esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### ANN Prediction Modeling
#### 2.3.3. Energy and Daylight Simulation
A Daysim occupancy file was generated in which the daily occupied hours were set to from 7 to 18. Then daylight simulation was performed and sDA,UDI values were calculated by the simulation results. The workflow described above was completed by the Energy and Daylight Simulation module, as shown in Figure 8. The EUI, sDA and UDI values of the samples were obtained in this module, and output to the ANN Modeling and Training module. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Hour of the day** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline Occupancy (\%) & 0 & 0 & 0 & 0 & 0 & 0 & 10 & 50 & 95 & 95 & 95 & 80 \\ \hline Ventilation (0-off/1-on) & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline Lighting (\%) & 0 & 0 & 0 & 0 & 0 & 0 & 10 & 50 & 95 & 95 & 95 & 80 \\ \hline Equipment (\%) & 0 & 0 & 0 & 0 & 0 & 0 & 10 & 50 & 95 & 95 & 95 & 50 \\ \hline Heating set point (\({}^{\circ}\)C) & 5 & 5 & 5 & 5 & 5 & 12 & 18 & 20 & 20 & 20 & 20 & 20 \\ \hline Cooling set point (\({}^{\circ}\)C) & 37 & 37 & 37 & 37 & 37 & 28 & 26 & 26 & 26 & 26 & 26 \\ \hline
**Hour of the day** & **13** & **14** & **15** & **16** & **17** & **18** & **19** & **20** & **21** & **22** & **23** & **24** \\ \hline Occupancy (\%) & 80 & 95 & 95 & 95 & 95 & 30 & 30 & 0 & 0 & 0 & 0 & 0 \\ \hline Ventilation (0-off/1-on) & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 \\ \hline Lighting (\%) & 80 & 95 & 95 & 95 & 95 & 30 & 30 & 0 & 0 & 0 & 0 & 0 \\ \hline Equipment (\%) & 50 & 95 & 95 & 95 & 95 & 30 & 30 & 0 & 0 & 0 & 0 & 0 \\ \hline Heating set point (\({}^{\circ}\)C) & 20 & 20 & 20 & 20 & 20 & 20 & 18 & 12 & 5 & 5 & 5 & 5 \\ \hline Cooling set point (\({}^{\circ}\)C) & 26 & 26 & 26 & 26 & 26 & 26 & 37 & 37 & 37 & 37 & 37 & 37 \\ \hline \hline \end{tabular} \({}^{1}\) Data from [34].  
\end{table} 
Table 5: Zone Schedules on working days \({}^{1}\).
# Fault Diagnosis of a Solenoid Valve Based on Multi-Feature Fusion
## 1 Introduction
This article uses SVM for pattern recognition and compares it with a neural network. SVM use structural risk minimization, and it can classify small sample problems [22]. SVMs have more rigorous mathematical proofs and faster convergence speeds [23]. Diagnosing the four typical solenoid valve states, namely normal, spring broken, spool slightly stuck and spool completely stuck, utilizing traditional neural network pattern recognition poses low accuracy, whereas traditional support vector machines present low credibility. Therefore, in this work, the research group considered a solenoid valve fault-diagnosis method on the basis of non-intrusive current detection and proposes a method that combines wavelet packet decomposition and principal component analysis, which fuses the time-frequency analysis characteristics of the current signal with the time-domain parameter characteristics. For electromagnetic directional valve fault pattern recognition, the research group exploited the C-parameter support vector machine (C-SVC) of the radial basis function (RBF). The effectiveness of the suggested method is verified in the corresponding experimental section. The remainder of this paper is as follows. Section 2 analyzes the solenoid valve current characteristics, and Section 3 presents the proposed feature extraction method. In Section 4, the research group introduces the solenoid valve failure-mode recognition scheme, and in Section 5, the research group evaluates the technique on an electromagnetic reversing valve. Finally, Section 6 concludes this work.
## 4 Solution Approach
### Action Set for Vehicles and UAV  
The action set comprises five vectors \([a_{1},a_{2},a_{3},a_{4},a_{5}]\). Here, \(a_{1}\) represents the characteristics of a packet, its current state, type of packet, and processing time. The vector \(a_{2}\) indicates the priority of a packet. The vector \(a_{3}\) implies a UAV's processor utilization, queue occupancy status, queuing delay, and processing delay for a packet. The vector \(a_{4}\) indicates the status of the UAV queue, queue length, waiting time, and the number of processed packets. The vector \(a_{5}\) represents the local model hyper-parameters of a vehicle's clusters and rejected redundant local models in each TTI. The rewards accumulated by the vehicles are stored in memory used to train a global model. At slot \(t\), each vehicle observes its local state and decides its next action. We quantify the estimator performance by its squared-\(L^{2}(p)\) error, which implies the precision in estimating the UAV's trajectory as  
\[\boldsymbol{\psi}(\Upsilon)\triangleq\mathbb{E}\,\|\,\Upsilon_{r}(x)-T_{r}(x )\,\|_{L^{2}(p)}^{2} \tag{16}\]  
where \(L^{2}(p)\) is the \(L^{2}\) norm. The magnitude of Equation (16) varies with all possible local model transmissions from individual vehicles and clusters to the UAV and identifies a configuration \(p_{\theta^{*}}^{*}\) as  
\[p_{\theta^{*}}^{*}\in\arg\max_{p_{\theta}\in P_{0}}\ \frac{1}{N}\sum_{i=1}^{V} \mathcal{L}(p_{\theta};\mathcal{D}_{train}^{(i)},\mathcal{D}_{valid}^{(i)}), \tag{17}\]  
where \(\mathcal{L}\) is the loss function, and \(\mathcal{D}_{train}^{(i)}\) and \(\mathcal{D}_{valid}^{(i)}\) are the training dataset and validation dataset. The posterior distribution for an action selected by a vehicle pertaining to a UAV's state is \(\forall a\in\mathcal{A}_{v}\), \(\forall v\in\{a_{1},a_{2},a_{3},a_{4},a_{5}\}\}\), and \(\mathcal{H}_{t}\) is the history of the past heights and trajectories of the UAV up to iteration \(t\). The batch selection scheme encourages diverse exploration and vehicle selection via a Poisson point process. The method has an exponential complexity for \(n\) samples.  
Figure 4: The local model storage and communication from vehicles to UAV. Using PFL, the redundant local model hyper-parameters are discarded to reduce communication overhead and latency.
## 2 Materials and Methods
### Carbon Flux and Meteorological Data Process  
Meteorological data were sampled every minute at the Daman and A'rou superstations and then converted to 30 min average values and daily values. The meteorological data were quality controlled by filtering values beyond the physical range or instrument range. The raw 10-Hz EC data were processed to half-hourly flux data via spike detection, lag correction of H\({}_{2}\)O/CO\({}_{2}\) relative to the vertical wind component, sonic virtual temperature conversion, coordinate rotation, the Webb-Pearman-Leuning (WPL) correction and frequency response correction [40]. Half-hourly carbon flux data were further partitioned into GPP and ecosystem respiration. The carbon flux value was excluded if the instrument range was excessive, rainfall occurred or instrument malfunctions occurred. At night, carbon flux data were filtered if the friction velocity (u*) was lower than a threshold value (0.15 m/s for Daman and 0.1 m/s for Arou). The gaps in the carbon flux data were filled using the light response function (Michaelis-Menten equation) and respiration function (Van't Hoff equation) [41]. The ecosystem respiration function (Van't Hoff equation) was fitted with nighttime net ecosystem exchange (NEE) and air temperature data. Then, the fitted function was applied to the daytime data to estimate ecosystem respiration. The GPP was calculated as the difference between the estimated ecosystem respiration and NEE. The partitioned GPP data were used as ground truth to evaluate the SIF and vegetation indices during the GPP estimation.
# Small-Target Detection Based on an Attention Mechanism for Apron-Monitoring Systems
###### Abstract  
Small-target detection suffers from the problems of low average precision and difficulties detecting targets from airport-surface surveillance videos. To address this challenge, this study proposes a small-target detection model based on an attention mechanism. First, a standard airport small-target dataset was established, where the absolute scale of each marked target meets the definition of a small target. Second, using the Mask Scoring R-CNN model as a baseline, an attention module was added to the feature extraction network to enhance its feature representation and improve the accuracy of its small-target detection. A multiscale feature pyramid fusion module was used to fuse more detailed shallow information according to the feature differences of diverse small targets. Finally, a more effective detection branch structure is proposed to improve detection accuracy. Experimental results verify the effectiveness of the proposed method in detecting small targets. Compared to the Mask R-CNN and Mask Scoring R-CNN models, the detection accuracy of the proposed method in two-pixel intervals with the lowest rate of small targets increased by 10%, 3.04% and 16%, 15.15%, respectively. The proposed method proved to have a higher accuracy and be more effective at small-target detection.  
small targets; Mask Scoring R-CNN; attention mechanism; feature fusion Article
## 2 Materials and Methods
### WSN Nodes  
The WSN nodes consisted of a number of slave sensor nodes (SSNs) and a master sensor node (MSN), which applied 433 MHz as the radio frequency to increase the transmission performance and form a wireless sensor network. Each SSN included a microcontroller, a 433 module with the antenna, sensors, a EEPROM (Electrically Erasable Programmable Read-Only Memory) chip, a clock chip, and a battery power supply. The MSN was an integration of a microcontroller, a 433 module with the antenna, a GPRS (General Packet Radio Service) module, and a battery power supply.  
The physical implementation of the WSN nodes is demonstrated in Figure 2a,b. STC12 LE5A60S2 (STC micro TM, Shanghai, China) was used as the microcontroller to improve processing speed, maintain low-power usage, and improve the capacity of disturbance resistance, in order to realize system functionality of the SSN and the MSN. Having a CC1110 as the core chip of the 433 module with the antenna presented many advantages over traditional wire transmission due to its low maintenance cost, low power consumption, high mobility, and high transmission performance. A GPRS module in the MSN via the RS232 bus, was used to communicate between the MSN in the vehicle and the remote server. An EEPROM chip was used to save sensor information when signals were cut off in long international transport. A clock chip was used to control the time when data collected was saved and produced a timing pulse to wake up the CPU (Central Processing Unit). The use of LCD1602 was optional, but it could be used to display information when the sensor nodes were tested. Sensors were used together with the real time remote monitoring terminal in order to monitor the temperature, humidity, CO\({}_{2}\), O\({}_{2}\), and ethylene levels periodically.  
Based on the field study and the existing literature review, sensor requirements are specified in Table 1. The temperature, relative humidity, CO\({}_{2}\), and GPS data in the peach and nectarine cold chain logistics were acquired and monitored by adopting the digital temperature and relative humidity sensor AM2322 (AOSONG, Guangzhou, China), CO\({}_{2}\) sensor ATI (analytical technology incorporated,  
Figure 2: The physical implementation of the WSN (wireless sensor network) nodes. (**a**) The flow chart of slave sensor node; (**b**) Hardware of master sensor node.  
New York, NY, USA), and U-BLOX M8030 (u-blox, Thalwil, Zurich, Switzerland). The range of temperature, relative humidity, and CO\({}_{2}\) were from \(-\)40 \({}^{\circ}\)C to \(+\)80 \({}^{\circ}\)C, 0% to 99.9%, and 0%-5%, respectively, and the accuracy was \(\pm\)0.3 \({}^{\circ}\)C, \(\pm\)2.0%, and \(\pm\)0.1%, respectively. The temperature, humidity, and CO\({}_{2}\) sensors were not used in the fruit cold chain until they were calibrated. The supply voltage of the slave sensor node was supplied by a lithium battery (aigo, Beijing, China), whose nominal voltage and capacity was 5 V and 8000 mAh, respectively, while the master sensor node was equipped with a 5 V, 2 A power adapter to provide a continuous supply.
## 3 Results and Discussion
### Temperature Effect  
Processing temperature appears to be the most significant influencing parameter since a large increase in both the Seebeck coefficient and the electrical conductivity can be observed upon increasing the processing temperature from 30 \({}^{\circ}\)C to 80 \({}^{\circ}\)C (Figures 6 and 7). However, upon exceeding 80 \({}^{\circ}\)C, there is no further improvement in most samples in terms of the Seebeck coefficient and electrical conductivity (Figures 6b and 7b). This could be attributed to the fact that the PANI matrix is close to a material transition phase, which may also influence the interfacial interactions between the matrix and the additive [36; 37; 38].  
Moreover, the thermal conductivity is not influenced by the processing temperature since only negligible changes are observed upon T increase, while the recorded values are within the expected range for the pure polymer [18], which corresponds to >90% of the materials' total volume. Consequently, an increase in processing temperature positively influences the materials' thermoelectric properties, resulting in an increased ZT value. These results are in line with a previous report dealing with the thermoelectric properties of inorganic Bi\({}_{0.4}\)Sb\({}_{1.6}\)Te\({}_{3}\) bulk samples, where both the power factor and the thermal  
Figure 8: Graphs of thermal conductivity (\(\kappa\)) versus processing temperature (T) measured at 130 \({}^{\circ}\)C (403K) at 4.5 tons (**left**) and 2 tons (**right**). The effect of the additive content (pure PANI—blue, 20 wt.% BST—green and 30 wt.% BST—red) and processing times (15, 45 and 75 min) can be seen in the graphs. In legend, X denotes the processing temperature, which is either 30, 80 or 100 \({}^{\circ}\)C.  
conductivity increased upon increasing the processing temperature [42]. As expected, the thermal conductivity of the PANI-BST composites is not influenced by the processing temperature, thus emphasizing once more the advantage of using polymers as a limiting factor for the overall thermal conductivity in polymer-based thermoelectric composites.
# Inter-UAV Routing Scheme Testbeds
## 2 Ad Hoc Routing
### Testbed-Friendly Ad Hoc Routing Protocols
#### 2.1.2 AODV  
AODV [43] is a reactive topology-based protocol as it calculates packet routes on-demand. It is based on the Destination-Sequenced Distance Vector routing (DSDV) [44]. AODV cannot optimize routes by default. As with OLSR, there exist -ETX variants/extensions of AODV. Researchers in [45] measured and compared the performance of AODV and AODV-ETX; they concluded that compared to AODV, AODV-ETX's average throughput is increased by 15.84%, packet delivery ratio is increased by 5.80%, average overhead is reduced by 4.31%, at the expense, however, of a 10.19% increased end-to-end delay and a by 3.09% increased route discovery delay. AODV is one of the most popular ad hoc routing protocols. Authors of [46] have compared AODV to Dynamic Source Routing (DSR) in an outdoors environment. They concluded that AODV performs measurably better in terms of packet loss and end-to-end delay. AODV is implemented in Linux kernel v. 3.8 as AODV-UU [47] created by the Uppsala University, hence the suffix.
# Self-Training with Entropy-Based Mixup for Low-Resource Chest X-ray Classification
## 1 Introduction
In summary, we suggest a way to improve performance by repeating the process of sample creation, selection, and expansion through the proposed method. The main contributions of this study are as follows:  
* We propose an entropy-based Mixup method for data augmentation. The entropy is calculated and sorted to select two labeled data for synthesis, and data with high entropy and data with low entropy are selected for mixed synthesis. Two target selection methods to solve the imbalance in the number of samples are compared through experiments: random selection and entropy-based selection. * We propose a self-training method for utilizing unlabeled data to overcome the low-resource problem. It improves performance by cycling through sample generation, selection, and expansion with sample synthesis. Through self-training, the overfitting problem that occurs during the training process can also be reduced. * Mixup and self-training are combined to improve the performance of imbalanced multilabel chest X-ray classification. The proposed method applies the Mixup algorithm to a small number of labeled data to alleviate data imbalance and applies self-training with a large number of unlabeled data, replacing the teacher model with the student model repeatedly to perform sampling, which can effectively utilize unlabeled data. Our paper is organized as follows: Section 2 describes related studies; Section 3 introduces a method of combining entropy-based Mixup and self-training; Section 4 analyzes the experimental results using the ChestX-ray14 dataset; Section 5 presents the conclusion and future research directions.
## 2 Background and Proposed System Model
### Correlation Model
[23] considers only separation distances between antenna pairs. These assumptions make this model simpler and suitable for the analyses in this paper since it allows performance analyses of spectrum sensing under different levels of correlation of received signals at the SUs simply by varying the correlation coefficient \(\rho\). Figure 1 summarizes the before-described systems for the adopted multiantenna-cognitive-radio-based blind SS assuming correlated signals and unequal signal and noise powers.
# Disentangled Feature Learning for Noise-Invariant Speech Enhancement
## 3 Proposed Method  
In this section, we propose a method to disentangle the speech and noise features for noise-invariant speech enhancement. We present (1) the proposed model architecture, (2) objective functions, and (3) the adversarial learning process.
###### Abstract  
The operating temperature is an essential parameter determining the performance of a photovoltaic (PV) module. Moreover, the estimation of the temperature in the absence of measurements is very complex, especially for outdoor conditions. Fortunately, several models with and without wind speed have been proposed to predict the outdoor operating temperature of a PV module. However, a problem for these models is that their accuracy decreases when the sampling interval is smaller due to the thermal inertia of the PV modules. In this paper, two models, one with wind speed and the other without wind speed, are proposed to improve the precision of estimating the operating temperature of outdoor PV modules. The innovative aspect of this study is two novel thermal models that consider the variation of solar irradiation over time and the thermal inertia of the PV module. The calculation is applied to different types of PV modules, including crystalline silicon, thin film as well as tandem technology at different locations. The models are compared to models that are described in the literature. The results obtained in different time steps show that our proposed models achieve better performance and can be applied to different PV technologies.  
photovoltaic; module temperature; PV operating temperature; module temperature models Article
## 5 Analysis of Results and Field Testing
### Results of The Experiment  
#### 5.3.1 Objective Comparison of Segmentation Performance  
To test the performance of the SegFormer-UNet segmentation network model proposed in this paper, we compared it with the FCN in [20], the UNet in [21], the DeepLab V3+ in [24], the PSPNet in [33], the HRNet in [34], and the SegFormer in [27]. The evaluation metrics used for comparison are shown in Table 2. It can be observed that the network proposed in this paper achieves an mIoU of 98.34% and an mPA of 99.1%, outperforming the other networks in terms of segmentation performance. These results demonstrate that the SegFormer-UNet segmentation network proposed in this paper is better suited for water body segmentation than other semantic segmentation networks.
# The Response of Vegetation Dynamics to Climate in Xinjiang from 1991 to 2018
## 3 Results
### Temporal and Spatial Variations of Vegetation Types and Areas  
From 1991 to 2018, the vegetation area in Xinjiang increased by 165,669.8 km\({}^{2}\), with grassland accounting for the largest share of this enhancement (88.7%), followed by cropland (9.7%). Grassland also represented the largest portion of vegetation loss at 92.9%, followed by cropland, which decreased by 4.2%. During the study period, the net increase in vegetation cover was 24,345.8 km\({}^{2}\). Except for cropland, the areas of forest and shrub land decreased, while the grassland area remained relatively stable. In southern Xinjiang, the main region of grassland expansion was in the Kunlun Mountains, while in northern Xinjiang, it primarily occurred at higher elevation around the Junggar Basin (Figures 6 and S1). The increase in natural forest mainly occurred in the Altai and Tianshan Mountains of northern Xinjiang, while other vegetation types like orchard and tea plantation expanded around the Tarim Basin in southern Xinjiang. In both southern and northern Xinjiang, the expansion of cropland mainly occurred around the basin edges. Many areas of cropland expansion overlapped with regions of grassland decline. Human demand for food and cash crops led to the conversion of more natural vegetation into cropland.  
The land use transfer matrix indicated a net increase of 33,062.8 km\({}^{2}\) in cropland, mainly from the conversion of grassland (61.2%) and bare/desert (26.0%). The net reduction in natural forest area was 7993.0 km\({}^{2}\), with grassland conversion accounting for 87.4% of this reduction, followed by bare/desert for 6.6%. Shrubland saw a net decrease of 1540.9 km\({}^{2}\), with 66.6% of this area converted to grassland, 20.5% from bare/desert, and 7.8% from natural forest. The net change in grassland area was \(-\)246.1 km\({}^{2}\), with 76.7% of new grassland coming from bare/desert and 9.6% from natural forest. It was evident that between 1991 and 2018, the increase in natural vegetation mainly resulted from the conversion of bare/desert, predominantly converted into grassland, which was distributed in mountainous areas. The expansion of natural forests was mainly concen  
Figure 5: The spatial distribution characteristics for (**a**) non-growing season, (**b**) growing season, and (**c**) annual value and changing trends (**d**) of GPP.  
trated in mountainous areas, extending into lower elevations. Cropland expansion was mainly concentrated in the plains and largely resulted from the conversion of grassland.