esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Comparative Experimental Analysis of Path Planning  
#### 5.4.1 Experimental Analysis in Mountainous Environments  
Figures 6 and 7 are schematic illustrations of the results from 1 of the 12 replicate trials in the two mountain scenarios. In the mountainous region 1 scenario, the paths planned by SSA and WOA make substantial turns during flight, which not only greatly increases the flight distance, but also poses a serious threat to the flight safety of the UAV; the GA path flies at a higher altitude, avoiding obstacles but at the same time increasing energy consumption; although the paths generated by AVOA and ABC are relatively smooth, the flight paths are still not optimal; SAVOA is able to find a path that is both smooth and relatively short. For the variation in fitness, according to Figure 8a, SSA starts to converge at generation 32, but it has the highest fitness value of 603.063; WOA falls into local optimality at generation 69 with an fitness value of 585.779; the GA underwent three convergences, at generations 2, 26, and 168, with a final fitness value of 534.291; ABC falls into a local optimum at generation 33 with a fitness of 486.8; AVOA converges multiple times and is the least adapted algorithm besides SAVOA at 481.708; SAVOA gradually converged at the 31 st generation, with slight changes afterwards, and the final fitness was 464.328, with the best convergence performance and optimization ability.  
In the mountainous region 2 scenario, the SSA, WOA, and GA turn angles are extremely large, which can lead to very unstable UAV flight; the AVOA has a medium turn angle, but flies a long distance; in contrast, SAVOA plans flight paths that are close to the shortest distance with the lowest fitness. From Figure 8b, it can be seen that SSA has the highest fitness of 775.521; this is followed by GA, which falls into the first local optimum at generation 6 and the second local optimum at generation 73, with a fitness of 568.374; WOA reached convergence at the 17 th generation, but its fitness was relatively high, at 524.655; AVOA nears convergence at generation 67, with slight changes afterward and a final fitness of 506.123. ABC's fitness decreases sharply at generation 9 and then falls to a local optimum at generation 48 with a fitness of 488.051; SAVOA finds the near-optimal path in generation 34, followed by fine-tuning, and the final fitness is 460.661, which is significantly better than the other five algorithms.
## 5 Experiments and Results
### Experiment Setup
The ABX test and a preference test were performed wherein stimuli consisting of 10 sentences were presented to 20 subjects (15 males, 5 females, ages ranging from 21 to 51 years, mean: 34.3, standard deviation: 10.8). All subjects had normal hearing ability. Although they were native Korean, they had participated in many VC tests using English utterances. In the ABX test, the first and second stimuli, A and B, were either the reference speaker's or the target speaker's, while the last stimuli X was converted speech achieved using the underlying methods. The subjects were then asked to select either A or B as a candidate for X. The subjects were allowed to listen to each utterance as many times as they wished before making a judgment. Along with the ABX test, a preference test was conducted in which the same subjects participated in the ABX test listened to two randomly selected converted utterances per method and conversion pair. The subjects were asked to choose the perceptually preferred stimuli. In this test, each pair of stimuli consisted of the two converted utterances, one from the proposed method and the other from the conventional methods. Since this test was designed to evaluate the overall quality rather than voice personality, the subjects were asked to pay more attention to the naturalness and intelligibility of the converted speech signals.
## 3 Results and Discussion  
### Evaluation of SFE-CO\({}_{2}\) of Hops at 10-15 MPa Pressure  
SFE-CO\({}_{2}\) is one of the most common non-conventional upscalable extraction techniques to isolate high-quality non-polar constituent assemblies from hops with relatively cheap, non-toxic, non-flammable, generally recognized as safe (GRAS) and readily eliminated after extraction food-grade solvent CO\({}_{2}\)[7; 31]. The effectiveness of SFE-CO\({}_{2}\) in terms of cumulative yields and selectivity towards specific hop constituents can be achieved by modifying P, T and \(\tau\); other factors, such as particle size and cosolvent addition may also be important [32]. The task of this part of the study was to assess commonly used process parameters in hop extraction industry before further SFE-CO\({}_{2}\) optimization experiments. The extraction curves in Figure 1 depict the cumulative _Ella_ hop extract yield as a function of time in the one-stage process at 10-15 MPa and 40 \({}^{\circ}\)C, which is most commonly applied at the industrial level [7]. The yields and TEACORAC values at the final point of the kinetic experiments (after 300 min) are reported in Table 1.  
All three extraction curves followed a similar pattern (Figure 1): \(\sim\)50% of the final SFE-CO\({}_{2}\) extract yield was obtained after 45 min of extraction, \(\sim\)80% after 120 min. For 10 MPa, the equilibrium state was reached after 180 min, yielding 9.3 g/100 g of light yellow extract. For 12.5 and 15 MPa, \(\sim\)96% of the final extract yields, amounting to 19.1 and 22.1 g/100 g (Table 1), respectively, were recovered after 240 min. Thus, the increase in CO\({}_{2}\) density from 629 kg/m\({}^{3}\) at 10 MPa to 780 kg/m3 at 15 MPa resulted in a remarkable (\(>\)2-fold) increase in yield. Nevertheless, the prolonged \(\tau\) (\(>\)240 min) was required to achieve this aim, which is in agreement with the previous data for SFE-CO\({}_{2}\) of hops at low P range (\(<\)20 MPa) [10; 12; 33]. It can be noted that the shape of _Ella_ hop SFE-CO\({}_{2}\) curves (Figure 1) is almost similar to the one previously reported for other hop varieties, exhibiting a rather long low-yield period at the beginning of extraction [10; 33; 34]. Antioxidant capacity of lipophilic extracts obtained at 10-15 MPa was assessed using biologically relevant peroxyl radical inhibition-based ORAC assay [35]. As reported in Table 1, the P change from 10 to 15 MPa augmented extract TEACORAC by 21% (from 1252 to 1515 mg TE/g). Considering extraction yields, the calculated recovery of TE antioxidants from the pellets was in the range of 117-335 mg TE/g, indicating a nearly 3-fold increase due to the higher P. For applications in functional food, intracerebral and commectic industries, the strong antioxidant potential of SFE-CO\({}_{2}\) extracts is the desired quality characteristic. Thus, the extraction of hop antioxidants at higher yields could be
# Time-Varying Preference Bandits for Robot Behavior Personalization
## 4 Methods  
We present a novel discounted preference bandit (DPB) to estimate the time-varying preference with the minimum number of queries. First, we newly define a context vector as the difference of feature vectors between two trajectories, i.e., \(X:=\Phi\big{(}\xi^{A})-\Phi\big{(}\xi^{B}\big{)}\), which represents the information of comparison. Let \(\mathcal{X}\) be a set of possible context vectors that are converted from all trajectory pairs. Then, the query selection problem is converted to choosing a proper query vector in \(\mathcal{X}\). Furthermore, based on \(X\), the probabilistic model in (2) can be converted into the following logistic distribution,  
\[P\Big{(}R_{t}\mid\xi_{t}^{A},\xi_{t}^{B};\theta_{t}^{*}\Big{)}=e^{R_{t}X_{t}^{ \mathsf{T}}\theta_{t}^{*}}\Big{/}\Big{(}1+e^{X_{t}^{\mathsf{T}}\theta_{t}^{*}} \Big{)}, \tag{3}\]  
where \(X_{t}\) indicates a context vector of \((\xi_{t}^{A},\xi_{t}^{B})\) that are compared at round \(t\). By introducing a context vector \(X_{t}\), PBL can be reduced to the online learning problem. Algorithm 1 demonstrates the online learning process of DPB to acquire time-varying reward functions. In each round \(t\), DPB selects a batch of queries as outlined in line 4. The human then observes these queries and provides preference labels for each query as described in line 5. Finally, the parameter is updated with the collected preference data as shown in line 6.  
```
0:\(c_{\mu},\delta,\lambda,\gamma,d,T,D,S,b,\mathcal{X}\)
0:\(\hat{\theta}_{T}\)
1:\(t\gets 0\), \(N(\gamma)\leftarrow\ln(1/(1-\gamma))/(1-\gamma)\), and \(V_{0}\leftarrow\lambda I_{d}\)
2:while\(t<T\)do
3: Set \(\alpha_{t}\) in Theorem 1
4: Select top-\(b\) queries \(\{X_{t+i}\}_{i=0}^{b-1}\in\mathcal{X}\) from (4)
5: Demonstrate \(\{X_{t+i}\}_{i=0}^{b-1}\) and collect \(\{R_{t+i}\}_{i=0}^{b-1}\)
6: Estimate \(\hat{\theta}_{t}\) by solving (5) and \(t\gets t+b\)
7:endwhile
```  
**Algorithm 1** Discounted Preference Bandits (DPBs)
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 2 Candidate Wireless Technologies
### Alternative Low-Power Wireless Technologies  
#### 2.2.1 Classic Bluetooth  
Classic Bluetooth is a Wireless Personal Area Network (WPAN) technology [61] where a number of Bluetooth devices (up to eight) form a short-range personal network known as a piconet. In Bluetooth, slave devices must be paired and synchronized with a master device before data communication starts. This is usually achieved via the use of common clock between the communicating devices. Bluetooth operates in the 2.4 GHz ISM frequency band; it utilizes frequency hopping mechanism among 79 channels with an average rate of 1600 hops per second to minimize interference. The Bluetooth standard classifies devices into three groups based on transmission power and corresponding coverage area. The wireless communication range supported by the standard provides adequate coverage for WBAN communications. The Bluetooth SIG has developed the Health Device Profile (HDP) [61; 62] capable of providing usage models for fitness and healthcare devices. HDP is also able to wirelessly connect devices such as glucose meters, pulse oximeters, weight scales, thermometers and blood pressure monitors to sink devices such as cell phones, PDAs, laptops and personal computers.
## 4 Engineering Application
### Support Test on the FMCFR in the 8# Coal Seam of GB Coal Mine  
After the excavation of the 842 air roadway (the 84 mining area, the 8# coal seam, GB Coal Mine), industrial tests were performed to apply the synergistic support system on a 500 m long section of ESEFMCFR. During the industrial tests, monitoring devices were installed to collect data. Subsequently, the support effect of the 842 air roadway was evaluated and analyzed.  
Analysis on the Monitored Data of the Mine Pressure  
Mainly roadway surface displacement data were monitored for the mine pressure of the 842 air roadway in GB Coal Mine. The displacement variations in the roof, floor and two sides of the 842 air roadway in GB Coal Mine are presented in Figure 14. During the monitoring period, the deformation of roadway SR varies notably within 25 days after excavation, yet tends to stabilize on the 30 th day. Before the roadway stabilizes, the displacement value of roof and floor is 121 mm, with its average displacement speed being 4.03 mm/d; those two parameters of the two sides are 74 mm and 2.47 mm/d, respectively. In the whole monitoring period, the deformation of SR in ESEFMCFR can be effectively controlled through the synergistic support system of \"LDAGF\".  
Evaluation on the Support Effect  
The supporting structures boast complementary advantages. Hence, the bearing capacity of the synergistic support system can effectively resist the severe deformation of the SR and fully mobilize its bearing capacity. Nevertheless, both the roof and floor anchor cables of the roadway can realize the expected effect and achieve the one-time permanent support of the roadway.  
Figure 13: Displacement curves of roof, floor and two sides of the 815 transport roadway in XH Coal Mine.
###### Abstract  
This work investigates the effects of pressure pulsations on reciprocating natural gas compressor performance thermodynamically. A nonlinear hybrid numerical model is thus developed to consider the interaction between the compressor and the pipeline system. The suction chamber, compressor cylinder and discharge chamber are modelled integrally based on the first law of thermodynamics and mass balance, and the pipeline flow is described by using the gas dynamic model. Methane is considered as the working fluid and its properties are computed based on ideal and real gas assumptions. For the real gas model, the methane properties are obtained by means of calling the NIST REFPROP database. The validity of numerical results is confirmed by previous experimental values. Results from the examinations of pressure pulsation influence demonstrate that discharge resonance requires more specific work than suction resonance in the same harmonic; in the suction system, the first harmonic response reduces the mass flow rate but significantly increases specific work, and the second harmonic response has a strong supercharging effect but the specific work is increased slightly; in the discharge system, the mass flow rate is changed little by pressure pulsations, but the indicated power and specific work are increased significantly; for the real gas model, the in-cylinder temperature during the compression and discharge phases, mass flow rate and indicated power are higher than those for the ideal gas model, whereas the specific work is less for the real gas model than for the ideal gas model.  
Keywords:natural gas; reciprocating compressor; hybrid numerical model; pressure pulsations; thermodynamic modelling +
Footnote †: journal: Applied Sciences
## 2 Background of DSRC
### DSRC Communication Stack  
The DSRC stack is a layered architecture that forms the foundation for communication in DSRC networks. DSRC is a wireless communication technology specifically designed for vehicular ad hoc networks, enabling vehicles and infrastructure to exchange information for various applications such as road safety, traffic management, and other services. The DSRC stack, as seen in Figure 2, consists of multiple layers, each serving a specific purpose in the communication process.  
The foundation of the DSRC structure lies in the Physical (PHY) and Media Access Control (MAC) layers, where the implementation of the IEEE 802.11p standard plays a crucial role. This layer serves as the gatekeeper, controlling access to the shared communication medium within DSRC networks. To ensure efficient management of access and to alleviate potential collisions among vehicles, it utilizes the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanisms. This strategic deployment of protocols and mechanisms underscores the sophistication and robustness of DSRC's design.  
By capitalizing on the virtues of the IEEE 802.11p standard, the DSRC system optimizes communication efficiency, particularly in the demanding vehicular environments in which it operates. To this end, it judiciously allocates specific channels for control- and safety-related communications, while reserving separate channels for general applications. This strategic channel allocation is complemented by the inclusion of synchronization intervals and guard times, further bolstering communication effectiveness. Consequently, the DSRC architecture operates seamlessly within its designated frequency range.
# An Improved Approach to DNS Covert Channel Detection Based on DBM-ENSec
## 4 Experiments and Analysis of Results
### Experimental Setup  
All experiments in this study were conducted on a workstation equipped with a 12 th Gen Intel(r) Core(tm) i7-12700 CPU (2.10 GHz), 16 GB of RAM, and an NVIDIA RTX 4070 GPU. The environment included both Windows 11 and CentOS 7 x86_64 platforms for traffic data collection. Model implementation was carried out using Python 3.8. Deep learning modules, including DBM, CNN, and attention mechanisms, were developed with TensorFlow/Keras, while ensemble classifiers such as XGBoost, LightGBM, Random Forest, and CatBoost were implemented using Scikit-learn and their respective native libraries.
# On Data Leakage Prevention Maturity: Adapting the C2M2 Framework
###### Abstract  
In an evolving cybersecurity landscape marked by escalating data breaches and regulatory demands, data leakage prevention (DLP) has emerged as one of several defense mechanisms. This study underscores unresolved foundational issues within DLP, revealing that it remains a significant challenge in large organizations. This highlights the necessity for a holistic approach to DLP to effectively address these persistent challenges. By developing a DLP Maturity Model, adapted from the renowned C2M2 framework, this research provides a comprehensive tool for assessing organizational DLP capabilities and pinpointing critical gaps. Applying the DLP Maturity Model within the financial sector as demonstrated through a banking scenario showcases its relevance and added value. This application illuminates the model's effectiveness in securing sensitive data and adhering to essential regulatory standards, highlighting its adaptability across various compliance landscapes. Implementing this DLP Maturity Model in a banking scenario showcases its applicability, highlighting its ability to formulate a strategy to secure sensitive data and comply with regulatory standards. This approach aligns with the concept of a continuous risk-based strategy, merging the holistic model to identify and address critical insider risks within organizations. The study addresses a specific gap in DLP research, notably the lack of a holistic framework for assessing and enhancing DLP strategies across organizations. It equips practitioners with a foundational tool to determine current DLP maturity and devise strategies for mitigating insider-driven data breach risks, thereby bolstering organizational cybersecurity resilience.  
data leakage prevention; data loss prevention; DLP; cybersecurity maturity; Maturity Model; C2M2 2024
###### Abstract
None of these works take recharging the UAV into account. In addition, the MDP-based UAV path-planning method requires the environment to be divided into discrete areas with separate action spaces, which results in a lot of additional propulsion power consumption. To solve these problems, we consider, in our work, that UAVs can perform long-term data collection tasks in dynamic scenarios where the AoI and their own power levels are constantly changing. Therefore, we formulate the trajectory design problem of the UAVs as a semi-MDP (SMDP) and use a hierarchical deep Q-network (DQN)-based method. The contributions can be summarized as follows:  
* We study the path-planning of rechargeable UAVs in continuous data collection tasks, in which a UAV moves towards the ground nodes to collect data during a period of time and returns to recharge at the right time. We introduce the AoI to indicate the freshness of the collected data;
* To optimize the freshness of collected data and to ensure that the remaining charge of the UAVs is sufficient, we formulate the path-planning problem as an SMDP and propose a hierarchical DQN (H-DQN) method to deal with it. The agent learns to select options at a high level and the action depends on the selected option. The rest of the paper is organized as follows. We review related work regarding the AoI and the typical algorithm of DRL in Section 2. Then, we describe our system model in Section 3. In Section 4, the proposed algorithm is explained in detail. Afterwards, we present simulation experiments and analyses to validate the performance and efficiency of our proposed approach in Section 5. Moreover, we compare our proposed method with the baseline methods to show its advantages. Finally, we give the conclusions in Section 6.
## 5 Results and Discussion
### Model Performance  
Table 6 shows the performances of all methods under the metrics of AUC and ACC. Each experiment was repeated three times and the averaged results are reported. Relative scores are given compared to the strongest baselines, whose results are underlined. Note that a slightly higher AUC of 0.001 is regarded as significant for CTR predictions [23; 39; 41]. Therefore, for both datasets, ALIEN outperformed all baselines with large margins.
# Time-Sensitive Networking Technologies for Industrial Automation in Wireless Communication Systems
## 6 Conclusions  
This study introduced research trends in TSN related to wireless networks such as 5G and 802.11 WLAN. TSN-related standardization trends in 5G networks were discussed and studies in 802.11-based WLAN including 802.11 ax and 802.11 be were introduced. In the future, studies focusing on applying TSN to wireless LAN or mobile communication environments will continue to be conducted owing to the advantages of TSN in wireless environments. In summary, this paper provided insights into the trends in TSN and related technologies employed for reliable and low-latency wireless communication. This survey could lay the foundation for establishing TSN in wireless communication environments.  
Conceptualization, Y.K. and D.A.; methodology, Y.K., S.L., S.G., T.K. and D.A.; formal analysis, Y.K. and D.A.; resources, T.K. and D.A.; writing--original draft preparation, Y.K. and D.A.; writing--review and editing, Y.K. and D.A.; visualization, S.L. and T.K. and D.A.; supervision, D.A.; project administration, Y.K. and D.A.; funding acquisition, Y.K. All authors have read and agreed to the published version of the manuscript.  
Figure 12: Multi-link operation.  
**Funding:** This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-00974-002, Development of ultra-reliable and low-latency 5G+ core network and TSN switch technologies).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experimental Procedure
### Adsorptive Removal of CFA
#### 3.5.2 Adsorption Isotherm  
Adsorption isotherm assays were performed in a similar manner to the adsorption kinetic study, except that the initial concentration of CFA solution was varied from 10 to 50 mg L\({}^{-1}\). The liquid samples were only taken at the equilibrium time, which was calculated in the adsorption kinetic experiment.  
The adsorption capacity of the materials at the equilibrium concentration of CFA solution was plotted to access the adsorption behavior according to Langmuir and Freundlich isotherm models [51].  
The Langmuir isotherm in a linear form was expressed as Equation (5);  
\[\frac{1}{q_{e}}=\frac{1}{q_{m}}+\frac{1}{k_{L}q_{m}C_{e}} \tag{5}\]  
where \(q_{m}\) is the maximum adsorption capacity (\(\mu\)g g\({}^{-1}\)) and \(k_{L}\) is the Langmuir constant.  
Equation (6) shows the linearized Freundlich isotherm expression;  
\[\ln q_{e}=\ln k_{F}+\frac{1}{n}\ln C_{e} \tag{6}\]  
where \(k_{F}\) is the Freundlich constant and \(n\) is the adsorption intensity (dimensionless).