esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### AE Method Application for Bridge Monitoring
#### 3.2.5 SHM-System  
SHM-system for this application was designed and installed on the bridge. AE sensors, vibration transducers, strain gauges, temperature sensors, modules for collecting, measuring and processing signals were installed on the span of the bridge structure (box beams). Low-frequency AE sensors, inclinometers, crack opening sensor were installed on the supports. In addition, SHM-system is equipped with video cameras to monitor the traffic situation, a weather station to monitor wind loads and to determine the presence of precipitation for AE signals filtering and a communication system.
# Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles
## 3 Delay Tolerant Network Protocols
### Black and Grey Hole PRoPHET  
The more complex PRoPHET (Probability Routing Protocol using History of Encounters and Transitivity) uses probabilities [31]. It calculates the delivery predictability based on contact history between nodes, where a higher value represents a greater chance of future contact with the destination node. This means that a message is only replicated if the delivery predictability of the new node is larger than that of the transmitting node [34]. This keeps overheads low, allowing it to be implemented where there are limited resources [35]. The delivery predictability value allows a projection as to how likely a node is to be in contact with the destination node. This means that a node stores a value for each node it encounters, and then increases the predictability upon future contacts. Only if the receiving node is more likely to deliver a message does the transmission node send the message.  
There are more advanced protocols based upon PRoPHET such as PRoPHET+ [36]. The latter takes into consideration other data about the nodes such as the buffer size, location, and status to support the delivery predictability. The further considerations allow the nodes to decide which is most likely to deliver the message if their predictability value is the same. For example if the nodes were both equally likely to deliver the message, but one was much closer than the other, it would receive the message as it would be deemed most likely to deliver the message.  
The vanilla PRoPHET protocol was amended to allow communication with grey and black hole nodes. As well as this, another algorithm was generated to allow vehicles to perform as black and grey hole nodes. Unlike the former algorithms, when nodes meet they exchange information about contact history and the probability of delivery. Due to this, the attacking nodes still need to perform some function rather than just removing data. The attacking nodes can falsely publish their probability of delivering a message to increase the likelihood of them receiving (and therefore removing) it.  
```
startwhileTruedo ifContactedNodethen  ExchangeSummaryVector()  UpdateDeliveryProbabilities() ifBlackHolethen  Drop Messages elseifGreyHolethen ifDroppingMessages then  Drop Messages else ifNextnodedoesnothavemessagesthen ifNextnodeisdestinationthen  Forward messages ifNextnodehasgreaterprobabilityofdeliverythen  Forward Messages  end if else ifNextnodedoesnothavemessagesthen ifNextnodeisdestinationthen  Forward messages ifNextnodehasgreaterprobabilityofdeliverythen  Forward Messages  end if  end if
```  
**Algorithm 3**Black and Grey Hole PRoPHET Router
# Moisture Measurement in Paper and Cardboard Packaging Waste Bales for Recycling
## 3 Results and Discussion
### Moisture Content Determined by the Oven Drying Method  
A relevant output of this work is the oven moisture content determination of the paper and cardboard packaging waste bales produced in geographically different places with different compositions of cardboard content all during the winter season. Therefore, it is possible to get an idea of the expected moisture for these kinds of bales at this time of year.  
The most representative moisture class of the analyzed bales was that of 10-13% moisture, representing a 43.8% frequency. Bales with moisture contents between 7% and 10% represented 20.8% of the total bales sampled, and bales with moisture contents between 13% and 16% represented a 25.0% frequency. Only four bales (frequency of 8.3%) showed moisture contents ranging from 16% to 19%. Therefore, 64.6% of the analyzed bales had moisture contents between 7% and 13%. Bales with moisture contents above 16% were rare, representing only 10.4% of the sample.
## 3 Maintenance and Lifespan Management
### Developing the Means that Enable Signals to be Digitally Acquired
#### 3.1.3 Partial Discharges  
The key to the problem of partial discharges is in a good design of the busbar, as recorded by [PERSON] et al. [179]. New developments in busbar are necessary, and their improvement will allow reducing partial discharges.  
Relating to partial discharges, [PERSON] et al. [180], [PERSON] et al. [181] and [PERSON] et al. [182] propose a technique for classifying partial discharge patterns based on ANN.  
[PERSON] et al. [183] present a methodology for the diagnosis of HVDC systems based on partial discharge analysis with a fuzzy system. [PERSON] et al. [184] propose an optimized electromagnetic detection method based on Fourier transform.  
It is possible to monitor the partial discharges of networks through direction of arrival (DOA), as presented by [PERSON] et al. [185].  
There is a certain relationship between the partial discharges and the gas dissolved in the oil, and this is presented in the experimental work of [PERSON] et al. [186]. In addition, the method is based on statistical parameters with canonical correlation analysis.  
By means of a chaotic analysis of partial discharges, it is possible to detect certain patterns related to the defects in cable terminals and connection boxes, as presented by [PERSON] et al. [187].  
It is possible to obtain a tool for the complete monitoring of defects due to partial discharges, as proposed by [PERSON] et al. [188]. The system is a decision support system. [PERSON] et al. [189] develop a technique that utilizes the random occurrence of partial discharges for detection and used for semi-square voltages.  
To summarize, Table 7 shows all the work described in this section. In the rows are shown different areas (sensors, normalization and partial discharges) while in the columns is shown the application rate on developing the means that enable signals to be digitally acquired (new devices and parameters and implementation of the IEC 61850). As can be seen in the previous table, the evolution of sensors and electrical elements will be a reality. In addition, currently shows that these devices should be integrated with IEC 61850. Along with the development of new sensors, it is necessary to define new interesting variables to monitor, since one of the main objectives will be the detection of partial discharges.
## 4 Theoretical Motivation of NBR-Clustering
### NBR Clustering Framework  
Given a resilience measure \(R\) (it is assumed that \(R\) expresses a minimization objective taken over possible attack sets of nodes \(S\)) and the resilience measure of a specific graph \(G\) denoted by \(R(G)\), the general NBR measure \(R\)-clustering framework, NBR-Clust, is as follows:  
1. If not \(G\), transform point data into a graph \(G\).
2. Approximate \(R(G)\) with an acceptable accuracy, and return candidate attack set \(S\) whose removal results in a number of candidate groupings (components).
3. Adjust the number of candidate groupings. If there are too many groups, combine them until the desired number of components is obtained. If there are too few groups, perform hierarchical clustering by proceeding recursively on the component \(C_{i}\) with the lowest resilience value \(R(C_{i})\). Continue hierarchical clustering until the desired number of groups is obtained.
4. For a complete (i.e., traditional partition-style) clustering outcome, perform a node-assignment strategy that assigns each node \(v\) of \(S\) to the component \(C\) (from step (3)) in its original semi-partition with which \(v\) shares the most edges. Break ties arbitrarily.
5. To cluster in the presence of noise (which implies removal of some nodes identified as noise), remove \(S\). It is possible that some components consist entirely of noise (often significantly smaller components). In such cases, identify and remove those noise components.
6. For clustering of networks with overlap, detect and assign overlap nodes appropriately.  
The implementation of this clustering framework is available on the project website [43].  
The NBR-Clust framework has great versatility in that it can be used on many different types of graphs, depending on the resilience measure \(R\) employed in step (2). Classical resilience measures such as those employed in this work are in most cases only meaningful on connected graphs. However, it is shown in [10] that the measures can be extended to account for disconnected and directed graphs. If a resilience measure can be applied to a particular graph, such as a directed, multi or binary graph, then the NBR-Clustering framework can also be applied. The following sections describe the implementation of parts of the NBR clustering framework.
## 2 Materials and Methods
### Data Preprocessing  
The data preprocessing algorithm is shown in Figure 8.  
The first step of the data preprocessing algorithm was to create a dataset from separate files of hourly power consumption of the entire compressor station and files of daily power consumption by ACUs and GCUs.  
The second step was to convert the matrix of hourly power consumption to a vector.  
The third step was to perform linear interpolation for the weather data to obtain hourly values.  
The fourth step was to create an initial dataset by adding the hourly weather data to the power consumption data and performing a Spearman correlation analysis.  
Figure 7: Interaction of storage agent, generator agent, and consumer agent.  
The fifth step was to create a dataset for a machine learning application. Forecast of power consumption is retrospectively based:  
\[y_{i}^{*}=f(g(y_{i-h},\ y_{i-h-1},\ldots,\ y_{i-h-w}),X), \tag{13}\]  
where \(y_{i}^{*}\) is the forecasting power consumption at \(i\)-time, \(f\) is the forecasting model, \(g\) is the function that defines the rule of selection of actual retrospective values of power consumption, \(h\) is the forecasting horizon, \(w\) is the width of the window of retrospective data, and \(X\) is other values.  
After the initial correlation analysis, it was determined that the forecast model can be sufficiently built with 12 h time step and 4-day retrospective depth [8]. This can be justified by the cyclic nature of production processes. In addition, values of 12, 24, and 36 h before the forecast hour were not considered when formulating the schedule of power consumption forecast because these data were not available in the ACEMS at the time of power consumption forecast creation. For example, the following data were used to forecast power consumption for 9 September 19:00-20:00:  
* First, 7 September 19:00-20:00 and 07:00-08:00;
* Second, 6 September 19:00-20:00 and 07:00-08:00;
* Third, 5 September 19:00-20:00.  
Therefore, \(y_{i}^{*}\) can be described as  
\[y_{i}^{*}=y_{i}^{*}=f(y_{i-48},\ y_{i-60},\ y_{i-72},y_{i-84},y_{i-96},\ X), \tag{14}\]  
where \(X\) can include the following:  
* Weather data:
* Wind speed in hour \(i\);
* Temperature in hour \(i\);
* Atmospheric pressure in \(i\);
* Humidity in hour \(i\);
* Production plan data:
* Hour number \(i\) (from 0 to 23);
* Day of the month (from 1 to 31);
* Day of the week (from 1 to 7);
* Month number (from 1 to 12);
* Production process index;
* Daily power consumption by ACUs and GCUs.  
The final step involved removing rows with zero power consumption values from the dataset. After preprocessing, the dataset contained 16,896 rows.  
Figure 8: The algorithm of data preprocessing.  
The whole dataset was split into training and testing sets in a 90:10 ratio.
# An Efficient Trust-Based Scheme for Secure and Quality of Service Routing in MANETs
## 4 Simulation and Result Analysis  
NS-2 (ver. 2.34) simulator system was utilized to evaluate the effectiveness of TSQRS with the adversary model. We performed the simulation for two scenarios: (1) by varying the mobility of nodes and (2) by varying the number of malicious nodes. We use packet drop ratio (PDR), routing overhead (RO), energy consumption (EC) to assess the performance of our proposed scheme. To show that TSQRS can achieve better routing decisions, the performance of TSQRS is compared to ETRS-PD and AODV with the adversary model. We carried out our simulations in a 1000 \(\times\) 1000 m\({}^{2}\) area and employed IEEE 802.11 MAC. The benign nodes were distributed randomly throughout the network which employs the AODV, ETRS-PD and TSQRS protocols. Randomly positioned nodes perform various packet forwarding misbehaviors according to the adversary model. Table 1 summarizes the simulation parameters.
# The Design and Implementation of a Phased Antenna Array System for LEO Satellite Communications
## 3 Theoretical Considerations on Phased Antenna Arrays for Leo Satellite Communications
### Phase Shift Signs Effects on the Antenna Array Radiation Pattern Orientation  
In our analysis, we considered the displacement of the wavefront from the reference element to the other elements so that the progressive phase shifts that will be introduced,first in simulations and then in the real array, were calculated by inverting the sign for the values in Equation (7).  
This resulted in rotating the radiation pattern by 180\({}^{\circ}\) and obtaining the intended results. Numerous simulations were performed using MMANA-GAL, version 3.0.0 [30] antenna design and analyzing software for several beam orientation angles, demonstrating the effect of the sign of the phase taper. Equation (7) was adapted for a square uniform planar antenna array with four elements spaced at half the wavelength corresponding to the frequency of 436.5 MHz (the middle of the 435-438 MHz band) so that M = N = 2, \(1\leq\text{m}\leq 2\) and \(1\leq\text{n}\leq 2\). Figure 2a illustrates the array positioned as in the theoretical aspects shown above. To obtain beam steering considering 0\({}^{\circ}\) at the top of the Ox axis and the positive values of the angle as the trigonometric one, the array should need to be physically designed according to the topology in Figure 2b. Thus, in practical experiments, we will make use of the positive phase taper as given in Equation (9), where \(\beta^{\prime}_{mn}\) is the phase taper used, which has the opposite sign compared to the values of \(\beta_{mn}\) in (7).
## 4 Results and Analysis
### Stability of The FSF  
Figure 6 shows the PSNR value of the fusion image when the downsampling factors are 4, 8, 16, 32, and 64 for the comparison method. Our proposed method FSF can maintain a high fusion effect under different downsampling factors, while other methods will decrease the fusion quality as the downsampling factor increases. The stability of our proposed method FSF is based on the fact that in matrix singular value decomposition, only the first few singular values need to be retained to recover the information of the original matrix. Furthermore, CSTF and Hysure are also stable for the downsampling factors since both methods also use SVD to extract part of the fusion information in the fusion process. On the other hand, the NLSTF method only achieves better fusion results when the downsampling factor is 4, while the fusion results are the worst in all other cases since NLSTF uses a nonlocal clustering operation before fusion, limiting the super-resolution capability.  
Figure 5: First row: the competing approaches to reconstructed images of oil_painting in the CAVE database at 10 th band. Second row: the corresponding error images of the 10 th band. Third row: the competing approaches to reconstructed images’ false-color images formed by bands 10 th, 19 th, and 28 th. Fourth row: the corresponding error images of the false-color images.
## 5 Conclusions  
The received signal strength of the underwater optical communication system changes with the change in the communication distance, resulting in a single modulation mode that does not match the channel loss and the dynamic range of the PMT. Therefore, this paper studies a rate adaptation scheme of underwater optical communication with joint control of the photoelectric domain. By estimating the channel conditions, the SNR and incident power can be obtained, allowing for the adaptive adjustment of the modulation scheme and the transmittance of the LCLV. The simulation results show that under the FEC threshold, the proposed algorithm extends the dynamic range of the communication distance and PMT, improves the throughput and verifies the effectiveness and reliability of CPS technology.  
Conceptualization, Y.C. and H.Q.; methodology, Y.C. and Y.L.; formal analysis, Y.C. and H.Q.; investigation, Y.C. and Y.L.; resources, H.Q. and Y.L.; data curation, Y.C., H.Q. and Y.L.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C., H.Q. and Y.L.; visualization, Y.C., H.Q. and Y.L.; supervision, H.Q.; project administration, H.Q.; funding acquisition, H.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China under grant (62371148), and Innovation Project of Guangxi Graduate Education (YCBZ2022110).  
**Informed Consent Statement**: Not applicable.  
Figure 9: Throughput comparison of different adaptive modulation methods (modulation mode from top to bottom: blue line—256 QAM, 64 QAM, 16 QAM, 4 QAM and BPSK; red line—256 QAM, PS\(-\)256 QAM (H = 7), PS\(-\)256 QAM (H = 6), PS\(-\)64 QAM (H = 5), PS\(-\)64 QAM (H = 4), PS\(-\)16 QAM (H = 3), 4 QAM and BPSK).  
Data Availability Statement: The data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors declare no conflicts of interest. The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.
## 6 Farber disease
### MALDI Non-Imaging MS Analysis of GSLs in Various Cell Cultures
#### 3.6.3. GSLs in Hepatic Cancer Cell Lines  
Directing attention toward the examination of GSL-glycans, a study investigated samples extracted from hepatic cancer cell lines (HCC) MHCC97L, MHCC97H, and HCCLM3, along with a normal human liver cell line, HL-7702. Du et al. [121] used lectin microarrays (not detailed) and supplementary data from MS analysis using a Bruker Daltonics ultrafileXtreme MALDI-TOF/TOF (Bruker Daltonics, Bremen, Germany) and 2.5 DHB as the matrix, in positive ion mode. The GSLs from the cultured cells were extracted using chloroform/methanol in three stages, and then NaClIO was added for oxidative release of the GSL-glycans. Afterward, the GSLs were purified using Sep-Pak C18 cartridges and subjected to MS performed in positive ion mode. The analysis of the mass spectra revealed the presence of specific glycan structures in different cell lines (Table 4). The presence of fucosylation as a modification of glycans present in the samples was determined by MALDI-TOF/TOF performed on different precursor ions. Thus, by comparing the glycans from hepatocarcinoma cell lines with a normal liver cell line, Du et al. found significantly different signals which indicate upregulations of corresponding glycoproteins such as \(\alpha 1-2\) fucosylation and \(\alpha 2-3\) sialylation of GSLs in HCC compared to normal controls [129]. The significant abundance of GSLs with \(\alpha 1-2\) fucosylation in all HCC indicates that the metastasis ability of HCC cells rises with this modification, which is in accordance with many reports which showed that glycan fucosylation is one of the numerous biological phenomena during tumor development, having a tumor-promoting effect in ovarian, prostate, breast, bladder, and epidermoid tumors and being a target in cancer diagnosis and therapy [277; 278; 279; 280; 281; 282]. In contrast, other data showed a tumor suppressive effect of \(\alpha 1\)- 2 fucosylation in hepatocellular carcinoma [283; 284].
# Optimization of Multi-Mission CubeSat Constellations with a Multi-Objective Genetic Algorithm
## 2 Mission Design  
### CubeSat Design  
CubeSats, in general, have a standardized size and form factor. The CubeSat standard size uses one unit (1U) measuring 10 \(\times\) 10 \(\times\) 10 cm in volume, which is extendable to larger sizes. In response to the need to conduct broader and more reliable missions, CubeSat platforms larger than 3U have been developed. Furthermore, the significant advancements in CubeSat platform technology and capabilities make them suitable to perform these missions [13; 14]. Larger CubeSats, such as 6U and 12U, will be able to perform multiple missions with improved reliability, which was previously limited by the size of smaller platforms. The first 12U CubeSat Star of AOXiang (SAOX) was developed by Northwestern Polytechnical University (NPU) in China. The SAOX project, launched in 2016, was able to test the 12U CubeSat platform in orbit. SAOX demonstrated the larger CubeSat platform's ability to perform reliable missions at a low cost [14].  
A CubeSat constellation system will be designed to meet the demand for global Earth observation and regional disaster monitoring and management missions. A 12U CubeSat platform with two payloads will be designed to satisfy these missions. The first mission, global Earth observation, uses an electro-optical camera (EOC) payload with a 30\({}^{\circ}\) field of view (FOV) conic sensor. The second mission, regional disaster monitoring and management, collects data from regional target area data centers using UHF receiver. A standard commercial off-the-shelf (COTS) UHF receiver at 436 MHz frequency with a 0.17 m dipole antenna is assumed for the second payload. High-level specificationsfor the 12U CubeSat are given in Table 1 based on CubeSat standards and developed systems [14; 15].
# An Effective Beam Width Model of Slab in Thick Wall-Thick Slab Joints Subjected to Lateral Forces
## 2 Background
### Effective Beam Width Method (EBWM)
Based on this consideration, they thus modeled the effective beam width as a simple linear function of the slab span length and column width for both interior and exterior cases in the elastic range. However, they pointed out that it tended to produce calculated stiffnesses higher than the true elastic stiffness. Analytical evaluation of the EBWM concept considering effects of connection geometric dimensions and cracking conditions was also performed in their study. [PERSON] and [PERSON] [15] proposed a simple model of effective beam width for initial stiffness of column-slab joints based on experiment results obtained from a two-story, two-bay slab-column frame structure. In their model, the initial stiffness for the interior column-slab joints was expressed by 1/3 times slab full-width while the initial stiffness for the exterior column-slab joints was expressed by a sum of the column width and the column depth. As a simple and convenient methodology to evaluate the stiffness of a flat slab by modeling an effective beam, both experimental and analytical research has been conducted on the application of the EBWM concept. Even though the rotational stiffness was considered roughly by flexural behavior of slabs while shear and torsional behaviors were not considered separately, the EBWM concept still showed good accuracy and calculation simplicity for estimating the stiffness of flat slab components according to previous research.