esa-sceva/satcom-chunk-collection huggingface dataset:
# A Sensitivity Study of POD Using Dual-Frequency GPS for CubeSats Data Limitation and Resources
## 3 Orbit Determination under Different Scenarios  
As a start point of our analysis, let us look at the results of code-based processing and see the impact of using a reduced-dynamic model. Figure 3 shows the 3D positional errors of the kinematic orbits (red) based on the code observations on P1 and P2 forming the IF combination (Step 1 in Section 2), and when computing the same data with the reduced-dynamic orbits (blue, Step 2 in Section 2) for GRACE FO-1 for the tested 1 day of data. As complete dual-frequency CubeSats data for a long period needed in our analysis are currently not available, the data from a typical LEO satellite that may share similar orbit conditions with CubeSats, in this study GRACE FO-1, were used for simulation of the best-case scenario of the CubeSat processing [33]. For the tested LEO satellite, the dual-frequency GPS data are tracked in a complete and continuous manner with an observation sampling interval of 10 s. A duty-cycle of 100% and a mean satellite number of about 9 above the elevation mask of 5 degrees were available for the processing of a daily arc using the IGS final products. The 3D RMSE of the code-based kinematic (Step 1 in Section 2) and reduced-dynamic orbits (Step 1 in Section 2) amount to about 1.6 m and 0.2 m, respectively, which clearly shows the value of employing strong dynamic models. When reducing the data availability and continuity, the accuracy of the reduced-dynamic orbit could degrade to the sub-meter level.  
For high-accuracy POD, as discussed in Section 2, the code-based orbits are only used as the a priori orbits for the phase processing. Therefore, in the rest of the sections, the sensitivity analysis will focus on the final phase-based reduced-dynamic orbits.
## 4 Performance Evaluation
### Experimental Results
#### 4.3.5 Summary and Discussion
The experimental results show that benchmark programs run 10.7% faster on the modified Linux kernel with the proposed task selection policy than on the default Linux kernel. Under the same CPU frequency, the shorter the benchmark programs run, the less energy is consumed. The degree of energy-saving needs further experiments.
# Device-Free Indoor Location Estimation System Using Commodity Wireless LANs
## 2 Channel Sounding Using IEEE 802.11n Wireless LANs
### Evaluation  
The channel impulse response test was conducted for system validation purposes. An unmatched T-junction (BL41-6203-00, Orient Microwave Corp.) [25] was used for the measurements. Figure 7a shows the measurement configuration. Transmitter antenna port 1 (Tx1) was connected to a power splitter. Port 1 of the splitter was connected to receiver antenna port 1 (Rx1), which was used for the reference measurement. Port 2 of the splitter was connected to port A of the T-junction, and the signal from port B was input to receive antenna port 2 (Rx2). Here, port C was connected to a 6 m cable that left the end opened, which generated reflected waves at the cable end. The attenuation per 1 m of the cable used (Enviroflex_316, Huber Shuner, Corp.) [26] was approximately 1.62 dB/m. The measurement results are shown in Figure 7b. Only direct waves were observed in Tx1-Rx1, and the first reflected wave from the cable end could also be observed in Tx1-DUT-Rx2, which was in good agreement with the calculated value. Here, the calculation was done considering the propagation speed of electromagnetic waves in coaxial cables to be about 77 % of that in a vacuum and the path length and attenuation rate of the cable (delay time: \(\frac{12}{3\times 10^{8}\times 0.77}=52\) ns, power attenuation: \(12\times 1.65=20\) dB).  
To evaluate the inter-channel phase stability, the Tx1 signal was distributed to Rx1, Rx2, and Rx3 using a splitter. After B2B calibration, two-hundred ten channel transfer functions were obtained. The variation of \(\angle\hat{H}_{2,1}\) and \(\angle\hat{H}_{3,1}\) based on the reference value of \(\hat{H}_{1,1}\) is shown in Figure 8. The left figure shows the relative phase difference of 217 sub-carriers for 210 time samples, and the right figure shows the phase difference of all sub-carriers acquired for 210 time samples. It can be seen that the phase variation was less than approximately 20 degrees.  
Figure 6: B2B calibration. (**a**) Calibration procedure. (**b**) Calibration result.
# Evaluating MPTCP Congestion Control Algorithms: Implications for Streaming in Open Internet
### Multipath Data Transfer  
The concept of multipath transmission is not new; however, its practical implementation has been fraught with challenges. The idea of utilizing multiple transmission channels has been proposed as an answer to the restrictions of traditional IP-based systems for devices equipped with multiple physical interfaces [9; 10]. Early attempts to realize this idea often faltered due to the complexities involved in managing those interfaces and ensuring a consistent QoS [2; 7]. However, the situation changed with the introduction of MPTCP, a promising extension of the traditional TCP protocol designed specifically for multi-interface traffic [5; 11]. MPTCP offers a more flexible and robust approach to data transmission, allowing for the simultaneous use of multiple, diverse network paths. This multiplicity not only enhances the reliability of data transmission but also provides an opportunity for load balancing, thereby optimizing the use of network resources [12].  
One of the most welcome aspects of MPTCP is its openness to innovation. The reference implementation of the protocol addresses only the general aspects of its behavior, leaving room for potential advancements in its implementation [13]. This flexibility is particularly relevant when it comes to the choice of CC algorithms, which play a pivotal role in determining the efficiency and fairness of data transmission. However, the design of MPTCP and its associated CC algorithms has primarily focused on boosting efficiency without compromising fairness [5; 6]. This line of reasoning often left other aspects, such as meeting delay constraints, vital for streaming applications and tactile internet [7; 14], unfulfilled.  
This paper aims to fill the existing gap in the research on the effective use of MPTCP for streaming services. Specifically, we investigate the suitability of various CC algorithms for this purpose through experiments in the open Internet. One should note that streaming applications have unique requirements that set them apart from other types of data transmission. Even though they often employ adaptive data compression methods, their primary concern is not necessarily a high bandwidth, but rather low latency, low error rate, and negligible jitter [2; 7]. In the context of multipath transmission, due to the need for stream synchronization at the receiver, meeting these unique requirements is not straightforward. The traditional MPTCP CC algorithms, designed primarily to enhance efficiency and fairness, perform below expectations when it comes to managing the delay and its fluctuation [6; 15; 16; 17]. The current works in the MPTCP context address the streaming performance from the perspective of schedulers, thus emphasizing the allocation of resources rather than the behavior of CC algorithms [12; 18]. Taking into account the profound impact the CC algorithms may exert on the QoS experienced by end-users [19; 20], it is a significant oversight this paper aims to mitigate.
## 3 Proposed System
### Detailed Operation of System
#### 3.4.2 Key generation (\(mk,w,I_{u}\))  
Performed with the attribute group, as well as the delegator identifier, \(I_{u}\), as follows:  
1. Base Component of the Private Key Create an element for the private key through Equation (2). \[d_{0}=g^{a-u_{id}}(u_{id}\in\ RZ_{p}^{*})\] (2)
2. Attribute Component of the Private Key Select attributes \(a_{j}\in w\), \(u_{j}\in\ RZ_{p}^{*}\) and calculate \(d_{j,1}=g^{u_{j}t_{j}^{-1}}\) and \(d_{j,2}=g^{(u_{j}-u_{j})t_{j}^{-1}}\).  
Send the first private key share, \(sk_{w\mu_{n},1}=(\forall a_{j}\in w:d_{j,1})\), to the proxy, and send the second private key share, \(sk_{w\mu_{n},2}=(d_{0},\forall a_{j}\in w:d_{j,2})\), to the delegator.  
Figure 7: The concept of CP-ABTD.
###### Abstract  
This study aims to analyze and compare landslide susceptibility at Woomyeon Mountain, South Korea, based on the random forest (RF) model and the boosted regression tree (BRT) model. Through the construction of a landslide inventory map, 140 landslide locations were found. Among these, 42 (30%) were reserved to validate the model after 98 (70%) had been selected at random for model training. Fourteen landslide explanatory variables related to topography, hydrology, and forestry factors were considered and selected, based on the results of information gain for the modeling. The results were evaluated and compared using the receiver operating characteristic curve and statistical indices. The analysis showed that the RF model was better than the BRT model. The RF model yielded higher specificity, overall accuracy, and kappa index than the BRT model. In addition, the RF model, with a prediction rate of 0.865, performed slightly better than the BRT model, which had a prediction rate of 0.851. These results indicate that the landslide susceptibility maps (LSMs) produced in this study had good performance for predicting the spatial landslide distribution in the study area. These LSMs could be helpful for establishing mitigation strategies and for land use planning.  
Keywords:landslide susceptibility; random forest; boosted regression tree; information gain; landslide susceptibility map +
Footnote †: journal: Applied Sciences
## 3 Proposed Approach and Methodology
### System Architecture
#### 3.1.3 Data Integration Platform  
The protocol is designed for a platform that collects, processes, and integrates real-time data from VLC-enabled devices, including vehicle positions, speeds, and pedestrian requests. The communication protocol defines the structure and rules governing the exchange of information, outlining the synchronization, identification, and payload portions of the transmitted frame. The communication protocol is detailed in Table 1.  
The frame structure begins with a 5-bit synchronization block (Sync) with the pattern [10101], used to synchronize the receivers and mark the start of a new frame (SOF). Following this, the TIME block encodes the current time with a 12-bit sequence (\(6+6+6\)), representing the hour, minute, and second. A flag with the pattern [1 1 1] indicates that specific ID blocks will follow. Each ID block consists of 4 bits, starting with the communication type (COM) that specifies the communication between streetlights (L), vehicles (V), pedestrians (P), and infrastructure (I). The next block provides the localization of transmitters, defined by x and y coordinates. Depending on the communication type, additional details include the occupied lane (Lane 0-7), requested traffic light signals (TL 0-15), the number of vehicles behind the leader (Veic. nr), the ID assigned by the Intersection Manager (IM) to acknowledge vehicle messages, the cardinal direction (Direct. ), and the active phase (Phase), provided by the infrastructure in a \"request\" or \"response\" message. For traffic-related messages, the frame includes vehicle information, such as coordinates, the position of vehicles behind the leader (CarIDx, CarIDy), and traffic-related data (payload), such as road conditions, average waiting times, and weather information. The frame ends with a 4-bit End of Frame (EoF) block, represented by the pattern [0000], signaling the conclusion of the frame. In Figure 7, the moments of communication for both vehicles are illustrated. A highlighted car is coming from the north, heading towards C0, and it is waiting for its phase to be activated to turn left. Various VLC communications, V2V, V2I, and I2V, are being
## 2 Materials and Methods
### Metagenetic Analysis  
A metagenomic analysis of bacterial and archaeal populations was performed by sequencing the hypervariable region V3-V4 of the 165 rRNA gene. A metagenomic analysis of the fungal population was performed by sequencing the hypervariable region ITS1.  
The specific 341F and 785R (16S), and ITS1 FI2 and 5.8S (ITS1) primer sequences were used to amplify the selected region and prepare the library. A PCR reaction was performed with Q5 Hot Start High-Fidelity 2X Master Mix; the reaction conditions were in line with the manufacturer's recommendation. Sequencing was performed on a MiSeq (Illumina, San Diego, CA, USA) using paired-end (PE) technology. An automatic data analysis was carried out on the MiSeq using MiSeq Reporter (MSR) v2.6 software (Illumina, San Diego, CA, USA); it included the following steps: (1) demultiplexing of samples and (2) generating fastq files containing raw reads.  
A bioinformatic analysis allowing the reads to be assigned to the species level was performed using QIIME software ([[http://qiline.org](http://qiline.org)]([http://qiline.org](http://qiline.org)), accessed on 20 October 2021) and SILVA 138 SSU (16S reference sequences database) ([[https://www.arb-silva.de](https://www.arb-silva.de)]([https://www.arb-silva.de](https://www.arb-silva.de)), accessed on 20 October 2021) or UNITE ver. 8.2 (ITS reference sequences database) ([[http://unite.ut.ee](http://unite.ut.ee)]([http://unite.ut.ee](http://unite.ut.ee)), accessed on 20 October 2021). This analysis consisted of the following steps:  
(1) adapter sequences removal--cutadapt, (2) quality reads analysis and low-quality sequence removal (quality! 20, minimum length 30)--cutadapt, (3) merging paired reads--fastq-join algorithm, (4) clustering based on the reference sequences database--uclust algorithm, (5) filtering out chimeric sequences--userach61 algorithm, and (6) taxonomy assignment with the selected reference database--uclust algorithm.  
The diagrams were plotted using the ggplot2 package ([[https://ggplot2.tidyverse.org](https://ggplot2.tidyverse.org)]([https://ggplot2.tidyverse.org](https://ggplot2.tidyverse.org)), accessed on 31 January 2022) [20].
# A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds
## 2 Related Work
### Self-Supervised Learning  
Self-supervised learning [44] is a type of unsupervised learning where the labels used for learning can be generated from the data themselves. It is usually pre-trained on a large dataset using a pretext task such as a reconstruction or puzzle, then fine-tuned to generalize its learned representations to other datasets for various tasks. It does not require labels during pre-training, which alleviates the high demand for manually labeled data [20].  
Self-Supervised Learning for Point CloudsSelf-supervised learning has also been extensively studied for point cloud networks. Most of them adopt the idea of mask reconstruction [19, 20, 45, 46]. OCo [45] attempts to recover the original point cloud from the occluded point cloud in camera views. As a BERT-style self-supervised framework, Point-BERT [19] employs a two-stage pre-training process where it predicts latent representations learned by an offline tokenizer. However, it is important to note that directly recovering raw point clouds can potentially result in information leakage due to the positional encoding of masked tokens. To alleviate the above problem, Point-MAE [20], which adopts the MAE [23] idea, was proposed to only input unmasked tokens into the encoder and add the masked tokens to the decoder for reconstructing.  
Information Theory in Self-Supervised LearningSelf-supervised learning models demonstrate that the intrinsic representations derived from pre-training exhibit strong generalization capabilities across various downstream tasks, yielding favorable performance outcomes. However, their internal workings remain opaque. In recent years, scholars have made attempts at interpretability [47]. From the perspective of information theory, [PERSON] et al. explain modern deep networks using the principles of data compression and discriminant representation [24, 25, 26], and also offer a fresh perspective to explain the quality of learning in the self-supervised learning framework and theoretically guide the optimization direction of self-supervised learning.  
While these methods showcase strong performance in point cloud processing, they often lack ample sensitivity to the intrinsic features embedded in rotated point clouds due to insufficient consideration of rotation equivariance. This paper introduces a network framework leveraging a masked autoencoder to effectively capture the intrinsic representations of point clouds through self-supervised learning, without relying on labeled data. Additionally, by vectorizing the network, it acquires rotation equivariance, empowering it to handle more complex rotations of point clouds. Furthermore, the adoption of pre-training and fine-tuning strategies further broadens the network's applicability, enabling it to tackle various tasks such as classification, segmentation, and pose change estimation.
## 2 Background and Related Work
### Background
#### 2.1.3 Common Vulnerability Scoring System  
The CVSS [11] is a framework for evaluating and quantifying the vulnerabilities' impact. The CVSS score is expressed on a scale of 0 to 10, allowing us to rate the severity of a given vulnerability to prioritize a response. Currently, CVSS v3.1 classifies various evaluation factors into three groups (the base metric, the temporal metric, and the environmental metric) to evaluate vulnerabilities.  
The base metric consists of the access vector, access complexity, privileges required, user interaction, scope, and impact. The access vector represents the path (the network, adjacent network, and local, physical) an attacker uses to gain access to exploit a vulnerability. The attack complexity indicates how complex an attacker should go through to exploit a vulnerability and is rated as 'low' or 'high'. The privileges required indicate what level of privilege an attacker should have to exploit the vulnerability and evaluates to 'none', 'low', and 'high'. The user interaction indicates whether an attacker needs to interact with the user to exploit the vulnerability and evaluates it to 'none' and'require'. The scope indicates the scope the vulnerability affects and is evaluated as 'unchanged' or 'changed'. The impact is divided into confidentiality, integrity, and availability, evaluated as 'none', 'low', and 'high', respectively. The temporal metrics include exploit code maturity, remediation level, and report confidence. The exploit code maturity indicates how mature the exploit code for a vulnerability is, rated as 'not defined', 'unproven that exploit exists', 'proof of concept code', 'functional exploit exists', and 'high'. The remediation level represents the organization's response to vulnerabilities, evaluated as 'not defined', 'official fix', 'temporary fix', 'workaround', and 'unavailable'. The report confidence indicates the reliability of reporting on vulnerabilities and is evaluated as 'not defined unknown','reasonable', and 'confirmed'. The environmental metric is a category of the base metric and consists of confidentiality, integrity, and availability requirements. The confidentiality requirement represents the required level of confidentiality/integrity/availability of the system or data affected by the vulnerability and is evaluated as 'not defined', 'low','medium', and 'high'. The CVSS score is calculated using the base metric, and the temporary and environmental metrics are used to provide additional information.
# Deep Reinforcement Learning for UAV-Based SDWSN Data Collection
## 5 Simulation Outcomes
\begin{table}
\begin{tabular}{c c} \hline \hline Learning rate \(\alpha\) & 0.1 \\ Discount factor \(\gamma\) & 0.9 \\ Number of episodes & 300 \\ Exploration rate \(\epsilon\) & 1 \\ Rewards & +10, \(-1\) \\ Bandwidth of channel (MHz) & 20 \\ Packet size (bytes) & 512 \\ Transmission range (m) & 100 \\ UAV velocity (m/s) & (5–40) \\ Number of nodes & 100 \\ Time step size \(\delta\) & 0.5 \\ Altitude of the UAV (m) & 100 \\ C1 Constant & \(9.26\times 10^{-4}\) \\ C2 Constant & 2250 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters for the proposed UAV path design model. Figure 5: UAV energy consumption and communication bit rate performances over multiple episodes. The reward function is determined for each epoch as follows: A reward is assigned based on the UAV's connectivity throughput to the gateway, which correlates with its distance to the gateways. The closer the UAV is to the gateway, the better the throughput and the higher the reward. Conversely, moving away from the gateway results in a penalty or negative reward. The same reward function is utilized to assess the UAV's energy consumption, thereby determining the proposed cost function for each epoch accordingly. As shown in the upper part of Figure 5, energy consumption decreases as the number of episodes increases, while the bit rate declines with the rise in episodes. This indicates that as the number of episodes increases, the likelihood of finding the optimal path improves, although it may also lead to greater complexity and longer runtime for the simulation. Also, with more episodes, the agent has more chances to explore the environment and learn from its actions, which enhances its decision-making over time. Furthermore, the agent can more effectively balance exploring new actions and exploiting known ones, leading to better overall performance. As a result, the proposed cost function in Equation (3) improves with each episode, leading to lower energy consumption and higher bit rates. The proposed method is compared with the methods in [14; 26], including the heuristic fuzzy algorithm without RL and the SCA UAV path approximation model. The results show better performance in the success rate of served sensor nodes with the RL algorithm. This comparison uses various network scalability metrics, keeping other parameters consistent. According to Figure 6, as the number of dispersed nodes increases, the proposed RL-based method shows a significantly larger gap between the SCA and heuristic algorithms. Additionally, the complexity of the proposed communication network is much lower than that of the SCA algorithm [26], which uses complex optimization algorithms for UAV trajectory planning.
# A Survey on Mission-Profile-Based Stress Testing of Electric Motor Drives
## 2 Influential Factors on the Aging and Reliability of EMD Components
### Electromagnetic Stress
#### 2.1.4 Voltage-Induced Stress  
Voltage as a stress factor can cause various negative effects on systems and components, including the following:  
* Overvoltage-induced dielectric degradation;
* Thermal degradation;
* Degradation of semiconductor components;
* Increased total harmonic distortion in voltage (THDU);
* EMI influence.  
Similar to the effect of an electric field as a stress factor, voltage induces stress in insulation materials by breaking chemical bonds within their structure [19]. According to [37], the electric motors driven by PWM inverters are susceptible to winding insulation deterioration as a result of overvoltage effects. High changes in voltage with respect to time generate excessive current impulses that stress materials and accelerate degradation.  
When a high voltage is applied to a semiconductor component, it increases power losses, which in turn generate excessive heat. This heat degrades the semiconductor material, reducing its reliability and lifespan [38]. Additionally, thermal cycling caused by rapid voltage changes can result in mechanical stress and material fatigue, further accelerating degradation.  
VSIs are very sensitive to sudden voltage surges. The paper [39] addresses the issue of the sensitivity of frequency converters to voltage sags. A reduction in the DC link voltage affects the output voltage of the converter, which no longer matches the nominal voltage of the electric motor. As stated in [40], this results in higher thermal and electrical stress on the machine windings and semiconductor devices.