esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Multispectral Data and Google Earth Engine  
GEE is a cloud-based platform for processing large-scale geospatial data for mineral mapping, environmental monitoring, and analysis. The GEE provides free and easy access to Landsat and Sentinel satellite datasets [36; 37]. Due to GEE development, the research enthusiasm in remote sensing and geospatial multispectral data science has increased [34]. GEE is a free-to-use platform providing access to:  
* Multispectral remote sensing dataset of various satellites, such as Landsat, Sentinel, and Modis, along with an explorer web app and other ready-to-use products.
* Different AI/ML algorithms with high-speed parallel processing using Google computational infrastructure.
* The two most popular programming languages are JavaScript and Python, supporting (APIs) application.
* Programming interface with the development environments.  
These GEE core features enable users to visualize and process large-scale multispectral geospatial data with high-speed supercomputers using available machine learning algorithms. Generally, SWIR and TIR bands are considered the most important for distinguishing lithologies [1]. The Sentinel-2 MSI datasets have a spatial resolution of 20 m for SWIR bands but lack TIR bands. In contrast, Landsat 8 OLI datasets contain TIR bands with a spatial resolution of 100 m and therefore have limited spatial detail, i.e., 1:100,000 [54]. Thus, the spectral responses in the TIR band may be diluted due to the mixed response from features due to its limited temporal, spatial, and spectral resolutions [17; 23].  
Additionally, vegetation and soil cover further make it difficult to map the deposits [24]. The Sentinel-2 satellite with the MSI on board [55] has a high revisit frequency, and mission coverage provides local, regional, national, and international data. In addition, due to the relatively higher spatial resolution SWIR bands, Sentinel-2 is generally more suitable for mineral exploration and lithological mapping [2]. However, robust ML algorithms must be required to deal with limited data (without TIR bands) for identifying various rock types.  
Limestone shows spectral variations in the SWIR range (2.10-2.30 \(\upmu\)m) and TIR range (10.25-11.65 \(\upmu\)m) [17; 21]. Gypsum shows a wide range of varying absorption features in SWIR ranges (1.2-1.38 \(\upmu\)m, 1.61-1.75 \(\upmu\)m, and 2.21 \(\upmu\)m) depending upon the low-frequency vibrational modes of associated crystal molecular water, O-H stretching fundamentals, and combinations of H-O-H bending [9]. Similarly, shale displays absorption features at 1.40 \(\upmu\)m attributed to OH/H2O stretches, 1.90 \(\upmu\)m related to H2O stretches, and 2.20 \(\upmu\)m due to a combination of the OH-stretching fundamental with Al-OH bending mode [10]. Therefore, Sentinel-2 bearing SWIR bands and high spatial resolution can be used for distinguishing these formations through robust ML classification algorithms, such as SVM and ANN [26].
## Materials
### Sentinel-1 SAR Data  
Altogether, 206 scenes of Sentinel-1 dual-polarization C-band SAR instrument imagery, which have VV (Single co-polarization, vertical transmit/vertical receive) and VH (Dual-band cross-polarization, vertical transmit/horizontal receive) bands, were employed in the crop extent identification, and they have been archived in GEE as the Sentinel-1 SAR Ground Range Detected (GRD) collection. All of the images were preprocessed by the Sentinel-1 Toolbox, and preprocessing included thermal noise removal, radiometer calibration orthorectification with a Shuttle Radar Topography Mission (SRTM) or Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM (Digital Elevation Model), and conversion to a backscatter coefficient (\(\sigma^{0}\)) in decibels (dB) [58].  
Figure 3b shows the Sentinel-1 ascending observation number between 1 June and 31 October 2017 for each pixel. Sentinel-1 had an average of 16.5 observations and a maximum of 26 observations. The SAR imagery covered 54.7% of the total pixels 10 to 12 times, 23.0% of the entire area 20 to 22 times, and 21% of the entire area 20 to 24 times. Compared with optical images for which repeat observations can provide more information for a time series, the overlap of SAR is redundant, because the incidence in overlap area ranges from approximately 30 to 50 degrees. Therefore, the image with a lower incidence angle is kept in the repeated observational areas [1].
# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
###### Abstract  
The stream mining paradigm has become increasingly popular due to the vast number of algorithms and methodologies it provides to address the current challenges of Internet of Things (IoT) and modern machine learning systems. Change detection algorithms, which focus on identifying drifts in the data distribution during the operation of a machine learning solution, are a crucial aspect of this paradigm. However, selecting the best change detection method for different types of concept drift can be challenging. This work aimed to provide a benchmark for four drift detection algorithms (EDDM, DDM, HDDMW, and HDDMA) for abrupt, gradual, and incremental drift types. To shed light on the capacity and possible trade-offs involved in selecting a concept drift algorithm, we compare their detection capability, detection time, and detection delay. The experiments were carried out using synthetic datasets, where various attributes, such as stream size, the amount of drifts, and drift duration can be controlled and manipulated on our generator of synthetic stream. Our results show that HDDMW provides the best trade-off among all performance indicators, demonstrating superior consistency in detecting abrupt drifts, but has suboptimal time consumption and a limited ability to detect incremental drifts. However, it outperforms other algorithms in detection delay for both abrupt and gradual drifts with an efficient detection performance and detection time performance.  
concept drift; drift detector; adaptive learning; synthetic datasets; stream mining Article
## 1 Introduction  
The mobile health (mHealth) provides an effective approach for medical and health services through the use of mobile communication technologies, such as the mobile phone. The mHealth improves not only the working efficiency of the hospital but also the health of the people, and it is becoming one of the most active research and application directions in the field of medical information recently [1]. However, the flourish of mHealth still faces many challenges, including communication mode and information security. The Opportunistic Networks (OppNets) is composed of a large number of mobile intelligent terminals and takes advantage of their movement to facilitate data transmission and exchange. The appearance of OppNets makes people's communication with each other break through the limitations of time and space, and provides them with quick and convenient access to information [2; 3]. Therefore, OppNets is more suitable for the mHealth applications and has a good prospect of application [4].  
Different from the traditional networks, OppNets adopts the \"storage-carry-forward\" routing strategies to transmit the messages [5]. The advantage of this strategy is that it can forward messagesbetween disconnected mobile nodes and expand the communication capacity of the network. However, there are some problems, such as lower delivery ratio, longer transferring latency, and the dependency on the cooperation of the nodes. To design more efficient routing mechanism, the community is introduced into OppNets, because nodes in the group usually have more opportunities to forward messages [6; 7; 8].  
Unfortunately, the community-based routing scheme encounters critical challenges in practical situations, such as the measure metric of the relationship between nodes, the community detection algorithm adapted to the unique signatures of OppNets, and efficient messages transmission between communication domains. To overcome these disadvantages, we propose an adaptive routing optimization algorithm in OppNets for mHealth. This algorithm firstly utilizes a new metric of node relations to detect the communities based on local information. Then, the super nodes are adaptively deployed to the network to ferry messages between communication domains.  
The main work of this paper includes the following three aspects: (1) To more accurately measure the relationship strength between nodes, we define a new metric using the average message forwarding delay with the historical encounters. (2) In order to avoid the difficulty that the global information of OppNets is too hard to obtain, we present a local community detection algorithm based on the metric. (3) With the help of ferry nodes, we propose an adaptive routing optimization algorithm in OppNets for mHealth.
# Research on Key Technologies of Elastic Satellite Optical Network Based on Optical Service Unit
## 3 Analysis and Discussion
### The Performance of OISL-OSU  
The mapping procedure for IP traffic over OISL-OSU is depicted in Figure 8, wherein IPv6 data (ICMPv6) are mapping into the payload of the ODU (as shown in Figure 7(a)). The ODU frame is then multiplexed to the OTU frame, with the OTU overhead (i.e., 0 xF6F6F6282828), as illustrated in Figure 7(b).  
Moreover, the OISL-OTN-FEC frame encoding process is presented in Figure 9. Given that VIVADO RS encoder/decoder intellectual property cores only support an 8-bit data bit width, while the data bit width of the OISL-OTN-FEC frame is 64 bits, eight RS encoder/decoder intellectual property cores are simultaneously instantiated for processing the frame payload and frame checksum (as shown in Figure 8(a) and Figure 8(b), respectively).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **SCID \#101** & **SCID \#1** & **SCID \#2** \\ \hline Forward LSP & Out-port: 3 1 & In-port: 3, Out-port: 1 & In-port: 1 \\  & Out-label: 0x00D2 & In-label: 0x00D2, Out-label: 0 xFFFF & In-label: 0 xFFFF \\ Backward LSP & In-port: 3 1 & Out-port: 3, In-port: 1 & Out-port: 1 \\  & In-label: 0 xFFFF & Out-label: 0 xFFFF, In-label: 0x0054 & Out-label: 0x0054 \\ \hline \hline \end{tabular} 1 The out/in-ports of forward/backward LSP are the same, i.e., port 3.  
\end{table}
Table 7: The bi-directional LSP between node SCID #101, node SCID #1, and node SCID #2.  
Figure 8: OISL-OTU frame.  
Figure 9: _Cont._
# A Systematic Review of Osteotomies to Correct Hallux Valgus in the First Metastasis
## 3 Results
### Basal Closing Wedge Osteotomy
#### 3.1.2 Follow-up and Biomechanical Analysis  
As per [PERSON] et al. (2018), there are significant issues associated with the osteotomy type, one of which is the shortening of the first metatarsal bone. On average, 5 mm of shortening is observed. The incidence of dorsal angulation is another common issue, affecting 25% of the cases analyzed. Transfer metatarsalgia is also a common problem, affecting 25% of the feet studied. Besides being a challenging technique, determining the exact dimensions from pre-operative radiographs is often difficult. However, the average reduction in the intermetatarsal angle (IMa) of 16.1\({}^{\circ}\) in the pre-operative period increased to 6.7\({}^{\circ}\) in the final follow-up, representing a good correction. The authors (2018) spine that the results were not as good as those reported for other procedures and, therefore, recommend using alternative methods, such as crescent basal osteotomy or Chevron basal osteotomy.  
In the literature, no recorded follow-up studies involving biomechanical methodologies are associated with this surgical technique.  
Figure 5: Basal closing wedge osteotomy procedure. **Left—**the laterally based bony wedge is removed with an oscillating saw, taking care to preserve the medial cortex. **Right—**the resulting osteotomy is closed and fixed with a navicular screw.
# Can Blockchain Strengthen the Energy Internet?
## 3 Improved Trust, Security and Privacy
### Future Directions  
Under the current blockchain context, immutability of the distributed ledger has been exploited to establish the trust factor and verify information security. However, the developments emerging with 6G technology facilitate distributed computing utilizing edge devices, which could further enable consolidation of resources to achieve computational efficiency [49]. Such collaborations would pose a risk of accumulating 51% authority over the peer nodes, thereby gaining capabilities for modification of the past records. Such adversaries should be addressed in the envisaged grid operation.  
Further, integrity and confidentiality of the information, along with the identity of the user, could be compromised through the revealing of the public and private keys used in PKI. This could be mainly due to the prolonged usage of the keys and as a result of the malicious attempts to reveal these cryptographic text patterns utilizing quantum computing, which is the most recent development enabling extensive computational capabilities [8]. Ensuring security and privacy with technological progressions would be challenging in future grid implementations [50].  
**Lessons:** Blockchain integration with Smart Grid 2.0 has facilitated in mitigating software and network related attacks, including Man-in-the-Middle and DDoS adversaries [37]. Further, the existing work has proposed different user authentication and privacy-preserving approaches, which have been implemented through cryptographic techniques used in blockchain [45; 51; 52; 53; 54; 55]. The most widely adapted approach could be identified as cryptographic encryption-based digital signatures for user verification [37]. However, modern smart grids, which are to incorporate predictive data analytic tools for intelligent decision, are vulnerable to AI and ML-related attacks [15; 16]. These adversaries would overlay the security threats governing Smart Grid 2.0, which would require accelerating the existing research initiatives.
# Cross-View Outdoor Localization in Augmented Reality by Fusing Map and Satellite Data
## 5 Discussion and Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Name**} & \multicolumn{6}{c}{**Cross-Area**} \\ \cline{2-11}  & & \multicolumn{4}{c}{**Lateral**} & \multicolumn{4}{c}{**Longitudinal**} & \multicolumn{4}{c}{**Azimuth**} \\ \cline{2-11}  & & **d = 1** & **d = 3** & **d = 5** & **d = 1** & **d = 3** & **d = 5** & **\(\theta\) = 1** & **\(\theta\) = 3** & \(\theta\) = 5** \\ \hline LM [13] & 27.82 & 59.79 & 72.89 & 5.75 & 16.36 & 26.48 & 18.42 & 49.72 & 71.00 \\ +train 21 epochs & 40.81 & 75.38 & 83.53 & 5.42 & 17.14 & 28.08 & 25.40 & 61.03 & 78.87 \\ +bigger U-Net & 41.08 & 71.69 & 79.65 & 11.58 & **30.05** & 44.59 & 164.27 & **98.46** & **99.91** \\ +OSM & 50.13 & 78.61 & 84.01 & 7.12 & 20.21 & 31.66 & 49.70 & 78.71 & 87.58 \\ +start at _lon - 1_ & **56.62** & 78.32 & 81.62 & **22.42** & **47.31** & **57.61** & 57.57 & 92.54 & 98.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation of contributions compared to original LM model. Best, second-best, and third-best results are marked. The original model was trained for just 2 epochs and thus did not achieve convergence. Increasing the amount of training the model underwent and the size of the U-Net using a more conventional architecture improved the recall rate. Using OSM data and starting at the minimum longitudinal value improved the model to competitive accuracy. **Author Contributions:** Conceptualization, M.R.O. and S.d.H. ; Methodology, M.R.O. and S.d.H. ; Investigation, R.E., S.d.H. and D.D. ; Writing--original draft, R.E. ; Writing--review & editing, D.D. ; Supervision, M.R.O., S.d.H. and D.D. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data from this study can be found at Planet Dump repository [[https://planet.osm.org](https://planet.osm.org)]([https://planet.osm.org](https://planet.osm.org)) (accessed on 10 June 2023). **Conflicts of Interest:** The authors declare no conflict of interest.
## 2 System Model
### LFSR Based Key Generation  
The steps for generating various sequences using LFSR (Ref Figure 4) are given below.  
* Select a three-bit of initial value randomly except all zeros.  
Figure 3: Receiver at the trailing vehicle.  
Figure 2: Transmitter at the leading vehicle.  
* [Step 2:] AND the initial value with '001' and get the result. Let this be A.
* [Step 3:] AND the initial value with '010', and right shift the result by one bit. Next, fill the empty MSB of the result with '0'. Let this be B.
* [Step 4:] XOR A and B. Then extract the LSB of the result and set as C.
* [Step 5:] Right shift the initial value by one bit and replace the MSB of the result with LSB of C. This gives new three-bit value.
* [Step 6:] Store the new three-bit value in an array. Consider this three-bit value as an initial value.
* [Step 7:] Repeat from step 2 to step 6 until seven different three-bit values are stored in an array. These seven values are considered as a sequence.
* [Step 8:] XOR last value and penultimate value in the array. The obtained result is considered as an initial value for the next sequence.
* [Step 9:] Repeat from step 2 to step 8 until seven different values are generated with seven different initial values.
* [Step 10:] All these sequences are stored in a memory array.  
The steps involved in generating a random key using the LFSR based key generation (Ref Figure 5 for flow chart representation) are given below.  
* [Step 1:] Get the color image of size \(256\times 256\) as illustrated in Figure 6. Divide the selected image into three layers red, green, and blue.
* [Step 2:] Select the red layer. Each pixel in the red layer is represented by eight bits.
* [Step 3:] Select the first sequence in the memory array. The elements in the sequence are considered as the bit locations of the eight-bit pixels.  
Figure 4: Flow chart for the linear feedback shift register (LFSR) random sequence generator.  
* Extract bits from first seven pixels of the red layer based on the bit locations in the first sequence and store the extracted bits in a buffer.
* Select the green layer and the next sequence in the memory array. Extract bits from first seven pixels of the green layer based on the bit locations in the second sequence and store the extracted bits in another buffer.
* Select the blue layer and the next sequence in the memory array. Extract bits from first seven pixels of the blue layer based on the bit locations in the third sequence and store the extracted bits in another buffer.
* Select the red layer and the next sequence again and repeat step 4 for the next seven pixels. Concatenate the obtained seven bits with the stored buffer.
* Similarly, repeat for the green and blue layers. If the previous sequence is the last sequence in the memory array, then the next sequence is the first sequence in the memory array.
* Repeat step 7 and step 9 until all the pixels are used for the bit extraction.
* This results in three buffers for the red, green and blue layers. Concatenate these three buffers and the result is considered as the generated key.  
Figure 5: Flow chart representation of the LFSR based key generation.
## 4 Machine Learning (ML) Algorithms
### Analysis
[127] suggested an extended regression neural network-based approach for estimating the cavitation severity. The study considered no cavitation, minimal cavitation, and developed cavitation. [PERSON] and [PERSON] [1] utilized the SVM technique to assess the suction obstruction and cavitation severity, using vibration data from the pump casing and bearing block. Hence, to obtain the most successful performance from the SVM classifier, kernel parameters were carefully selected in the work by [PERSON] et al. [36] to forecast cavitation and flow obstruction in the pump, while [PERSON] and [PERSON] [128] worked on the cover plate problem, discharge blockage, suction blockage, and impeller fault. Their algorithm identified the different rates of mechanical defects, flow disturbance, and the various combinations of these problems. Figure 16 summarizes the failure issues in studies and the common measures used, along with the typical features selected for analysis using the standard algorithms. According to Figure 14, the typical failure issues addressed by several research works include leakage, defective bearings, cavitation, misalignment, and seal breakage. The algorithms employed in most studies to address these failures are ANN. SVM, principal component analysis (PCA), the neuro-fuzzy hybrid model, fuzzy logic, rough sets, and Fourier transform. Furthermore, the study's standard parameters include vibration pressure analysis and acoustic emissions. Lastly, the common features considered in most studies are the vibration characteristics and the frequency time-domain characteristics. Understanding the properties of the data obtained and the potential cause-and-effect relationships between the qualities of the data is required before using certain algorithms in a study. In addition, it is important to comprehend the pump's nature in terms of its working circumstances, servicing frequency, system dynamics, and any other pertinent  
Figure 16: Common machine learning tools in pump studies. characteristics [67]. Depending on the application and setup, researchers typically have varying algorithm choices. Concerning this, [PERSON] et al. [67] elaborated on these standard algorithms, along with their applications, advantages, and disadvantages. The most commonly used algorithms in PHM studies have been summarized, along with each algorithm and its applications, benefits, and limitations. The selection and application of the algorithms depend on their suitability and adaptability to specific characteristics of the data in a study. For further details, the reader may refer to [PERSON] et al. [67]. However, the data usually utilized in various studies are categorized into two types. These are actual data, which are generally taken from real-world machinery. The simulated or generated data, on the other hand, is the second form of data, and it is intended to fulfill particular requirements, such as the validation of models in machine learning. Therefore, this study divides the datasets into two groups, based on the current research work. Table 8 depicts the data used in the various studies.
## Materials and Methods
### Volatile Compound Analysis  
The volatile compounds in fermented grain were analyzed using an Agilent 6890 GC equipped with a 5975B series mass spectrometric detector (Agilent Technologies, Palo Alto, CA, USA). A 50:30 mm DVB-CAR-PDMS SPME fiber (Supelco Co., Bellefonte, PA, USA) was used for headspace solid-phase microextraction (SPME). Briefly, 2 g of the fermented grain sample was added to 8 mL of Milli-Q water. After ultrasonic wave treatment for 30 min, the sample was centrifuged at 6500\(\times\)\(g\) and 4 \({}^{\circ}\)C for 10 min. The supernatant (8 mL) was transferred to a 20 mL vial containing 2 \(\upmu\)L of 4-methyl-2-pentanol (125.0 mg/L) as an internal standard and 3 g of sodium chloride. Volatile compounds were collected for 45 min at 50 \({}^{\circ}\)C, followed by GC-MS analysis [26]. Volatile compounds were identified by matching with the NIST 14 mass spectral database and were quantified with 4-methyl-2-pentanol as an internal standard.