esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Framework
### Machine Learning: Random Forest Regression Algorithm  
According to [PERSON], [PERSON] et al. [18], the random forest model stands out among machine learning models for predictive analytics applications and is useful in approaching databases in tabular forms with numerical and categorical variables. [PERSON], [PERSON] et al. [1] stated that there are advantages of the method in that it is nonparametric, easy to understand and quick to adjust, even in large problems, in addition to it not requiring deep statistical knowledge. It is an additive model that makes predictions by grouping decisions from a sequence of base models, and unlike linear models, it can interpret nonlinear behaviors between variables. The model can be written according to the following equation:  
\[g(x)=f_{0}(x)+f_{1}(x)+f_{2}(x)+\cdots\]  
where g is the last model, given by adding the \(f_{i}\) base models. The basic models are built by partitioning the data, turning it into independent decision trees.  
Figure 3: Schematic diagram of the CRISP-DM process [15].  
[PERSON] et al. [1] explained that the approach employs bootstrap sampling, which consists of sampling with replacement, to reduce the variance in predictors through predictions made by multiple models of decision trees trained with subsamples of the same database. The schematic diagram presented in Figure 4 indicates with red arrows the bootstrap sampling process, where the prediction is obtained by calculating the average for every corresponding split for B trees. The diagram also indicates that for each node, two thirds of the database is used for model training, and one third is used for testing, known as the out-of-bag sample (OOB).  
The authors [1] summarize the method as follows:  
1. For nodes b = 1 through B:  
* A size n \(\times\) p bootstrap sample is used for size N \(\times\) p training data, where N is the number of observations, p is the number of variables and n is a sample of N.
* A decision tree \(T_{b}\) is created based on the sampled data via the minimization criterion of \(\text{SSE}=\sum_{i=1}^{n}(y_{i}-\overline{y}_{R})^{2}\), where \(y_{i}\) represents the observed values and \(\overline{y}_{R}\) represents the mean fitted values. The sampled data is divided recursively according to the following steps: 1. For each node, select m variables (out of the total p variables) and split points; 2. Among the variables, select the best variable and its best division points; 3. Split each parent node between two child nodes.
2. B decision trees are developed via the procedure applied in step 1, as shown in \(\{T_{b}\}_{1}^{B}\).
3. For a given x, the prediction will be the average of the predictions made by the individual decision trees B.
## 4 Security Analysis
### Informal analysis  
#### 4.2.1 Mutual Authentication  
The proposed protocol can satisfy mutual authentication between both  
1. \(UAV\to GCS:\left\{M1,HMAC\right\}\): The GCS first verifies \(t1\) and HMAC values, then uses the public key of the UAV certificate to verify \(S1\), if successful, the UAV is successfully authenticated
2. \(GCS\to UAV:\left\{M2,HMAC,E(SK_{GU_{i}},MSG)\right\},\,t2\) is verified firstly, and then \(S2\) is verified with the public key of the GCS certificate, followed by multiple key, and HMAC value authentication. This method ensures the security of the two-way authentication phase of the protocol. Participants can authenticate each other by verifying that the received message is valid.
# Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition
## 2 Related Studies
[49] proposed the use of continuous wavelet transform (CWT) with prosodic features to recognize speech emotion. They used PCA for feature transformation with quadratic kernel SVM as a classification algorithm that achieved an average accuracy of 60.1% on the RAVDESS database. [PERSON] et al. [50] proposed a model for recognizing speech emotion using empirical mode decomposition (EMD) based on optimal features that included the reconstructed signal based on Mel frequency cepstral coefficient (SMFCC), energy cepstral coefficient (ECC), modulation frequency feature (MFF), modulation spectral (MS) and frequency weighted energy cepstral coefficient (FECC). They achieved an average accuracy of 91.16% on the Spanish database using the RNN algorithm for classification. Emotion recognition is still a great challenge because of several reasons as previously alluded in the introductory message. Further reasons include the existence of a gap between acoustic features and human emotions [31; 36] and the non-existence of a solid theoretical foundation relating the characteristics of voice to the emotions of a speaker [20]. These intrinsic challenges have led to the disagreement in the literature on which features are best for speech emotion recognition [20; 36]. The trend in the literature shows that a combination of heterogeneous acoustic features is promising for speech emotion recognition [20; 29; 30; 36; 39], but how to effectively unify the different features is highly challenging [21; 36]. The importance of selecting relevant features to improve the reliability of speech emotion recognition systems is strongly emphasized in the literature [3; 20; 29]. Researchers frequently apply specialized software such as OpenEar [31; 32; 35], OpenSmile [30; 32; 34; 36] and Praat [35] for extraction, selection and unification of speech features from heterogeneous sources to ease the intricacy inherent in the processes. Moreover, the review of numerous literature has revealed that different learning algorithms are trained and validated with the specific features extracted from public databases for speech emotion recognition. Table 1 shows the result of the comparative analysis of our method with the related methods in terms of the experimental database, type of recording, number of emotions, feature set, classification method and maximum percentage accuracy results obtained. It is conspicuous from the comparative analysis that our emotion recognition method is highly promising because it has achieved the highest average accuracy of 99.55% across two dissimilar public experimental databases.
# Predicting Human Location Using Correlated Movements
## 3 Preliminaries  
### Dataset  
In this work, we consider two different datasets. The first one is called the MIT Reality Mining dataset [12], which was collected during a period of nine months with the attendance of 106 subjects, including students and faculty members of MIT. Since subjects in the MIT dataset are involved in the same university, the social relationships between people exist with a high probability.  
The MIT dataset provides cell tower logs including the tower transition events and a set of base stations seen by the participants. In cellular networks, a mobile phone can be within the ranges of several cellular towers. However, the phone is only associated with the tower with the strongest signal. The events of tower transitions are recorded with cell tower ID and a timestamp. Due to the fact that small and short-range cells that cover distances of few hundred meters are more popular in metropolitan areas [13], the cell tower logs can be used to represent human locations [14; 10; 15; 16; 17; 18]. Hence, this work uses the dataset of cellular traces in order to evaluate the movement prediction model. The subjects in the dataset participated in the experiment in different time periods, and some subjects have no data or very little data [24]. Therefore, by considering overlapping periods and available data, 43 people with sufficient mobility data were selected. \(\mathbb{M}\) and \(m\) are defined as the set of chosen people and the cardinality of \(\mathbb{M}\), respectively.  
The second one, called Dartmouth dataset [19], provides the mobility traces extracted from logs of APs in the Dartmouth university campus. A log message including the timestamp, user ID, and AP ID was recorded when a mobile device connects or disconnects to the AP. Because of the short range of the Wi-Fi technology, human mobility can be represented as a sequence of connected APs [20; 21; 22]. For the Dartmouth dataset, a 4-month period from 3 January to 30 April 2004 was considered since the during this period the academic campus was relatively consistent [20; 40]. Similar to the MIT dataset, persons whose mobility data was provided less than 75% over the experiment period was filtered out. Then, the Dartmouth dataset includes 162 mobile users.
# Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining
###### Abstract  
When it comes to association rule mining, all frequent itemsets are first found, and then the confidence level of association rules is calculated through the support degree of frequent itemsets. As all non-empty subsets in frequent itemsets are still frequent itemsets, all frequent itemsets can be acquired only by finding all maximal frequent itemsets (MFIs), whose supersets are not frequent itemsets. In this study, an algorithm, named _right-hand side expanding_ (RHSE), which can accurately find all MFIs, was proposed. First, an Expanding Operation was designed, which, starting from any given frequent itemset, could add items using certain rules and form some supersets of given frequent itemsets. In addition, these supersets were all MFIs. Next, this operator was used to add items by taking all frequent 1-itemsets as the starting point alternately, and all MFIs were found in the end. Due to the special design of the Expanding Operation, each MFI could be found. Moreover, the path found was unique, which avoided the algorithm redundancy in temporal and spatial complexity. This algorithm, which has a high operating rate, is applicable to the big data of high-dimensional mass transactions as it is capable of avoiding the computing redundancy and finding all MFIs. In the end, a detailed experimental report on 10 open standard transaction sets was given in this study, including the big data calculation results of million-class transactions.  
association rule; frequent itemset mining; big data; maximal frequent itemsets
# Research on Three-Phase Wireless Power Transfer System
###### Abstract  
Aiming at the problems of low power, low energy transmission efficiency, and high stress in the circuit of a single-phase wireless power transfer system, this paper proposes a wireless power transfer (WPT) system with a three-phase angle difference of 120 degrees and establishes a COMSOL multi-physics simulation model for analysis. In this simulation model, the topology of the three-phase resonant compensation network is studied in detail, and the structure of the coupling coil is designed and adjusted. Compared with the single-phase system with the same environmental conditions, air gap, and operating frequency, the simulation results show that the proposed three-phase system can effectively reduce the magnetic flux leakage, reduce the stress in the circuit, and significantly improve the energy transmission efficiency. In order to verify the reliability of the simulation results, an experimental platform was built. The experimental results show that the efficiency and coupling degree of the new system are significantly improved at the resonant frequency of 47.5 kHz, and the stress in the circuit is also significantly reduced.  
overlapping winding; coupling mechanism; wireless power transfer; fan coil +
Footnote †: journal: Applied
# Building Reliable Massive Capacity SSDs through a Flash Aware RAID-Like Protection +
###### Abstract  
The demand for mass storage devices has become an inevitable consequence of the explosive increase in data volume. The three-dimensional (3D) vertical NAND (V-NAND) and quad-level cell (QLC) technologies rapidly accelerate the capacity increase of flash memory based storage system, such as SSDs (Solid State Drives). Massive capacity SSDs adopt dozens or hundreds of flash memory chips in order to implement large capacity storage. However, employing such a large number of flash chips increases the error rate in SSDs. A RAID-like technique inside an SSD has been used in a variety of commercial products, along with various studies, in order to protect user data. With the advent of new types of massive storage devices, studies on the design of RAID-like protection techniques for such huge capacity SSDs are important and essential. In this paper, we propose a massive SSD-Aware Parity Logging (mSAPL) scheme that protects against \(n\)-failures at the same time in a stripe, where \(n\) is protection strength that is specified by the user. The proposed technique allows for us to choose the strength of protection for user data. We implemented mSAPL on a trace-based simulator and evaluated it with real-world 1/O workload traces. In addition, we quantitatively analyze the error rates of a flash based SSD for different RAID-like configurations with analytic models. We show that mSAPL outperforms the state-of-the-art RAID-like technique in the performance and reliability.  
NAND flash memory; SSDs (Solid State Drives); reliability; raw bit error rate Article
## 3 Experimental Verification of Chatter Suppression Effect with TMDs  
Based on the chatter vibration modes discussed in Section 2, the influence of the mounting arrangement and tuning parameters of the TMDs on the chatter suppression during the turning process of a thin-walled cylinder was experimentally investigated. First, the specifications of the TMDs and the mounting method on the workpiece are discussed. Thereafter, the chatter suppression achieved by the TMD mounting arrangement in the circumferential and axial directions of the workpiece was examined for the chatter in a single vibration mode. In addition, the number and arrangement of the lightweight TMDs required to enhance the chatter suppression effect even with extremely lightweight TMDs were investigated under the condition of chatter generated by multiple vibration modes.
## 4 Discussion
### Limitations  
In our survey, we exclusively gathered feedback from students and instructors within the medical faculty at Heidelberg University. This makes our analysis a single-center study, limiting the direct applicability of our results to other universities. Nevertheless, we believe that an AR course would also be valuable at other institutions. Through academic exchange with colleagues from other universities, whether during lectures or conferences, we have observed a significant demand for innovative teaching and learning methods, particularly in the field of (cardiovascular) education, and the need for scientific evaluation of potential learning benefits. Key elements of our study (e.g., modular curriculum, AR-ready infrastructure, and the need for anatomy education enhancement) can be found in other institutions too. This is why we think that the approach is transferable.  
Moreover, the data collection period spanned nearly a year (September 2023 until July 2024), which may introduce temporal biases. However, the curriculum and exposure to AR/VR remained stable during the data collection window.  
Participation was anonymous, uncompensated, and without incentives, in an effort to minimize response bias. Nonetheless, due to the voluntary nature of the survey, the potential for self-selection bias cannot be fully excluded and should be considered when interpreting the findings.
# Elevation Angle Characterization for LEO Satellites: First and Second Order Statistics
## 3 Methodology for the Elevation Angle Characterization
### Orbit Coverage
\begin{bmatrix}b_{1,1}&b_{1,2}&b_{1,3}& \dots&b_{1,14}\\ b_{2,1}&b_{2,2}&b_{2,3}&\ddots&b_{2,14}\\ b_{3,1}&b_{3,2}&b_{3,3}&\ddots&\vdots\\ b_{4,1}&b_{4,2}&b_{4,3}&\ddots&\vdots\\ b_{5,1}&b_{5,2}&b_{5,3}&\ddots&\vdots\\ NaN&b_{6,2}&b_{6,3}&\ddots&\vdots\\ NaN&NaN&b_{7,3}&\ddots&\vdots\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ NaN&NaN&NaN&\dots&b_{18,14}\end{bmatrix} , \tag{17}\]  
where \(NaN\)'s are placed for the case \(\phi_{ES}>i_{SAT}\), and \(h\in h_{s}\) corresponds to the simulation altitudes set defined in Table 2 as  
\[h_{s}=\{1000\text{ km},1200\text{ km},\dots,2000\text{ km}\}. \tag{18}\]  
Tables A1-A6 in Appendix B, show the shape and scale parameters obtained from simulations with configurations shown in Tables 1-3. From those values, matrices \(A_{h}\) and \(B_{h}\) can be constructed, and linear interpolation can be applied to those in order to obtain shape and scale parameters for different orbit inclinations, ES's latitudes, and altitudes that were not simulated, but are in the range of \(h_{s}\), \(i_{s}\), \(\phi_{s}\).
# Marine Internet for Internetworking in Oceans: A Tutorial
## 3 Environments for Communication in Oceans
### Summary  
Table 2 summarizes communication conditions available in oceans, and shows that many resources can be exploited for networking. In general, networks along the coastline or on islands can provide reliable performance with stable energy sources by using wired communication systems, electricity grid and generators. Typical stable resources include the BS/AP, power tower and observatories installed along coastlines or in islands and optical fibers. However, it is very expensive to deploy this kind of infrastructure densely at sea. Relatively, it is more feasible to deploy a large number of vessels, boats, buoys and floating platforms over water surface in oceans. However, they adopt wireless communication and ad hoc networks, which cannot guarantee communication quality and network connectivity. For vessels and boats, energy consumption for communication and networking usually takes very small portion of their overall energy to be consumed. Satellites and HAPs in sky/space can provide good performance, while their transmission speeds are smaller than those of wired communication on average. It is also expensive and difficult to densely deploy them, while the major energy sources are battery and solar energy. In underwater environments, the majority of networks are based on acoustic wireless communication and battery, which limits network sizes  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Achievable Transmission Rates versus Distances**} & \multicolumn{2}{c}{**Velocity in**} \\ \cline{2-7}  & **50 m** & **1 km** & **2 km** & **10 km** & **100 km** & **Water (km/s)** \\ \hline Acoustic waves (kbps) & 300 & 30 1  & 0.14–15.36 [33] & 0.3–0.5 2  & 1.5 (seawater) \\ \hline Blue/green laser (Mbps) & 10 & & & & 225,600 3  \\ \hline VLF/ELF 4 (bps) & 300 & 3 & & & 33,333 [34] \\ \hline \hline \end{tabular}  
* Using MIMO and space time code, 2 200 km coverage in deep water, 3 LED velocity measured in seawater is 217,100 [35]. 4 VLF (3–30 kHz) and ELF (3 kHz).  
\end{table}
Table 1: Characteristics of typical underwater communication media [20].  
Figure 3: Diagram of wave propagation with atmospheric duct.  
and capacity. Costly acoustic communication devices and their deployment underwater make dense network node distribution very difficult [4]. The availability of many resources mentioned above (e.g., wireless channel capacities, network connectivity and energy sources) is not stable, which requires the networking architecture and protocols able to adapt themselves to such dynamics.
## 4 Results and Discussion  
As described in the previous section, the results obtained in the real scenario will be discussed in this chapter. In the tests, the main metrics used were RSSI and SNR, considering the different points, sides and SF. Furthermore, the tests were performed considering the LoRaWAN and LoRaMESH protocols at different geographical positions within the city. At each position, 10 data were recorded for each SF configuration (SF7-SF12), different distances (between 200 m to 2.35 km) and sides (left, right and front). In total, 780 packets were recorded in the test with LoRaWAN and 210 packets with LoRaMESH. The following tests were performed for LoRaWAN and LoRaMESH protocols in the proposed scenario.  
Figure 25: Endode positions in the actual scenario in LoRaWAN implementation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Point** & **Distance from Gateway** & **SF Analyzed** & **Position** \\ \hline A & 500 m & All & left \\ B & 800 m & All & left \\ C & 1 km & All & left \\ D & 1.5 km & 10, 11 e 12 & left \\ E & 2 km & 10, 11 e 12 & left \\ F & 2.35 km & 10, 11 e 12 & left \\ A & 500 m & All & front \\ B & 732 m & All & front \\ C & 1 km & All & front \\ D & 1.6 km & 10, 11 e 12 & front \\ E & 2 km & 10, 11 e 12 & front \\ A & 800 m & All & right \\ B & 1 km & All & right \\ C & 1.35 km & All & right \\ D & 1.5 km & All & right \\ E & 2 km & 10, 11 e 12 & right \\ \hline \hline \end{tabular}
\end{table}
Table 5: LoRaMESH Real Scenario Point Specifications.  
1 Received Signal Strength Indication (RSSI)
2 Signal-to-Noise Ratio (SNR)
3 LoRaWAN or LoRaMESH restricted network reach
4 LoRaWAN or LoRaMESH restricted network capacity
# Tailored Nanoparticles as Vaccine Components
## 3 Types and Uses of Nanoparticles in Vaccines
### VLPs and Self-Assembled Protein Cages  
Virus-like particles (VLPs) are capsid-like NPs (20-200 nm) made up of viral proteins that can self-assemble into regular structures. They lack a viral genome and thus cannot replicate [4; 10; 13; 32; 35; 60; 61]. When they are produced in eukaryotic cells (e.g., mammalian or insect cell lines, plant [62; 63], or yeast cells), VLPs can have an envelope membrane through which the antigens protrude as transmembrane proteins, making them--from the outside--similar to liposomes. Because of their highly repetitive structure, VLPs sometimes generate powerful immune responses even without adjuvant [26; 64]. The VLP vaccine for HBV (Engerix) was the first virus-like particle vaccine to reach the wider market, followed by prophylactic VLP vaccines for the human papillomavirus (Cervarix) and malaria (Mosquitix) [9; 65; 66]; influenza VLP vaccines are in clinical trials [67].  
The surfaces of VLPs, or the extra-particular parts of their transmembrane proteins, can be modified to attach other proteins for targeting or for adjuvant functions. This is achieved, for example, by genetic engineering [68], the Spy/SpyCatcher reaction [69], or by chemical crosslinking [70]. One such approach uses genetic fusions of the transmembrane and cytosolic portions of the vesicular stomatitis virus G protein (which makes the VLPs) with variant-specific surface proteins from protozoa (e.g., _Giardia_), which act as very strong mucosa-specific adjuvants [60].  
Self-assembling cage-like protein particles similar to VLPs were also designed de novo. In one study, a trimeric aldolase was engineered with RosettaDesign to fit an icosahedral symmetry with novel interfaces between the trimers optimized for close packing without steric clashes [36]. This self-assembling cage, produced in _E. coli_, was then functionalized with the recombinant receptor-binding domain of the SARS-CoV-2 Spike protein by the Spy/SpyCatcher reaction for eventual use as a VLP vaccine, using additional adjuvant [71; 72].  
On the small side of self-assembling protein complexes, ferritin is an icositetramer (24-mer) with an almost spherical structure of 10 nm diameter. In the development of an influenza vaccine, a fusion protein of ferritin and hemagglutinin (HA) from the virus is therefore spontaneously assembled into an octahedral particle with eight trimeric HA spikes. This yielded a stronger immune response than the trivalent inactivated influenza vaccine by itself [35].