esa-sceva/satcom-chunk-collection huggingface dataset:
# Anomaly Detection of Metallurgical Energy Data Based on iForest-AE
## 2 Related Work
### Anomaly Detection Process Based on the iForest-AE Method
#### 2.3.2 Data Detection  
In the detection phase, the test data are fed into the autoencoder model and the reconstruction error of these test data is calculated. Additionally, the test data whose reconstruction error is larger than the threshold are determined as an anomaly.  
In the anomaly detection based on the autoencoder, the selection of the threshold is extremely critical. We selected several values as thresholds, and used the F1 score to assess the effect of anomaly detection. The threshold that corresponds to the maximum F1 score was selected as the final threshold.
## 3 Wireless Communication Networks
The multiuser channel is divided in two types, the first is the broadcast channel where a single WD transmits signals to many WD receivers e.g., the relationship between the base station (BS) and user equipment (UEs) in cellular networks. Secondary, the multiple access channel uses schemes that distribute the resources and permit the transmission among many transmitters with one receiver [30; 31]. The access to the multiuser channel employs schemes to allocate users with conditions to communicate. The schemes based on random allocation is known as random multiple access (RMA) scheme that is widely used e.g., in satellite networks and large wireless networks without signaling overheads [32]. On the other hand, the schemes based on multiple access allocation assign dedicate channels to users using orthogonal or semi-orthogonal division of resources [31; 33]. This way, some techniques based on multiple access schemes have been proposed to provide uniformly the resources considering the temporal, frequency and spatial parameters or combinations to separate users [34; 35]. The presence of multiple WDs in a given area is widely studied because it can change the transmission considerations in the wireless channel. Multiple access schemes are useful techniques that require previous agreements. This way, the schemes can be described in temporal, spectral, spatial or combinations. * _Temporal_. This scheme establishes transmission periods which can be used orderly or opportunistically. For example, time division multiplexing (TDM) techniques are orderly schemes due to the fact that time is divided in intervals with the same length that are assigned to each active user. Cognitive Radio technologies is an opportunistically scheme due to the fact that the channel is used by secondary users only when it is possible without disturbing the primary user transmission. * _Spectral_. This scheme consists of assigning sub-bands to each communication link. They can be assigned one-per-user or flock-per-user during a communication session, i.e., single-carrier (SC) or multi-carrier (MC) basis systems. Frequency division multiplexing (FDM) and multi-carrier technologies are some examples of this kind of scheme. * _Spacial_. This scheme consists of spatially separating the resources considering their coverage area forming clusters, cells, or sectors coverage to allow their reuse. These schemes are widely used in satellite communications and wireless mobile telephony systems.
## 4 Existing Jamming Attack Detection Methods and Their Analysis
### Method Suggested by [PERSON] et al. [2]  
[PERSON] _et al._ use '_the swarm intelligence and ant system'_ wherein they create an agent (ant) which proactively uses the WSN node's information (key performance parameters), as it traverses a route from node to node, to predict or anticipate jamming, and accordingly, changes the route to avoid jamming. They suggest a decision threshold, called probability of selecting a link between nodes \(i\) and \(j\), called \(P_{ij}\) to be calculated at node \(i\). They describe that if the calculated \(P_{ij}\) is within the acceptable limits then the agent selects the link for its travel, else, it rejects it and selects that link whose \(P_{ij}\) is within the acceptable limits. \(P_{ij}\), as suggested by them, is to be calculated using complicated formulae.  
Although, the approach to the problem is novel, it is obvious that it is not workable for detecting a jamming attack, especially in an information warfare environment, because: (1) some of the data, e.g., packet delivery ratio and the packet loss on a link will not be readily available normally, and if they are to be kept readily available, they will be at the cost of memory space of the node, (2) some of the data, like BER,are extremely complicated to be ascertained (as already mentioned and to be described in detail later) and involve communicating with other nodes, which may not be possible under jamming, (3) it is computation-intensive and taxes the resource-starved WSN node, (4) it involves of fixing threshold of the decision parameter for each node under different conditions, which is fraught with pit-falls, as discussed earlier, and (5) it is based on evolutionary algorithms whose complexities in terms of time and space is difficult to ascertain; but are important to be minimized for any resource-constrained network, like the WSN.
# The Real-Time Optimal Attitude Control of Tunnel Boring Machine Based on Reinforcement Learning
## 4 Case Study
### OACP Model
The episode rewards of the control policy produced in the training process were recorded, and the changes in episode rewards of four control policies with the interactive timesteps as the abscissa are shown in Figure 10. In order to better view the trend of episode rewards change, the episode rewards were smoothed using the smoothing parameter 0.9, shown in Figure 10. From the figure, it can be seen that the episode rewards of four control policies all have significantly decreased by 80-95%, which indicates the effectiveness of optimizing the attitude control policy by reinforcement learning. To verify the effectiveness of the obtained OACPs, the effects of OACP model control and manual control were compared. The total fitting degree between the tunneling axis and the designed axis is the evaluation of the attitude control effect. The deviation between the tunneling axis and the designed axis in the heading face could be obtained at each timestep. The cumulative discount sum of each timestep deviation from a section of the tunneling axis was used to evaluate the fitting degree, which was called the episode reward. The episode reward with 400 interaction steps was used as the comparison evaluation index. The OACP model was used to interact with the TBMAP predictive environment to sample the episode rewards, and the episode rewards of manual control were obtained by sampling the cumulated reward of 400 interaction steps from the actual engineering data. For each GEC, 2000 episode rewards from OACP control and manual control were compared, as shown in Figure 11. From the figure, it can be seen that the OACPs of four GECs all had lower episode rewards than the manual control. This indicates that the tunneling axis under the OACP control had a lower overall deviation than the designed tunnel axis. The OACPs also had better stability compared with manual control. Figure 10: Changes in episode rewards with epochs: (**a**) episode rewards change in GEC 2, (**b**) episode rewards change in GEC 3, (**c**) episode rewards change in GEC 4, and (**d**) episode rewards change in GEC 5.
## 3 Localisation of a City Bus with GNSS
### Software
The NTRIP client receives the RTCM correction using the LTE module, the GNSS driver handles the data coming from u-blox F9P receivers, the CAN bridge provides the interface for communication with the sensors mounted in the bus, the localisation node calculates the pose of a bus in relation to a charging station, the system activation node launches the motion-planning system when the bus approaches the depot, and the path planner and feedback controller generate the reference path for the driver and guide him during the docking manoeuvre. It is also worth mentioning that the described system is powered directly from the bus and thus can slightly affect its power demand. Thanks to the flexibility of ROS packages, which can be deactivated after the manoeuvre, we are able to run the main software only when the bus is close to the charger, and this way, we reduce the power consumption for the vast majority of the time. The Intel i5-8365 UE CPU used in our version of UP Xtreme has power consumption at the level of 15 W at the maximum load, while the monitor consumes around 5.5 W. Unfortunately, all remaining components, such as the LTE module and CAN-USB adapter, are powered through a USB port and cannot be turned off automatically. This means that by reducing the CPU usage and turning off the display, we can save up to around 20 W of power. Although it seems to be insignificant when compared to the total energy consumption of the whole electric bus, which typically varies between 0.8 and 1.7 kWh per kilomet, it is simply good practice to reduce power usage whenever possible.
## 4 Experimental Results and Analysis
### Simulation Scenario
#### 4.1.2 The Targets  
A number of observation target points are randomly established globally and assigned to each target a benefit value. The distribution of the targets in STK is shown in Figure 8.
## 5 Noise  
A micromechanical sensor comprises several loss mechanisms which, according to the fluctuation-dissipation theorem [42], correspond with noise. In general, such losses can be taken into account in an electrical equivalent circuit in the form of dissipative elements, i.e., by electrical resistors. For piezoelectric resonators, and in accordance with the two resistors in the sensor's electrical equivalent circuit in Figure 1, the loss mechanisms can be divided into two types.  
Dielectric losses are affiliated with the sensor's capacitor-like structure, i.e., to the piezoelectric material acting as the dielectric medium. These dielectric losses are considered by the loss factor \(\tan\delta_{0}\) with reported values for thin-film piezoelectric materials as low as, e.g., \(2.5\cdot 10^{-4}\) (aluminum-nitride, AlN) [43], \(1.3\cdot 10^{-3}\) (aluminum-scandium-nitride, AlScN) [44], and \(4\cdot 10^{-3}\) (lead-zirconate-titanate, PZT) [45]. The exemplary sensor referred to in this article exhibits a value of \(\tan\delta_{0}=5\cdot 10^{-3}\), thus resulting in a resistance in parallel to the static capacitance \(C_{0}\) with a value in the vicinity of the resonance frequency of \(R_{0}(f_{\text{res}})=(2\pi f_{\text{res}}C_{0}\ \tan\delta_{0})^{-1}\approx 97.5\, \text{M}\Omega\).  
The predominant loss mechanism of micromechanical cantilevers under atmospheric pressure is usually air damping, commonly referred to as viscous damping. In addition, e.g., thermoelastic friction intrinsic to the solid structure, support losses, surface losses, and mounting losses may further attenuate the cantilever's deflection, also expressed by its quality factor \(Q\)[46]. In the electrical equivalent circuit model, these losses are taken into account by the motional resistance \(R_{\text{m}}\).  
In addition to these standard loss mechanisms, depending on the type of sensor, the special functional layer may introduce additional dominant losses, e.g., in the case of magnetostrictive films [29; 38]. Such losses and the associated noise are not considered here. However, they can be taken into account using the same approach as shown below if necessary. Furthermore, the noise of the electronics is also neglected at this point, because corresponding amplifiers can generally be realized with very low noise [47], and likewise, at least the phase noise of the generator can be sufficiently suppressed [48].
## 3 Methodology
### Features Extraction
#### 3.1.2 Deep-Learned Features  
We applied deep transfer learning by using a CNN as a fixed feature extractor (see the upper part of Figure 1). Similar to the methods used in [34; 75; 86], we used a pre-trained VGG-16 on ImageNet [87] and removed the last fully connected layer. We treated the rest of ConvNet as a fixed feature extractor for our datasets. The input layer accepted images of the size 224 \(\times\) 224 and had three channels: red, green, and blue. The input images went through a series of hidden convolution layers, which used the rectified linear unit activation function. Some layers were followed by a max-pool layer, which was performed over non-overlapping max-pool windows of the size 2 \(\times\) 2 with the stride equal to two. The dimension of the deep-learned features was 7 \(\times\) 7 \(\times\) 512.
## 2 Background and Related Works
### Combining MCDA and SA  
There are not many previous works that use reviews to rank a set products. Moreover, the existing ones do not take into account the preferences of the user. For example, a method that ranks products considering the public reviews was proposed by [PERSON] et al. [31]. They first identified subjective and comparative sentences using dynamic programming; then, they employed those sentences to generate a graph of products from which the top ones can be computed.  
[PERSON] et al. [32] analysed reviews to identify their main aspects, and they used PROMETHEE to rank the commented products. [PERSON] et al. [33] proposed a similar system. They used an aspect-based sentiment classifier on the reviews and, after that, they ranked the aspects using probabilities taking into account their frequency of appearance in the reviews. [PERSON] et al. [34] calculated the user's preference on a set of options by combining a sentiment analyser and the VIKOR methodology. [PERSON] et al. [35] integrated a lexicon-based sentiment analyser with a representation of the performance of each object based on an intuitionistic fuzzy number. The final ranking was made using PROMETHEE-II. The combination of aspect-based sentiment analysis and intuitionistic fuzzy sets to rank a set of items was also proposed in [36]; in that case, the employed MCDM mechanism was IF-ELECTRE.  
Similarly, we propose to use the power of aspect-based sentiment analysis systems and ELECTRE ranking methods to develop a novel ranking system. However, unlike the previous studies, our system is the first that integrates the preferences of the users and the sentiment analysis of the reviews, as shown in the following section.
# Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data
## Materials
### Data Description  
#### 2.2.1 Hydrometeorological Data  
The meteorological and hydrological data used in this study mainly include the China meteorological forcing dataset (CMFD), CMA Land Data Assimilation System Version 2.0 (CLDAS-V2.0), and in situ data.  
The CMFD is a near-surface meteorological and environmental reanalysis dataset provided by the Institute of Tibetan Plateau Research at the Chinese Academy of Science [29]. The dataset is based on the Princeton reanalysis data, Global Energy and Water Exchanges-Surface Radiation Budget (GEWEX-SRB), the goal of the Global Land Data Assimilation System (GLDAS), and Tropical Rainfall Measuring Mission (TRMM) data, combined with meteorological data from the China Meteorological Administration (CMA). Meteorological station observation data from 740 stations in the CMA were used to correct systematic departures in the background data. The spatial resolution of CMFD is 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) with temporal coverage from 1979 to 2018. The CMFD dataset has been widely used in the hydrometeorological forecasts and hydrological research [30; 31; 32].  
CLDAS-V2.0 has a spatial resolution of 0.0625\({}^{\circ}\)\(\times\) 0.0625\({}^{\circ}\) and a temporal resolution of 1 h, covering the whole East Asia region (0\({}^{\circ}\)-65\({}^{\circ}\)N, 60\({}^{\circ}\)-160\({}^{\circ}\)E). The CLDAS dataset includes a variety of land-surface forcing data, including precipitation, temperature, shortwave radiation, specific humidity, wind speed, surface pressure, and soil status variables. In addition, the CLDAS-V2.0 dataset was developed by CMA. The soil moisture storage (SMS) starts in April 2017, and the SMS data at 2 m were used as the validation data.  
In situ groundwater level data of Ganzhou Hotel station (25.84\({}^{\circ}\)N, 114.87\({}^{\circ}\)E), Zhangji-awei station (25.84\({}^{\circ}\)N, 114.94\({}^{\circ}\)E), and Yichun station (27.69\({}^{\circ}\)N, 114.29\({}^{\circ}\)E) used in our study  
Figure 1: The location of the Ganjiang River Basin and the distribution of river system.  
are from the China Groundwater Level Yearbook from the GEO-Environmental Monitoring Institute. The Ministry of Water Resources provides in situ runoff data.
## 6 Conclusions  
The experiments were conducted to evaluate the MAF properties of a non-toxic abrasive compound (diamond powder + cold cream + processing oil (olive oil: C\({}_{98}\)H\({}_{184}\)O\({}_{10}\), grapeseed oil: C\({}_{18}\)H\({}_{32}\)O\({}_{2}\), or castor oil: C\({}_{57}\)H\({}_{104}\)O\({}_{9}\)). The following results were obtained:  
1. The best surface roughness (Ra) value was 0.04 \(\upmu\)m, obtained when using iron powder + diamond particles + cold cream + grapeseed oil under the optimal conditions (rotational speed: 1000 rpm, finishing time: 180 s, diamond grain size: 1 \(\upmu\)m, and vibration: 8 Hz).  
Figure 8: SEM images of a workpiece surface finished with grapeseed oil (particle size 1 \(\upmu\)m, rotational speed 1000 rpm, finishing time 180 s, and magnification \(\times\)130, 850): (**a**) Before the finishing process; (**b**) After the finishing process.  
Figure 9: EDS test results of a workpiece’s surface (**a**) before finishing and (**b**) after finishing with grapeseed oil.  
2. The best result in terms of surface roughness was found with grapeseed oil, followed by olive oil and castor oil. This can be explained by the low viscosity of grapeseed oil that makes it feasible for ultra-finishing of orthopedic Ni-Ti stent wire.
3. SEM microimages showed that the MAF process using grapeseed oil and cold cream could improve the surface finish quality of orthopedic Ni-Ti stent wire.
4. EDS testing was conducted to determine whether the components of the workpiece changed after MAF due to the various components in the abrasive compound. There were no changes in the workpiece composition when using the grapeseed oil + iron powder + diamond particles + cold cream compound, which confirmed that grapeseed oil could be used as a processing oil for high-precision finishing of orthopedic Ti-Ni wire material.  
Conceptualization and writing, J.S.K.; methodology, J.S.K. and L.H.; software, S.C.; validation, L.H.; writing--original draft preparation, J.S.K. and S.D.M.; funding acquisition, L.H. and S.D.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation (NRF) of South Korea (Grant No. 2019R1F1A1061819 and Grant No. 2021R1 IIA1A01060699).  
Not applicable.  
Data available from the authors upon request.  
The authors declare no conflict of interest.
# Revisiting the Detection of Lateral Movement through Sysmon
## 5 Lateral Movement Categories on Windows Environments
### Pass the Hash Credential Override  
Hash transmission is a method that provides account identification and credentials integrity check without the requirement for any username or password in plaintext form. This method overrides traditional user authentication, which requires a personal access password in plaintext form, and proceeds directly to the host's approval with the use of hash algorithm-encrypted character chains. Hash distribution instead of plaintext account credentials was considered a tough-to-crack identification and authentication method. Despite the robustness of the aforesaid authentication mechanism, penetration techniques have been developed to allow the collection and extraction of all the hashes related to valid accounts. These techniques fall under the general category of \"Credential Access\". [PERSON] and [PERSON] [1] describe this attack vector as the _keylogging_ and _credential dumping_ techniques, making LM more effective and harder to be identified in real-time.  
PH is a method for achieving successful authentication without the possession of the original credentials (usernames and passwords) in cleartext form. It is considered a verysuccessful and targeted attack for bypassing traditional authentication procedures via stolen hashing credentials. Through PtH, access control services of Windows can be compromised, considering an adversary's LM as legit. PtH is a credential theft technique and LM attack at the same time, with which the attacker can leverage the challenge-and-response nature of the Windows New Technology LAN Manager (NTLM) authentication procedures utilized from the network's hosts in place of traditional plaintext credentials. With standard security protocols, NTLM hashes change only when the plaintext passwords are changed. This is the feature that makes PtH ideal for threat actors to move laterally across a network. For easy reference, the Windows Kerberos hashing authentication procedure along with the NTLM hash extraction vulnerability is illustrated in Figure 2.
## 4 Discussion
### Practical Implications of Effect Sizes  
The effect sizes reported in this study serve as a bridge between statistical findings and practical applications, offering important insights for optimizing training strategies. A large effect size (e.g., r \(>\) 0.50) observed in the relationship between maximal strength (e.g., 1 RM-derived power) and court-specific movement speed underscores the importance of strength and power development for enhancing agility and responsiveness. These findings align with the existing literature emphasizing the role of neuromuscular adaptations in improving high-intensity, short-duration movements. For coaches, this suggests prioritizing strength and power training, particularly pyometric and resistance exercises targeting lower-body explosiveness. Such programs should be customized to the athlete's position; for instance, middle blockers and outside hitters may benefit more from explosive strength training due to their roles in blocking and attacking, while setters might require balanced strength and agility conditioning. Furthermore, moderate effect sizes (e.g., 0.30 < r < 0.50) identified in the association between endurance thresholds (e.g., LT2) and performance metrics highlight the need to integrate aerobic and anaerobic conditioning into training regimens. These results suggest that while strength and power are pivotal, endurance adaptations support recovery between rallies and sustain performance throughout extended matches, particularly for positions requiring prolonged play, such as liberos and setters.  
To use these findings in practical applications, coaches should consider designing periodized training plans that emphasize position-specific demands, combining strength, power, and endurance development. Effect sizes provide a quantifiable framework to assess the expected impact of these interventions, enabling practitioners to make data-driven decisions that maximize athlete performance. Additionally, these findings reinforce the need for continuous monitoring and evaluation of training outcomes to ensure alignment with individual and team objectives.