adaptive, resilient, and efficient agricultural systems.
## 2 Morphological Reconstruction and Multi-feature Analysis for TIR Ship Images
### Gray-Level Morphological Reconstruction for Sea Clutter Suppression and Saliency Detection
#### 2.2.3 Brightness Contrast Saliency Detection  
As can be seen from Figure 5d-f, the pre-processed images based on OGMR or CGMR can efficiently remove dense and intricate sea clutter while completely preserving the ship target signals including intensity, shape, and contour information. Therefore, considering the favorable performances of OGMR or CGMR pre-processed images, we try to fully exploit the brightness uniformity and the local region contrast of TIR ship target to further enhance ship target and suppress trivial clutter. Firstly,  
Figure 6: The processed results of TIR ship images by intensity foreground saliency map (IFSM). (**a**) The estimated background of Figure 5a by OGMR, (**e**) the estimated background of Figure 5b by CGMR, (**f**) the estimated background of Figure 5c by OGMR, (**d**) the result of (**a**) processed by \(IFSM_{b}\), (**e**) the result of (**b**) processed by \(IFSM_{d}\), (**f**) the result of (**c**) processed by \(IFSM_{b}\).  
similar to cascade morphological operation, the opening or closing operation with selected structuring element \(B\) is employed on the pre-processed TIR ship image obtained by CGMR or OGMR to perceive trivial background clutter. Then, the proposed brightness contrast saliency map (BCSM) for bright (dark) infrared ship image is defined as follows:  
\[BCSM_{b}(x,y)=CGMR_{f}[h](x,y)-CGMR_{f}[h]\circ B(x,y), \tag{14}\]  
\[BCSM_{d}(x,y)=OGMR_{f}[g]\bullet B(x,y)-OGMR_{f}[g](x,y), \tag{15}\]  
By the definition of BCSM, the BCSM can be simply treated as two steps: The first step utilizes CGMR or OGMR to efficiently remove heavy sea clutter according to region-neighbor contrast, and the second step uses opening or closing operation to perceive trivial background clutter and pixel-wise subtraction to probe bright or dark regions where potential ship targets may exist. Figure 7 shows the processed results of TIR ship images by BCSM. The pre-processed infrared ship images based on CGMR or OGMR are shown in the first row, and the second row displays their acquired BCSM results, respectively. As can be seen by comparing Figures 7d-f and 3d-f, for these three images, the proposed BCSM outperforms the classical morphological filters, because the proposed BCSM algorithm fully exploits the information of the spatially local region contrast and brightness uniformity of the ship targets.  
Figure 7: The results of TIR ship images processed by brightness contrast saliency map (BCSM). (**a**–**c**) are pre-processed TIR images by gray-level morphological reconstruction, (**d**) the result of (**a**) processed by \(BCSM_{b}\), (**e**) the result of (**b**) processed by \(BCSM_{d}\), (**f**) the result of (**c**) processed by \(BCSM_{b}\).
## 6 Numerical Simulation Analysis of the Dual-Electrode Excitation Method  
Taking into account the engineering application environment, the following parameters are set: excitation current \(I_{s}\) = 10 A; electrode spacing \(L\) = 10 m; drilling well radius \(r_{d}\) = 150 mm; distance measurement tool radius \(r_{t}\) = 100 mm; formation resistivity\(R_{f}\)= 10 \(\Omega\cdot\)m; mud resistivity \(R_{m}\) = 1 \(\Omega\cdot\)m; casing conductivity \(\sigma_{c}\) = 1.9 \(\times\) 10\({}^{6}\) S/m; casing radius \(r_{c}\) = 150 mm; casing wall thickness \(h_{c}\) = 12 mm; distance measurement \(d\) = 10 m; and axis angle \(\theta\) = 0\({}^{\circ}\). It is assumed that the electrode is a point electrode, and the casing is infinitely long.  
Based on the established (\(X\),\(Y\),\(Z\)) coordinate system in Figure 1, with Q as the origin, the numerical simulation analysis is performed using the above parameters within the range of (\(-\)30 m\(\sim\)30 m), according to the distance positioning calculation method.
# Fronthaul Design for Wireless Networks
## 2 Background on C-RAN and Fronthaul Communication Technologies and Related Work
### Cloud Radio Access Network and Fronthaul  
In its traditional architecture, the RAN comprises costly stand-alone Base Stations (BSs), each one serving a certain area and thereby only handling transmitted and received signals of the User Equipment (UE) within that same area. In order to attain high data rate coverage, a large number of BSs is required, which also poses some challenges, such as the costly initial investment, site rental, and site support; thus, an increase in the number of BSs gives rise to a significant increase in CAPEX and OPEX. On the other hand, traditional BSs are configured to fulfill peak demands in order to reduce outages; however, since the average network load is usually far lower than that in peak load, the BS utilization rate ends up being low. Noticing that the processing capacity of each BS cannot be shared with others under the traditional RAN architecture, reducing the number of BSs or their processing resources is not an option to optimize their utilization efficiency, as it would lead to many and unacceptable congestion events during peak hours, or even a lack of coverage issues.  
In order to reduce the overall expenses of deployment and operations, the C-RAN architecture was proposed: a radio access network model in which baseband resources are pooled so that BSs can share them. This is achieved by separating the traditional BS into an RRH, which is responsible for power amplification and analog processing, and a BBU, which manages the common digital baseband functions, such as modulation and coding. The optimization of the BBU usage is reached through the virtualization of a BBU pool, which can then be shared among cell sites, i.e., RRHs. Figure 1 depicts a typical C-RAN architecture, which highlights the backhaul segment, i.e., the connection between the core network and the BBU, and also the new connectivity segment--the fronthaul--introducedby the C-RAN architecture between the multiple distributed RRHs and the BBU. According to the related literature [4; 5], a C-RAN presents the following benefits relative to a traditional RAN: energy efficiency, decrease in CAPEX and OPEX, throughput improvement, adaptability to non-uniform traffic, and smart Internet traffic offload.  
One of the main technical challenges regarding the C-RAN architecture is the large amounts of baseband sampling data that have to be carried out in real time between RRHs and the BBU pool. Besides strict latency requirements, the wideband required by some radio access systems, like Long-Term Evolution (LTE) or 5G, implies that each fronthaul link should support substantial bit rates, e.g., 1 Gbps [6].
# Gap Analysis of Ambient Electromagnetic Noise Measurements Stored in the ITU Data Banks
## 1 Introduction
### Man-Made Radio Noise  
The radio noise environment is very different in cities and residential areas, where the population is dense, and where the density of electrical and electronic devices is high. Each of these devices emits a small amount of electro-magnetic radiation, and all these components add up to an electromagnetic field that resembles additive white Gaussian noise (AWGN) [13]. The cumulative field strength of this'man-made noise' is significantly higher than the natural noise. Often an impulse noise (IN) component maybe superimposed on the AWGN component, the amplitude of which will depend on the pulse width of the source and the sensor (receiver) bandwidth. If there is one or a small number of man-made noise sources nearby, single-carrier noise (SCN) may be seen in the spectrum, consisting of one or multiple narrow-band noise-like signals, carriers or noisy spectral 'humps' [14]. Especially in an indoor environment the likelihood of one or a few sources being in close range is high, and as a result a mix of AWGN, IN and SCN is observed.
# Mission-Oriented Real-Time Health Assessments of Microsatellite Swarm Orbits
## 4 Simulation and Analysis
### Simulation Results  
#### 4.2.1 Normal-to-Life Health Assessment Simulation  
Assuming that the on-orbit operational lifetime of the swarm reaches the normal operating time, i.e., normal to life, a health assessment of the swarm is performed to verify the validity of the assessment algorithm in the following steps.  
1. According to Equations (8)-(10), the reliability curve of a single satellite is shown in Figure 4a; 2. Combining Equations (3)-(7) and calculating the weights of each satellite, the reliability curves of all satellites of the swarm can be obtained, as shown in Figure 4b; 3. Similarly, from Equations (12)-(16), we can find the weight values of each satellite and communication link in the swarm, and then obtain the reliability curves of all intersatellite communication links, as shown in Figure 5;  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Satellite lifetime & \(12\times 8.76\times 10^{3}\) h \\ End of lifetime & \(10\times 8.76\times 10^{3}\) h \\ Number of satellites (\(N\)) & 62 \\ Several InterSat Comm. Links (\(M\)) & 175 \\ Scale parameter (\(\beta\)) & 1.46 \\ Shape parameter (\(\eta\)) & 2.96742 \(\times 10^{5}\) \\ Communication range (R) & 400 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Star swarm model parameters.  
Figure 4: Single-satellite reliability curves for star swarms: (**a**) Satellite reliability model; (**b**) Reliability models for all satellites in the swarm.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline X-axis position error & 0.12420713 \\ Y-axis position error & 0.090394914 \\ Z-axis position error & 0.25590956 \\ X-axis attitude error & 0.19691368 \\ Y-axis attitude error & 0.099702608 \\ Z-axis attitude error & 0.09746243 \\ Energy consumption error & 0.135409679 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance evaluation index parameters.  
Finally, the weight values and the reliability model parameters are substituted into Equation (25) to derive the health curves of the star swarm in Figure 6.  
As observed from Figure 6, the health of the swarm first decreases smoothly with time in the normal-to-life condition, and then enters the Subhealth stage from the Health stage at approximately 6.7 \(\times\) 10\({}^{4}\) h. The health changes to a rapid decline at this stage and enters the failure-prone stage at approximately 9.8 \(\times\) 10\({}^{4}\) h (approximately 11 years), and the whole process is consistent with the general spacecraft failure \"horsetail curve\". The general swarm works together to form a virtual large satellite, and thus, the health state of the swarm is similar to that of a general large satellite, which further demonstrates the effectiveness of the real-time health assessment algorithm in this thesis.
# Closing Editorial for Computer Vision and Pattern Recognition Based on Deep Learning
## 7 Segmentation  
[PERSON] et al. [30] were the first to evaluate and compare the performance of state-of-the-art instance segmentation models by focusing on their inference time in a fixed experimental environment. They proposed the accuracy and speed of the models in a fixed hardware environment for quantitative and qualitative analyses.  
[PERSON] and [PERSON] [31] proposed an enhanced MaskRCNN framework for the semantic segmentation of satellite images, specifically targeting the detection and segmentation of shadow cumulus clouds, which play a crucial role in environmental and climate analysis. The proposed approach incorporates two deep neural network architectures leveraging auxiliary loss and feature fusion functions, aimed at improving segmentation accuracy.
# Online Determination of Boron Isotope Ratio in Boron Trifluoride by Infrared Spectroscopy
## 3 Results and Discussion
### Influence of the Pressure and Temperature  
Due to the effect of the concentration of BF\({}_{3}\), absorbance values grew with an increase in pressure. In addition, an increase in the temperature made the peak pattern wilder. Figure 6A,B show the influence of pressure and temperature on the isotope ratio of \({}^{10}\)B/\({}^{11}\)B, with the ranges of pressure and temperature being the possible interval values for the chemical exchange rectification operation. It was found that both the pressure and temperature have little impact on the isotope ratio determination results. Nevertheless, the optimum operation temperature was in the range of 278 to 298 K, because the errors might increase significantly when the temperature is higher than 303 K.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multicolumn{1}{c}{\({}^{10}\)B/\({}^{11}\)B} & \multicolumn{5}{c}{Sample (\({}^{10}\)B/\({}^{11}\)B, by IR Method)} & \multicolumn{2}{c}{**Mean**} & \multirow{2}{*}{\(\mathcal{S}\)} & \multirow{2}{*}{**RSD/\%**} \\ \cline{2-2} \cline{5-9}
**(by ICP-MS)** & & & & & & & & **Value** & \\ \hline
1.2351 & 1.2290 & 1.2382 & 1.2401 & 1.2320 & 1.2355 & 1.2413 & 1.2360 & 0.0048 & 0.39 \\ \hline
0.2473 & 0.2405 & 0.2428 & 0.2451 & 0.2422 & 0.2506 & 0.2472 & 0.2447 & 0.0037 & 1.52 \\ \hline
0.0506 & 0.0559 & 0.0532 & 0.0485 & 0.0511 & 0.0463 & 0.0509 & 0.0510 & 0.0034 & 6.64 \\ \hline \end{tabular}
\end{table}
Table 4: Precision experiment for the IR method.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{5}{c}{\(A_{10}\)/\(A_{11}\) **(by IR Method)**} & **Mean Value** & **RSD/\%** & \({}^{10}\)B/\({}^{11}\)B (by ICP-MS \({}^{1}\))** & \(K\) \\ \hline
0.4508 & 0.4513 & 0.4634 & & & & \\
0.4585 & 0.4601 & 0.4512 & & & & \\
0.4535 & 0.4498 & 0.4515 & 0.4553 & 1.16 & 0.2473 \(\pm\) 0.0009 & 0.5431 \\
0.4626 & & & & & & \\ \hline \end{tabular} \({}^{1}\) mean and standard deviation were obtained from 3 determinations.  
\end{table}
Table 3: Investigation of the correction factor by continuous determination of the ratio of \(A_{10}/A_{11}\) of the natural abundance BF\({}_{3}\).
# MinHash-Based Fuzzy Keyword Search of Encrypted Data across Multiple Cloud Servers
## The Proposed Fuzzy Keyword Search Scheme Based on MinHash
### Implementation of the Algorithm
#### 3.4.2 The Search Algorithm  
Figure 2 also illustrates the principle of fuzzy search based on MinHash keyword fingerprints. Assume that the user needs to retrieve keyword w = encrypt. Due maybe to the carelessness of the user, the keyword that is actually entered is w\({}^{\prime}\) = encrypt, i.e., y is misspelled as i. Using the traditional hash algorithm, any difference between two keywords will result in two completely different hash values. Using the keyword fingerprint generation algorithm, however, since the keywords have undergone n-gram processing, the value of the keyword fingerprint is determined by a plurality of elements in the gramset. When the user misspells one letter, only two out of the six elements in the gramset are changed, i.e., ry becomes ri and yp becomes ip. Although the hash values of the two elements result in differences (dotted box in Figure 2), since the fingerprint is derived from mapping the hash values of six elements, the other four unaltered elements play a dominant role in the value of the fingerprint. Therefore, the Jaccard distance between the fingerprint of the misspelled keyword and that of the correct keyword is smaller than the fingerprint of other irrelevant keywords. Using this technique, the user can search for the correct keyword by comparing the Jaccard distance between the fingerprints even in the case of some misspellings. As can be seen in Figure 2, since the Jaccard distance between fingerprints S\({}_{1}\) and S\({}_{2}\) through calculation is 2, it can be determined that the keyword \"encrypt\" corresponding to fingerprint S\({}_{1}\) could be a keyword that the user can use to do the search. During the search phase, the cloud server will also calculate the Jaccard distance J\({}_{\text{w}_{1}}\) of the fingerprint trap gate T\({}_{\text{w}}\) and the index S\({}_{i}\) in the index I\({}_{\text{w}_{1}}=\{\text{S}_{i},\text{FID}_{\text{w}_{1}}\}\) according to the same principle. Therefore, according to Jaccard distance J\({}_{\text{w}_{1}}\), the set of top-k ciphertext documents C\({}^{\prime}\) = (c\({}_{1}\), c\({}_{2}\),..., c\({}_{k}\)) are returned to the user.
## 3 Experimental Results and Analysis
### Practical Experiment
The corresponding STD value is 0.0195, and the RMSE is 0.0220. However, it can be also observed that between 11 s and 12 s, as indicated by the dashed oval annotation, additional noises were introduced through traditional SOBI, which degraded the accuracy of the monitoring signal. (3) When using the traditional SOBI method for noise reduction, only the first component of the separated signal is retained, and the remaining signal is discarded as noise, in order to achieve the noise reduction effect, which leads to the introduction of additional noise. The improved SOBI method optimizes both the mixing matrix and the separated signals, preserving useful information and the main frequency components while achieving noise reduction, as shown in Figure 14. Although the differences in evaluation metrics after the improved SOBI processing are not significant, there is a certain improvement in STD, RVR, and NCC, indicating that this method addresses the additional noise introduced by the traditional approach while maintaining SOBI's overall noise reduction performance for the signal. (4) The gradual de-noising method proposed in this paper, after the initial noise reduction using I-SOBI, further addresses the high-frequency noises in the signals. It ensures that the final de-noised signal is smoother. Compared to the original signal, the proposed method has decreased the STD by 24.60% and decreased the RMSE by 14.17%. Additionally, the RVR has been reduced from 0.7741 to 0.7561, and the NCC has been improved from 0.9452 to 0.9515, compared to the traditional SOBI method.
# Zero Energy IoT Devices in Smart Cities Using RF Energy Harvesting
## 2 Literature
### Wireless Power Communication Networking (WPCN)  
WPCN is RF-EH-based communication networking that uses the uplink and downlink concept for wireless energy transmission (WET) and wireless information transmission (WIT). The basic idea of WPCN is as follows: each node harvests energy from RF and transmits this energy wirelessly to other nodes by using the downlink. Other nodes use this harvested energy to transmit the data to the destination over the uplink. The authors of [6] presented an efficient data collection algorithm for WPCN-based WSNs. In the algorithm, WPCN nodes harvest their energy from RF and store it in capacitors or rechargeable batteries. The downlink is used for WET, and the uplink is used for WIT at the same time. The authors assume a one-hop star topology in which the sink node is surrounded by sensor nodes, and the sensor nodes harvest their energy from the sink node. The sink node collects the sensed data from the nodes by using the uplink and sends wireless energy to one hope node by using the downlink. The authors investigate the throughput of the node during the collection of sense data to the sink per unit time. MAC is a layer 2 protocol for communication between nodes to access a transmission line. A lot of research has been conducted to develop MAC protocols for EH-WSNs. The authors of [10; 11; 12] presented the Slotted ALOHA (SALO-HA)-based protocol and claimed that this is the first work to apply the SALOHA in EH-WSNs for WPCN. SALOHA has time slots; each node waits for a time slot and sends the data during the time slot. Otherwise, it waits for the beginning of the next time slot. We can describe EH-SALOHA as follows: each node harvests the energy from the RF energy harvester, and when a slot is accessed, it transmits the data. In [10; 11], the authors proposed the harvest-until-access technique, where nodes harvest energy continuously until a slot is accessed and then send the packet. In [12], the same authors proposed another scheme for the harvest-or-access technique.