esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of BDS-3: Measurement Quality Analysis, Precise Orbit and Clock Determination
## 3 Results
### Measurement Quality Analysis  
#### 3.1.1 Carrier-to-Noise Density Ratio  
The C/N0 ratio given by GNSS receiver is a result of signal gain and loss along the complete transmitting and receiving chain [5]. The whole chain consists of the electronic circuit and antenna of the satellite, the signal path, and the antenna and the electronic circuit of the receiver. The observed C/N0 values of BDS-2 and BDS-3 signals at sites BJF1 and KUNU equipped with the GMR-4011 and POLARX5 receivers are depicted as a function of elevation in Figure 3. Eighteen days of tracking data between DOY 204 and DOY 221 (2016) are used to produce the results. We first group all C/N0 values according to their corresponding elevations separately for B1 and B3 signal of all BDS-2 IGSO, BDS-2 MEO, BDS-3 IGSO, BDS-3 MEO satellites. Then, all C/N0 values within an elevation range of \(1^{\circ}\) are classified into one group. Finally, a mean value of C/N0 is computed and shown for each elevation group.  
From the figure, BDS-2 and BDS-3 signals from these two stations exhibit generally similar elevation-dependent C/N0 characteristics, from about 35 dB-Hz (at the low elevation) to about 50 dB-Hz (at the high elevation). MEO satellites are generally tracked with a 2-4 dB-Hz higher C/N0 values than the IGSO satellites, which are related to the smaller orbital radius. Furthermore, it is obvious that the B1 signal of BDS-3 IGSO and MEO satellites has approximately 1-2.5 dB-Hz higher C/N0 values than the corresponding BDS-2 IGSO and MEO satellites, respectively. For B3 signal, the C/N0 values of BDS-3 IGSO are higher by about 1-4 dB-Hz than BDS-2 IGSO, and BDS-3 MEO are higher by about 1-2 dB-Hz than BDS-2 MEO. In general, the signal strength of BDS-3 signals exceeds the values of the BDS-2 for the same satellite type on the each frequency.  
Figure 2: Mean RMS values of the station-specific post-fit residuals of ionosphere-free linear combination of B1 and B3 code and phase observations for BDS-2 and BDS-3 satellites. I(2) and M(2) represent BDS-2 IGSO (C06, C07, C08, C09, C10) and MEO (C11, C12, C14) satellites, and I(3) and M(3) represent BDS-3 IGSO (C31, C32) and MEO (C33, C34) satellites, respectively.
## 2 Materials and Methods
### Bacterial Viability Assay  
After rinsing the biofilm-covered HAP disk specimens with PBS (1.5 mL) thrice to remove free-floating bacteria after completing the antibacterial treatment, each specimen was moved into a conical tube containing PBS (1 mL). To prepare the biofilm suspensions, the samples underwent a dual mechanical disruption process, whereby the samples were subjected to treatment for 1 min each using a vortex mixer (VM-96A; Lab Companion, Seoul, Republic of Korea) and an ultrasonic vibrator (SHB-1025; Saehan Sonic, Seoul, Republic of Korea). A LIVE/DEAD BacLight Bacterial Viability Kit (Invitrogen, Waltham, MA, USA) was then employed to evaluate bacterial viability according to the manufacturer's guidelines. SYTO-9 and propidium iodide (PI) were combined in equal proportions and used to distinguish living bacteria from those that were non-viable. SYTO-9, which is capable of permeating cellular membranes, produces green fluorescence in the presence of live bacteria. Conversely, PI, which is unable to penetrate intact cellular membranes, stains only those cells with damaged membranes, resulting in red fluorescence. When both dyes coexist within a cell, PI's stronger nucleic acid affinity dominates, leading to the cell exhibiting only red fluorescence. Consequently, when using this dual-staining method, bacteria that are alive are indicated by green fluorescence, whereas non-viable bacteria are characterized by red fluorescence [27]. Dye mixture (3 \(\upmu\)L) was added to the biofilm suspension (1 mL) and allowed to react at 37 \({}^{\circ}\)C for 15 min in a dark room. Subsequently, 5 \(\upmu\)L of the stained suspension was imaged at 100\(\times\) magnification using ZEN 2009 software and confocal laser scanning microscopy (CLSM; Zeiss LSM 700; Carl Zeiss, Inc., Oberkochen, Germany), performed at an excitation wavelength of 488 nm and an emission wavelength of 555 nm. Image Pro software was used to analyze the green and red intensities across the full image area. The ratio of the green-stained area to the total stained area (\(\text{Ratio}_{\text{G/G+R}}\)) was calculated and presented as bacterial viability, with higher values indicating higher bacterial survival rates [28].  
Figure 5: (**A**) Body of the quantitative light-induced fluorescence-digital (QLF-D) camera. (**B**) Light sources of the QLF-D camera. (**C**) Region of interest (ROI) on a fluorescence image of the oral microcosm biofilm to analyze the \(\text{Ratio}_{\text{R/G}}\)[22].
###### Abstract  
Divalent cations were known to alleviate salinity stress on anammox bacteria. Understanding the mechanism of reducing the salinity stress on anammox granules is essential for the application of the anammox process for saline wastewater treatment. In this study, the effect of Ca\({}^{2+}\) and Mg\({}^{2+}\) augmentation on the recovery of the activity of freshwater anammox granules affected by salinity stress was evaluated. At the condition of a salinity stress of 5 g NaCl/L, the specific anammox activity (SAA) of the granule decreased to 50% of that of the SAA without NaCl treatment. Augmentation of Ca\({}^{2+}\) at the optimum concentration of 200 mg/L increased the SAA up to 78% of the original activity, while the augmentation of Mg\({}^{2+}\) at the optimum concentration of 70 mg/L increased the SAA up to 71%. EPS production in the granules was increased by the augmentation of divalent cations compared with the granules affected by salinity stress. In the soluble EPS, the ratio of protein to polysaccharides was higher in the granules augmented by Ca\({}^{2+}\) than with Mg\({}^{2+}\), and the functional groups of the EPS differed from each other. The amount of Na\({}^{+}\) sequestered in the soluble EPS was increased by the augmentation of divalent cations, which seems to contribute to the alleviation of salinity stress. Ca. _Kuenenia_-like anammox bacteria, which were known to be salinity stress-tolerant, were predominant in the granules and there was no significant difference in the microbial community of the granules by the salinity stress treatment. Our results suggest that the alleviation effect of the divalent cations on the salinity stress on the anammox granules might be associated with the increased production of different EPS rather than in changes to the anammox bacteria.  
Keywords:anammox; cation; EPS; granule; SAA; salinity stress +
Footnote †: journal: Applied Sciences
## 8 Future Directions
### Explainable AI (XAI)  
As AI is increasingly used for critical tasks within communication networks, the need for Explainable AI (XAI) becomes crucial. XAI techniques aim to make AI models interpretable and understandable, allowing network operators and stakeholders to trust and validate AI-driven decisions. This transparency is particularly essential for applications like network security, where understanding the model's rationale is critical for effective threat mitigation [139].  
Developing XAI methods specifically tailored for communication networks poses unique challenges, as network data are often complex and high dimensional. Common XAI approaches include methods like SHAP (Shapley Additive Explanation), LIME (Local Interpretable Model-agnostic Explanation), and Feature Attribution Maps [139].  
Table 16 provides a comparison of various XAI methods, highlighting their effectiveness and trade-offs.  
Future research in XAI for networks should focus on developing efficient, real-time interpretability methods that can integrate with Edge AI and provide explanations that network administrators can act upon promptly.
# Dynamic Measuring Method of Laser Beam Incident Angle for Laser Doppler Vibrometer
## 3 Static Measuring Method of Incident Angle  
### Principle of Static Measurement  
The basic principle of the traditional measuring method for a laser beam angle is shown in Figure 3. A right-handed coordinate system \(o\)-\(xyz\) is established in space. The  
Figure 1: Measurement principle of pavement deflection velocity. (**a**) Synthesis principle of velocity \(V_{\rm m}\), (**b**) Relative position of the laser beam and the wheel, (**c**) Physical photo.  
Figure 2: Key processes of static and dynamic measurement methods and their connections.  
laser beam is irradiated on a piece of coordinate paper in the _oxy_ plane with an incident angle \(\beta\) of about 2\({}^{\circ}\). Since angle \(\beta\
eq 0\), the laser spot on the coordinate paper changes with the distance between the coordinate paper and the LDV, such as moving from point A to point A\({}^{\prime}\). Assuming that the moving distance of the LDV along the \(z\)-axis is \(l\), and the moving distance of the laser spot in the _oxy_ plane is \(d\), then the angle \(\beta\) can be accurately calculated according to the geometric relationship of the triangle, see Equation (3).  
\[\beta=\arctan(d/l)\cdot 180/\pi \tag{3}\]
## 2 Materials and Methods  
### Bacterial Strain, Media, and Growth Conditions  
_L. paracasei_ [PERSON] was cultured in MRS (de Man-Rogosa-Sharpe) medium containing 10.0 g/L of peptome, 8.0 g/L of beef extract powder, 4.0 g/L of yeast extract powder, 20.0 g/L of glucose, 0.2 g/L of MgSO4.7H2O, 5.0 g/L of sodium acetate, 2.0 g/L of sodium citrate, 2.0 g/L of K2 HPO4, 0.15 g/L of MgSO4, and 1.0 mL of Tween-80. The agar concentration in culture plates was 15 g/L.  
Solid-state fermentation (SSF) medium contained 15.0 g soybean meal, 5.0 g wheat bran, and 12.0 mL deionized water. Sixty gram soybean meal, 20.0 g wheat bran, and 800.0 mL deionized water were mixed. The mixture was heated, and boiled for 10 min,filtered with two layers of gauze to obtain 300 mL solution, from which 100 mL was taken as liquid-state fermentation (LSF) medium. _L. paracasei_ [PERSON] from a \(-80\)\({}^{\circ}\)C glycerol stock was streaked on MRS agar plate and cultured for 24 h at 37 \({}^{\circ}\)C. A single colony of _L. paracasei_ Zhang was cultured in 5.0 mL MRS medium overnight at 37 \({}^{\circ}\)C for further experiments.  
Preparation of L. paracasei Zhang Bacterial Suspensions after Solid-State and Liquid-State Fermentatio, Cell Crushing Supernatant, and Cell Debris  
The overnight cultured _L. paracasei_ Zhang was inoculated into 32.0 g fresh SSF medium and 100.0 mL LSF medium with 1.0% inoculum size and cultured for 14 h at 37 \({}^{\circ}\)C.  
For SSF bacterial suspension preparation, firstly, bacteria were collected from the SSF medium. Then, 80.0 mL sterilized saline water (0.85%) was added into the SSF system and shaken at 200\(\times\)\(g\) for 5 min at 37 \({}^{\circ}\)C. Afterwards, the mixture was filtered by 2-layer gauze, and the filtrate was centrifuged at 5000\(\times\)\(g\) for 5 min. The obtained precipitate was washed twice and finally resuspended with sterilized saline water (0.85%) to obtain the SSF bacterial suspension. For LSF bacterial suspension preparation, firstly, bacteria were collected from LSF medium by aspirating the upper layer of culture carefully, then the obtained culture was filtered by 2-layer gauze. The filtrate was centrifuged at 5000\(\times\)\(g\) for 5 min. The resultant precipitate was washed twice and finally resuspended with sterilized 0.85% saline water to obtain the LSF bacterial suspension.  
The bacterial suspension was centrifuged at 5000\(\times\)\(g\) for 5 min, and the resultant precipitate was washed twice with ultra-pure water and resuspended with ultra-pure water. The suspension was put into 5 mL centrifuge tubes with each tube filled with 3 mL. The suspension in centrifuge tubes was subjected to an ice bath, crushed with an ultrasonic crushing instrument for 20 min (SONICS & MATERIALS, INC.), and centrifuged at 12,000 r/min to obtain crushed cells. The supernatant or the cell fragments were extracted for subsequent use.
# Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models
## 5 Experimental Results and Discussion
### Additional Experiments  
We have performed some additional experiments using the publicly available pneumonia dataset ([[https://www.kaggle.com/anaselmasry/covid19](https://www.kaggle.com/anaselmasry/covid19) normal]([https://www.kaggle.com/anaselmasry/covid19](https://www.kaggle.com/anaselmasry/covid19) normal) pneumonia-ct-images, accessed on 20 July 2021). As the CT scans from the pneumonia dataset are incorporated in the database, eventually the domain of the study shifts from a two-class classification problem to a three-class classification problem. This helps to evaluate the robustness and reliability of the proposed stacking ensemble methodology. As mentioned previously, the dataset considered in this study consists of a total of 2492 CT-scan images. Additionally, 1000 samples from pneumonia CT scans are merged with the existing database. This creates three categories of images, namely patients infected by COVID-19, healthy persons and patients infected by bacterial or viral pneumonia. The merged dataset is divided into 80% training data and 20% validation data. The three transfer learning models are trained for 100 epochs each after modifying the final classification layer for the three-class classification problem. These transfer learning models are used as base learners to create the stacking ensemble framework. The obtained results are shown in Table 5. From the results, it can be seen that the ensemble framework outperforms the individual  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approach** & **Accuracy (\%)** & **Precision** & **Recall** & **F\({}_{1}\)-Score** & **AUC** \\ \hline eXplainable DL approach & 97.38 & 0.9916 & 0.9553 & 0.9731 & 0.9736 \\ \hline DenseNet201 based deep TL & 96.25 & 0.9629 & 0.9629 & 0.9629 & - \\ \hline Proposed Method & 98.79 & 0.9879 & 0.9879 & 0.9879 & 0.9880 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the proposed model with some past methods for the prediction of COVID-19 on the SARS-CoV-2 CT scan dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **VCG16** & **ResNet50** & **Xception** & **VCG16+ResNet50** & **VCG16+Xception** & **ResNet50+ Xception** \\ \hline Stacked model & 0.00201 & 0.01810 & 0.01207 & 0.00402 & 0.0039 & 0.01207 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The \(p\)-values of the models obtained after Student’s \(t\)-test.  
models in all metrics considered and achieves an accuracy of 98.85%. This validates the performance of the ensemble framework. In other words, we can say that if there is an increase in the number of classifying categories, the model performs as expected not only in terms of accuracy but also in the other metrics (precision, recall, \(F_{1}\) score).
# Disaster Recognition and Classification Based on Improved ResNet-50 Neural Network
## 2 Materials and Methods
### Dataset Preparation
The process includes image collection from multiple sources, preprocessing steps (such as resizing and normalization), classification using the improved ResNet-50 model, and final disaster type output for emergency decision-making support.
# New Hybrid Algorithms for Prediction of Daily Load of Power Network
## 4 Experiments and Results
### Prediction Results  
The prediction results of the algorithms are shown in Table 8, where LS is the classical least squared; NN is the fixed structure neural network. According to the statistics of the prediction results, as shown in Figure 11, the 124 th day is the largest prediction error of SGWO and SGWOD. From the quantified data on the 124 th and 125 th days, it is seen that the power load has changed drastically in the two days, and they are large influenced by other external influence factors. It also shows from another side that power load is closely related to temperature and date types. The efficiency of network can be further improved by adapting the optimization methods [58; 59; 60; 61; 62].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Temperature (\({}^{\circ}\)C)**} & **Quantitative Value** & \multirow{2}{*}{**Temperature (\({}^{\circ}\)C)**} & **Quantitative Value** \\  & **of Temperature** & & **of Temperature** \\ \hline \(<\)\(-15\) & \(-1\) & 15\(\sim\)20 & \(-0.1\) \\ \(-15\)\(\sim\)\(-5\) & \(-0.8\) & 20\(\sim\)25 & 0 \\ \(-5\)\(\sim\)\(0\) & \(-0.6\) & 25\(\sim\)30 & 0.3 \\ \(0\)\(\sim\)5 & \(-0.5\) & 30\(\sim\)35 & 0.6 \\ \(5\)\(\sim\)10 & \(-0.4\) & 35\(\sim\)40 & 0.9 \\ \(10\)\(\sim\)15 & \(-0.2\) & \(>\)40 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Quantitative value of temperature.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & **Prediction Accuracy (\%)** & **Squared Error** \\ \hline GWO & 87.01 & 0.1256 \\ GWO-DE & 87.64 & 0.1276 \\ SFLA & 86.73 & 0.1182 \\ SGWO & 89.08 & 0.1317 \\ SGWOD & 89.3 & 0.1236 \\ LS & 69.01 & 0.4613 \\ NN & 86.37 & 0.1167 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The prediction results.
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 4 Proposed System
### Software Architecture
#### 4.3.2 Data Messages  
DMs are used to transmit images of a detected boat from the UAV that found the target. The DM format is presented in Table 3 and a description of the fields that constitute the header is given as follows:  
* Identification: unique identifier of the message within a sequence. The initial packet starts with zero value, while the following ones are incremented by one unit until the value of the last packet is reached;  
\begin{table}
\begin{tabular}{c c c} \hline  & **Field** & **Size (bytes)** \\ \hline ID & Identification & 4 \\ TY & Message Type & 1 \\ SN & Source Node Address & 2 \\ DN & Destination Node Address & 2 \\ PN & Packet Number & 4 \\ TI & Time & 10 \\ LA & Current location & 18 \\ QN & Number of targets found & 4 \\ LN & Location of targets & 36 \\ \hline \end{tabular}
\end{table}
Table 2: Control message (CtrM) Format.  
* Message type: it indicates the packet type, that is, type 2 in this case;
* Number of packets: it indicates the number of packets that must be sent;
* Source Node Address: identifier of the node that found the boat and generated the message;
* Packet Number: unique identifier of packet sequence, whose value is equal to the field of the CtrM header that contains the boat location. All packets that belong to a same sequence assume a constant value for this field. This field is used in the reassembly process, being associated with the source node address to identify the target location;
* Data: it contains the fragmented image of the target.
# Damping Ratio of Sand Containing Fine Particles in Cyclic Triaxial Liquefaction Tests
## 2 Materials and Methods  
### Physical Properties of Test Materials  
The base sand used in this study is a poorly graded French reference sand: Hostun sand 31 (termed \"HN31\" hereafter). The sand is characterised by uniform sub-angular grains and widely adopted in many European laboratories. A non-plastic silica powder C500 was selected as the added fines. The corresponding grain size distribution curves measured by the laser diffraction method are displayed in Figure 3, and the detailed index properties [17] are summarised in Table 1.  
Figure 3: Grain size distribution curves of the test materials [17].
# Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5
## 4 Experimental Results and Analysis
### Detection Effect  
To validate the effectiveness of the P2E-YOLOv5 model for small-target detection in transmission lines, we utilized the trained model on the augmented small-target data set to detect images from the test set. Furthermore, we randomly selected three transmission line images captured in different scenes for detection. The test results are depicted in Figure 14.  
In Figure 14, the test results of each algorithm are shown with rectangle boxes marking the successfully detected small targets. Additionally, blue circles indicate small missed targets that were missed by the detection algorithm.  
Upon observation, the Faster-RCNN and YOLOX detection algorithms demonstrate high detection accuracy, accurately identifying most small target objects. However, for some small targets with subtle features, some cases are missed. RetinaNet, SSD, and YOLOv3_pp can detect most small targets, but with low accuracy and numerous omissions. The YOLOv5 detection algorithm shows high detection accuracy and can detect most small targets, yet some cases are still missed.  
Figure 14: _Cont._  
Comparing the six detection algorithms, the P2E-YOLOv5 algorithm stands out for accurately identifying small targets in different scenes with higher detection accuracy and the lowest missing rate. Several factors contribute to this success. Firstly, the added ECA attention module significantly enhances the network's focus on small targets and reduces the impact of environmental interference. Secondly, the newly introduced high-resolution P2 detection header significantly improves the network's ability to detect small targets. Lastly, employing EIOU_Loss is used as the regression loss function to enhance the precise identification and positioning of small targets. Consequently, the P2E-YOLOv5 detection algorithm outperforms the other six detection algorithms on a small-target detection data set of transmission lines.
# New Developments and Progress in Absorption Chillers for Solar Cooling Applications
## 4 Prototypes of Absorption Chillers
### Advanced Absorption Chillers
#### 4.2.2 Mechanical Compressor Assisted Absorption Chillers
Figure 18: Single-stage water/LiBr cycle with an integrated mechanical compressor between the (**a**) evaporator and absorber and the (**b**) desorber and condenser. Adapted from References [82; 83]. Note: ABS = absorber; COM = compressor; CON = condenser; DES = desorber; EVA = evaporator; SHX = solution heat exchanger. The chiller unit was designed to ensure undisturbed transonic vapor flow into the compressor. The risk of the entrainment of water droplets and flow losses was also minimized. The turbo compressor's implementation was based on the numerical analysis carried out and can be applied up to a pressure ratio of 3.5 and a maximum number of revolutions of 90,000 rpm. The isentropic efficiencies were approximately 60% for pressure ratios of less than 3.0 [83]. The research into laboratory and pre-industrial prototypes of absorption chillers that can effectively use solar energy is summarized in Table 2.  
Figure 19: A prototype of the mechanical compressor-assisted single-stage water/LiBr absorption chiller developed at the Munich University of Applied Sciences (Germany). Adapted from Reference [82]. (**a**) Components of the prototype; (**b**) Internal view of prototype. Reproduced with the permission of the International Institute of Refrigeration ([[https://iiifir.org](https://iiifir.org)]([https://iiifir.org](https://iiifir.org))) + [PERSON] et al. [82].