esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Studies
### Case Study 2--Ground-Based Surveillance  
The ground-based surveillance system comprises two electro-optical (EO) sensors that are located at two different observation sites, as illustrated in Figure 9. As illustrated in Figure 4b, two ground-based EO sensors track the RSO simultaneously and share the estimated RSO position data with a distributed satellite system (DSS) constellation through a network [24; 81; 82; 91]. For the ground-based tracking scenario, the following assumptions are adopted [92]:  
* The DSS assets are placed in a nearly circular low earth orbit (LEO) at an altitude of 500 km to carry out Earth observation activities.
* The participating spacecraft are equipped with sophisticated GPS for positioning and navigation that provide a full set of navigation data.
* The estimated RSO position from ground-based sensors is uplinked to the DSS assets to ensure their safety.
* The participating spacecraft share their position information and the RSO position estimates with other satellites using inter-satellite links (ISL).
* The ground-based EO sensors assumed in this scenario are similar to the sensors used in [93] and operate in the infra-red (IR) region between the wavelengths of 3-12 \(\upmu\)m.  
The proposed AN system comprises the following components:  
* **Navigation hardware** comprises the state of the art GPS to obtain a full set of navigation data comprising the DSS satellite positions, velocities, and attitude rates.
* **Tracking hardware** comprises ground-based EO sensors that track the RSO by simultaneous optical measurements.
* The obtained data from the hardware are used as inputs by the **On-Board System** (**OBS**) to obtain the RSO position estimates, error measurement budget, and to generate the uncertainty ellipsoids.
* The **navigation and guidance system** exploits the data generated by the OBS for trajectory planning and optimization to generate the steering commands.  
Figure 9: Ground-based surveillance scenario.  
* **Actuators** use the steering commands to perform the collision avoidance manoeuvres in order to avoid a collision with the RSO.  
Figure 10 illustrates the individual segments of the tracking system architecture.
## 2 Methodology
### Implementation of a Data-Driven Prediction Approach Based on Machine Learning
#### 2.3.2 Normalization of Variable Data  
As we know, different monitoring items use different measurement units, as shown in Table 3. In order to seek the relationship among the variables in Equation (2), it is necessary to normalize these variables first, so as to eliminate the restrictions caused by the difference in the measurement units of the data and convert them into dimensionless pure values. There are two normalization methods. The first is to normalize the monitoring results by using the design limits (i.e., control values) for specific projects, which must be implemented before constructing equivalent variables. Since the control value set for the specific project has a large subjective nature and cannot cover the actual monitoring results, the normalized results by this method cannot be guaranteed to be within the range of [-1,1]. The second method is to consider the characteristics of the data and adopt mathematical statistical methods to normalize the monitoring data, and some normalization functions have been implemented in MATLAB [53] including the **premnmx** function, **tammmx** function, **postmnmx** function, **mapminmax** function, and **prestd** function, etc. Among which, the **mapminmax** function can be used to realize the normalization of data and the restore of normalized data. Thus, the normalization function of **mapminmax** in MATLAB is directly used for data preprocessing in this article.
## 5 Conclusions  
In this paper, adaptive bidirectional droop control based on normalized AC frequency and DC voltage is proposed to address the problem that traditional bidirectional droop control in islanding mode does not consider the priority of DC voltage and AC frequency  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & **Adaptive** & **Power Ratio** & **Adaptive** & **DC Power** & \multirow{2}{*}{**Parallel Converter**} \\  & **Droop** & **Control**[13] & **Coordinated** & **Feedback** & **Control**[26] \\  & & & **Control**[24] & **Droop**[25] & \\ \hline Max. frequency deviation (Hz) & 0.21 & 0.25 & 0.28 & - & 0.24 \\ Max. DC voltage deviation (V) & 13 & 15 & 18 & 15 & 16 \\ Power transfer capacity (kW) & 11.5 & 9.2 & 7.8 & 7.0 & 8.5 \\ Recovery time (ms) & 80 & 120 & 150 & 200 & 180 \\ Switching events (min) & 3 & - & 5 & - & 10 \\ Priority regulation & Yes & No & No & Partial & No \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison with state-of-the-art adaptive techniques.  
regulation. Through theoretical analysis and simulation verification, the following conclusions were obtained:  
1. The adaptive droop control strategy proposed in this paper enables the ILC power to flow in both directions when the AC and DC loads are changing, realizing power autonomy as well as power mutualization.
2. In the case of AC and DC sub-microgrids with different power magnitudes, with the change in load, the adaptive droop control strategy proposed in this paper is able to control the ILC to transmit more power from the side with a smaller deviation of AC frequency or DC voltage to the side with larger deviation. Priority regulation and equalization compensation are realized to ensure the stability of the system.  
Conceptualization, H.Z.; Methodology, C.D.; Software, R.Z.; Validation, R.Z. and W.C.; Formal analysis, H.Z.; Writing--original draft, W.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 3 Prototype Testing
### Aeronet-Oc Data Used for Comparison  
The CIMEL/Seaprism system providing data for AAOT through the AERONET-OC network was installed on the west corner of AAOT on a purpose-built jetty to minimize any shading effects of the platform on the water being measured. Full details on the instrumentation and measurement protocol and data processing and quality control can be found in Reference [6].  
Level 1.5 normalized water-leaving radiance data, \(L_{wn}\), were used without \(f/Q\) correction [32] as it is not applied to the PANTHYR and M3 TRIOS data. This data were downloaded from the AERONET-OC (AOC) website for the \"Venise\" site located on AAOT [6]. Level 1.5 data are automatically cloud cleared but do not have final post-deployment calibration applied and are, hence, not fully quality assured. The matchup data used in this paper do, however, pass quality control and are likely candidates for level 2.0 data if post-deployment calibration is acceptable [[PERSON], pers. comm. 18 March 2019]. \(L_{wn}\) values were converted to water reflectance, \(\rho_{w}\), by  
\[\rho_{w}=\pi\frac{L_{wn}}{F_{0}}, \tag{5}\]  
where \(F_{0}\) is the extraterrestrial irradiance [33] resampled to gaussian band-passes with 10-nm full-width half-maximum centered on the reported exact center wavelengths of the CIMEL instrument. Each of the level 1.5 AOC measurements was matched to the closest PANTHYR measurement within 20 min, and the PANTHYR data were resampled to the same band.
# Mercury Contents in the Liver, Kidneys and Hair of Domestic Cats from the Warsaw Metropolitan Area
###### Abstract
The primary role in processes of inorganic mercury excretion from organisms is played by excretory systems, enabling its elimination in urine. Thus, the measurement of total mercury content in urine is used to investigate exposure to inorganic compounds [29]. The fate of methylmercury is different, as it is transferred from the gastrointestinal tract almost entirely to the blood circulation, where 90% of it binds with erythrocytes. The main routes of methylmercury elimination are bile and hair coats; thus, the total mercury content in hair is treated as an indicator of exposure to methylmercury [30]. It should be added that the organs which display the symptoms of metal or metalloid toxicity are not necessarily those in which bioaccumulation of the element in question takes place, as this depends on the element's toxicokinetics. Although the data of US EPA [31] indicate that the organs susceptible to the toxic effect of a given element may differ between species, for mercury in warm-blooded terrestrial vertebrates it is clearly indicated that the kidneys are the organs particularly susceptible to its toxic effect (in the case of inorganic compounds), along with central nervous system tissue in terms of organic compounds of mercury. The organs in which bioaccumulation of this element takes place are the aforementioned kidneys and liver [32; 33; 34; 35; 36]. In the context of analyses of metal and metalloid contents, scientists began to search the material enabling intravital sampling in a less invasive way, and in the last three decades attention has been paid to skin derivatives, among which the hair coat seems to be the most suitable investigational material [37; 38]. This material appears to correctly reflect the level of mercury in the organism, enabling observation of long-term exposure to the metal [4; 14; 29; 40; 41]. The advantage of hair over other biological samples is due to the ease of its collection in a non-invasive manner, and it does not require refrigeration conditions during storage. Mercury content in hair is higher than in blood or urine samples. What is more, retrospective analyses can be performed as a result of testing the hair of individuals maintained in museums [42; 43; 44]. The objective of this study is the analysis of total mercury contents in the liver, kidneys and hair of domestic cats originating from two different habitats within the Warsaw metropolitan area, as well as determination of the correlation between the contents of mercury in the liver and kidneys and the hair of the examined individuals. Another significant issue is the assessment of the effects of habitat, sex and age on the examined animals with respect to tissue contents of mercury, which should provide an answer to the question of which group of animals is more exposed.
## 2 Materials and Methods
### CT Acquisition  
Image acquisition was performed with a 128-slice computed tomography equipment (Somatom Definition AS, Siemens Healthcare GmbH, Erlangen, Germany).  
The scanning protocol used 100 kVp for minimizing radiation dose, with the tube current modulation set at a reference dose of 160 mAs, a tube collimation of 0.6 \(\times\) 128 mm, a slice thickness of 3 mm and increments of 3 mm. Image acquisition was performed with the patient in a supine position, in a craniocaudal direction, from 2 cm above the branching of the renal arteries to the pedal arteries. Contrast media was administered via an intravenous antecubital cannula, of 20-22 gauge, by injecting 120 mL of nonionic iodine-based contrast medium (Ultravist, Bayer, Leverkusen, Germany) and a 30 mL saline flush, at a flow rate of 4 mL/s, with a monophasic injection rate. The attenuation threshold for automatically triggering the optimal image acquisition was set at 160/180 Hounsfield units above the baseline value within the vascular bed. For optimization of the time delay for image acquisition, the region of interest was placed at the level of the abdominal aorta, above the iliac bifurcation. Breathing instructions were given to the patient for approximately 4 s, for proper imaging of the abdominal aorta. A typical scan duration was approximately 40 s, which ensured an adequate opacification of the distal arteries.  
All images were analyzed by an experienced radiologist in cardiovascular CT imaging. The final image data set processed included: (1) thin slice maximum-intensity projections (MIPs) through the abdominal aorta, renal arteries, iliofemoral artery axes and crural vessels, (2) whole volume MIPs after bone removal, (3) curved planar reformations (CPRs) and (4) volume rendered images.
# Deep Learning Model with Sequential Features for Malware Classification
## 1 Introduction
[10] extracted byte and opcode sequences, which were fed into a classifier composed of two convolutional neural networks (CNNs). Although the structure was relatively simple, the accuracy failed to exceed that of complex classifiers. [PERSON] et al. [11] proposed the Malnet detection model, which used CNN to learn the features of grayscale maps and LSTM to learn the opcode and then merged the classifications using a simple weighting approach. [PERSON] et al. [12] used a CNN-LSTM approach for feature extraction and two types of machine learning for classification using support vector machines and logistic regression. Researchers [PERSON] and [PERSON] et al. [13,14] extracted 15 and 6 features from malware, respectively, with more comprehensive information extraction, but feature extraction and selection were time-consuming and contained features that had little effect on classification. Because a single feature has limitations, and in order to improve the ability of feature mining, the accuracy of malware classification, and reduce the interference of malware variants, packaging and obfuscation technologies, the present study proposed a multi-classification method of malware families incorporating TCN-BiGRU. The main contributions are as follows. 1. A malware detection and classification method (TCN-BiGRU) that fuses the temporal convolutional network and the bidirectional gated recurrent unit was proposed to improve the overall performance of the malware detection and classification model. 2. Opcode and bytecode sequences were fused to obtain their occurrence frequencies, reduce interference from shelling and obfuscation techniques, and improve the accuracy rate. 3. The feature extraction capability of temporal convolutional networks (TCN) for temporal data was introduced to fully learn the dependency relationship among data. 4. The output of the maximum pooling layer and the output of the average pooling layer were fused for relatively comprehensive extraction of data features. 5. The nonlinear fitting ability of a bidirectional gated recurrent unit (BiGRU) was used, and further feature extraction was conducted to learn the dependency of the before and after information in the opcode sequence, extracting the opcode features based on the time series to improve the model classification detection effect. This paper proceeds as follows. Section 1 introduces the relevant background and related work. Section 3 presents the model. Section 4 presents the experimental results and analysis. Finally, Section 5 summarizes the experimental conclusions and discusses future research prospects.
# AODs-CLYOLO: An Object Detection Method Integrating Fog Removal and Detection in Haze Environments
## 4. Results
### Dataset Creation  
The scarcity of existing foggy weather datasets makes it challenging to train stable and robust object detection models under foggy conditions. In this study, we selected five common object categories in foggy environments for training: person, bicycle, car, bus, and motorcycle. We constructed two datasets simulating foggy weather conditions based on the classic PASCAL VOC dataset combined with an atmospheric scattering model. These datasets are the training set VOC_train and the testing set VOC_test. Detailed information about the datasets and the objects within them is shown in Table 1.  
The VOC_train dataset consists of images from the VOC2007_trainval and VOC2012_trainval sets, which have been randomly augmented with fog elements for the aforementioned five categories (fog density is controlled by adjusting the medium's transmittance). Similarly, the VOC_test dataset is composed of images from the VOC2007_test set, randomly augmented with fog for the same five categories. Examples of images from the datasets are shown in Figure 7.
## 2 Materials and Methods
### Mechanical Tests and Density  
#### 2.8.1 Tensile Test  
The tensile test was performed on an Instron ElectroPuls E10000 universal testing machine (Instron, Norwood, MA, USA). The tests were carried out on filament samples for each composition 5 times. The length of the test samples was 200 mm; the length between grips was 150 mm. Before testing, the diameter of each filament was measured at five points using a 293-330-30 Mitutoyo micrometer (Mitutoyo Corporation, Takatsu-ku, Kawasaki, Japan). The extension rate was 0.5 mm/min; the test temperature was 21 \(\pm\) 1 \({}^{\circ}\)C; and the relative humidity was 52 \(\pm\) 1%. The test rate was chosen based on the fact that the specimen would rupture as a result of its extension at least 1 min after the start. Filaments that broke in the grips were rejected. The ultimate tensile strength [MPa], elongation at break [%] and Young's moduli [MPa] were determined. The Young modulus of the specimens was determined from the slope of the least squares regression of the stress-strain curve between 0.15% and 0.5% strain.
# New Developments and Progress in Absorption Chillers for Solar Cooling Applications
## 3 Newly Developed Commercial Absorption Chillers
### Efficient and Compact Absorption Heat Pump: Cooling and Heating
The chillers were driven by hot water from compound parabolic solar collectors (gross area of 480 m\({}^{2}\), 388 m\({}^{2}\), 449 m\({}^{2}\), and 140 m\({}^{2}\) for each pilot project). The system showed stable and successful operation during the field test with electrical and thermal COPs of up to 30 kW\({}_{\text{cold}}\)/kW\({}_{\text{hel}}\) and 0.8 kW\({}_{\text{cold}}\)/kW\({}_{\text{dhs}}\), respectively, with chiller availability of 90-100% [29]. The electrical efficiency of the systems improved due to the volume flow control approach applied in the control strategy. Figure 4: The new absorption heat pump machine, new-AHP, operating as a chiller for solar space cooling in extreme ambient conditions in Jordan [24]. (**a**) Driving heat input and cooling capacity; (**b**) Temperature of the external heat carrier streams.
# System-Level Statistical Eye Diagram for Signal Integrity
## 1 Introduction  
Recent consumer electronics have been required to provide more functionalities to satisfy the demands of the market. The above technical trend has caused data to be transmitted over more physical channels at higher data rates. In other words, the number of channels and the corresponding data rates have continuously increased. Inside silicon chips, the number of transistors has increased to support the required functions [1]. In order to fabricate more transistors on the limited size of a wafer, the silicon fabrication process has been developed for a narrower sized transistor [2]. In electromagnetic compatibility (EMC), a signal integrity (SI) analysis provides information on how the signal propagates over a high-speed channel [3]. Hence, the above technical trend makes an SI analysis more important in terms of high-performance system design [4]. Furthermore, a system becomes more complicated with these advancements, thus an SI analysis of a system also becomes hard [5; 6; 7].  
An SI analysis includes time-domain and frequency-domain approaches to investigate how a signal is degraded during data transmission. The frequency-domain approach uses scattering (S)-parameters such as reflection loss and insertion loss [8]. And the time-domain approach uses an eye diagram [9], which overlaps the received ONEs and ZEROs. The insertion loss in the S-parameters is defined as a ratio of the transmitted waves compared to the incident waves. Thus, the amount of the transmitted energy can be shown with the magnitude and the phase of the insertion loss, respectively. The magnitude and phase are given at each frequency of interest. Thus, the frequency-dependent behavior is shown within the frequencies.  
In contrast, an eye diagram shows how reliable the received data by a channel are in a graphical manner as shown in Figure 1. An eye diagram has important parameters, i.e., eyeheight (EH) and eye width (EW) inside its contour. The EH shows the voltage margin at the sampling time and the EW shows the timing margin at a given threshold voltage. The outcomes of the frequency- and time-domain approaches provide meaningful information about a channel. However, an eye diagram becomes more important for the fixed data rate because an eye diagram is the outcome of a combination of the frequency-dependent behavior of a channel and the input signal. Furthermore, an eye diagram is more intuitive compared to the insertion loss due to its representation. If the eye diagrams have non-zero EH and EW values, then an eye-opening exists in the middle. The eye-opening means that no waveform passes through the center of the eye diagrams. In other words, the received ZEROs and ONEs at the receiver are clearly distinctive.
## 3 Results
### The Influence of Subsequence Length on MDI and MERLIN  
The goal of this experiment was to examine the influence of the subsequence length on the results for MDI and MERLIN and to evaluate their ability to utilize additional information about the problem domain given with the range of subsequence lengths. To make a fair comparison, we fixed the subsequence length range for MDI and MERLIN to \(L_{min}=75\) and \(L_{max}=125\) time steps in the results presented in Sections 3.1 and 3.2, regardless of the specific characteristics of the individual time series, such as cycle length or expected length of the anomaly.  
We therefore compare the baseline results from Section 3.1 with two strategies for setting the subsequence range. For the \"dynamic\" strategy, we provided additional information by setting the range of subsequence lengths based on the length \(L_{Sa,b}=b-a\) of the true anomaly \(\tilde{S}a,b\) to \(L_{\tilde{S}a,b}\pm 25\%\). For the \"fixed\" strategy, we chose a fixed length of 100 timesteps, thereby reducing the given information compared to the baseline.  
The results for MDI, shown in Figure 8a, demonstrate that fixing the subsequence length to 100 and reducing the given information leads to a decrease in the AUC ROC and F1 score. In contrast, choosing the range for the subsequence length dynamically leads to an increase in the AUC ROC and F1 score. The results for the UCR score do not reflect this trend; the highest UCR score is still achieved with the baseline configuration, but the difference between the fixed and dynamically chosen subsequence length is relatively small.  
The results for MERLIN, displayed in Figure 8b, show similar behavior, but the differences between the strategies are more pronounced. For MERLIN, the positive effect of additional information is present across all three metrics.  
Figure 8: Results for different subsequence lengths for MDI **(a)** and MERLIN **(b).****(a)** The baseline results “MDI” are those from the main experiments using a subsequence length range of \(L_{min}=75\) and \(L_{max}=125\). The results labeled “MDI_fixed_100” were obtained using a fixed subsequence length of 100 time steps, while the results labeled “MDI_dynamic” were obtained by individually setting \(L_{min}\) and \(L_{max}\) to 75% and 125% of the true anomaly length, respectively, for each time series. **(b)** The baseline results “MERLIN” are those from the main experiments using a subsequence length range of \(L_{min}=75\) and \(L_{max}=125\). The results labeled “MERLIN_fixed_100” were obtained using a fixed subsequence length of 100 time steps, while the results labeled “MERLIN_dynamic” were obtained by individually setting \(L_{min}\) and \(L_{max}\) to 75% and 125% of the true anomaly length, respectively, for each time series.