esa-sceva/satcom-chunk-collection huggingface dataset:
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 2 State of the Art
### Introduction of Biometrics  
A statistic published in 2018 shows that the share of smartphone shipments including a fingerprint sensor increased from 19% to 55% globally between 2014 and 2017. For the year 2018, a penetration rate of over 60% was predicted, due to the expected breakthrough of the in-display fingerprint [16]. [PERSON] and [PERSON] [10] surveyed users about their opinion on biometric authentication systems resulting in 83% of all respondents thinking positively about biometric authentication systems. Furthermore, about 61% responded positively about the use of a non-intrusive continuous authentication system [10].  
An important advantage of biometrics is that they are more difficult to forge and copy than password-based authenticators. Their strength lies in the difficulty of counterfeiting them. On the other hand, passwords and PINs are easier to replace than biometrics when they get compromised. For example, if an adversary gets hold of a user's fingerprint, or a driver's license as a traditional biometric, they should no longer be used for authentication, since a third party could be tricked into thinking the adversary is the original owner of the biometric data [17].
## 2 Materials and Methods
### Study Design  
Using a single-blinded, crossover research design, participants were tested during two separate days, each consisting of three, 10 min treadmill walking trials separated by a 3 min rest period, with a 24 h washout period in between each day. All participants were randomly assigned to a participant number produced from a random number generator, which was operated by an investigator independent from recruitment and testing. Each participant number corresponded to a randomized sequence of testing for the two different days. The order for the speeds of the treadmill (3.3 mph, 3.5 mph, and 3.7 mph) were randomized, as well as the order for the IVR or non-IVR trials (i.e., IVR trials occurred on either the first day or second day of testing). The non-IVR trials used the same treadmill albeit with no head-mounted display (HMD). Therefore, while the non-IVR trials did not contain the same sensorimotor elements utilized with the HMD, the same speed order and testing sequence used in the IVR trials was applied for consistency. Participants were told (and deceived) that all trials occurred at 3.5 mph for blinding purposes. While one researcher was responsible for monitoring the participant during their rest periods in a nearby room without the treadmill, the other researcher was tasked with changing the speeds based on the assigned order. The first researcher, blinded to the speeds of the treadmill and their order, was solely responsible for assessing the RPE and HR data to ensure that a blinded approach was achieved. All testing was administered by trained research personnel under the supervision of the lab director in the UC Fit-Digital Health Exercise Physiology Research Laboratory at UCLA during the early spring of 2023 for a one-month period. Study participants were asked to refrain from completing moderate-vigorous physical activity at least 24 h prior to beginning the study until its conclusion. For baseline measures, body mass was measured on a calibrated medical scale (accuracy \(\pm\) 0.1 kg), and height was determined using a precision stadiometer (Seca, Hanover, MD, USA; accuracy \(\pm\) 0.01 m). Body fat percentage was measured using a validated cotipolar, multi-frequency, multi-segmental bioelectrical impedance analyzer (InBody Co., Seoul, Republic of Korea) [24]. The instrument measured resistance and reactance using proprietary algorithms. To ensure accuracy, participants adhered to standard pre-measurement BIA guidelines recommended by the American Society of Exercise Physiologists [25].
## 5 Experiment III: Listening Experience Rating
### Interim Discussion  
In contrast to Expt. II, in which subjects specified the SBR that they felt was most appropriate, in this last experiment listeners were asked to rate their experience resulted from different SBR levels, including those favourable levels observed in the previous experiment. For the constant SBRs, listeners tended to rate 9-dB higher than 0-dB in relatively steady backgrounds (crowds in 'Football commentary' and babble noise in 'Public speech'), in which energetic masking is strong [13]. This could be because 0-dB is too close to the \(\text{SBR}_{min}\), which is the measured threshold for perceiving all the speech content. Understanding speech at this level requires more listening effort from the listeners [52], leading to degraded listening experience. An opposite pattern was observed for 0-dB and 9-dB in the presence of fluctuating backgrounds (music or sound effects in 'Radio drama' and 'Radio poetry'). Given the predicted \(\text{SBR}^{\prime}_{min}\) of \(-12.4\) dB for 'Radio drama' and \(-4.2\) 'Radio poetry', this evidently is no longer due to the issues of intelligibility or listening effort. It is likely that listeners thought that 9-dB BSR led to considerably low background sounds, hence the lack of atmosphere for the scenes. In the two scenes, 9-dB was merely rated as the same as \(\text{SBR}^{\prime}_{min}\). This is a clear demonstration of the importance of the balance between speech intelligibility and background sounds to listener listening experience. However, this explanation may not apply to 'News broadcasting'--the competing speech in the background induces more negative effects (e.g., informational masking and attention switching) than other backgrounds tested here, which mostly only introduce pure energetic masking. This has already been discussed in Section 4.4.  
One would naturally expect that the listening experience with the listener-preferred level should be rated higher than other levels. However, while LP and OPT indeed led to better listening experience in most of the scenes, listeners also thought that some other levels may provide similar experiences although there was a difference of up to 8.6 dB (e.g., between LP and 9-dB in the 'Football commentary' and 'Public speech' scene, and between LP and 0-dB in the 'Radio drama' scene). This may be a reflection of the diversity of listener's opinion on the optimal SBR level for mixing--while some listeners enjoy louder background sounds that may create better atmosphere in the scene, others perhaps prefer a quieter background that results in better intelligibility with less listening effort used in understanding the target speech. Furthermore, such listener variability in preference suggests that using a reference system to compare candidates to, as in e.g., [19], may be inappropriate, leading to biased opinions of listeners.
###### Abstract
[43] proved that the ERT method could be used as an effective tool to map the distribution of gas and leachate in a large landfill site and showed that the relationship between the resistivity and moisture content of waste samples could be fitted according to Archie's law. [PERSON] et al. [44] obtained a 2D model of an area through ERT, identified the high-resistance area below the seabed, and determined that it was a mixed area of fresh water and saltwater. In this study, we chose ERT as the method to detect pollution. On the one hand, ERT is easy to operate and highly precise. The data are detailed, clear, and sensitive to NAPL(non-aqueous phase liquids) [45]. On the other hand, the application of ERT in quickly detecting pesticide leakage pollution is still rare and the delineation of pollutants is not perfect, which needs to be further studied. We used non-intrusive ERT technology to remotely obtain data from several meters below ground level in this paper. Then, we accurately characterized the subsurface pollution plumes at the site under non-invasive conditions. Combining drilling and water chemical analysis, we successfully verified the ERT's results and then delimited the pollutant plumes in spatial distribution. This proved the feasibility of ERT, which can efficiently and effectively characterize the discontinuous distribution of pollutants underground, and can now serve as a useful guideline for other similar projects. It has been demonstrated that this remote sensing technology can be a new source of evidence for fingerprinting unanticipated and unknown pollutants and for the rapid on-site identification of contamination scales.
# EEMIP: Energy-Efficient Communication Using Timing Channels and Prioritization in ZigBee
## 3 The Proposed EEMIP Method  
Based on the state-of-the-art, we propose a solution (i.e., the EEMIP method) to increase energy-efficiency of periodic ZigBee communication with a reliable delivery. In the proposed solution, we use two main nodes in the network topology; one coordinator, which controls the communication flow, and multiple end devices with sensors. Since we target the star topology, there is no need for router-type devices. The two types of nodes communicate with each other in order to exchange data. An architecture overview is provided in Figure 1.  
The key idea is to use a nonbeacon ZigBee mode, but each end device is assigned with a unique time slot for communication. Based on the different priorities, the end devices use different time periods, which enable some devices to communicate more frequently than the others. It is also useful in a case of traffic congestion, where the coordinator ensures unaffected delivery of high-priority data (e.g., health failure, fall) in contrast to low priority data (e.g., temperature, pulse), which may be delayed. The method is described in the following subsections in more detail.
## 3 The Proposed Model
### Energy Model  
The underwater energy utilization method shown in [52] is used in the study. This study considered \(P_{0}\) as the minimum power that a node needed to receive the packet, and minimum transmission power must attain \(P_{0}A(l)\), whereas \(A(l)\) represents an attenuation function. The energy utilization for receiving and transmitting is estimated as follows:  
\[E_{t}(l)\ =\ T_{t}P_{0}A(l) \tag{1}\]  
\[E_{r}=\ T_{r}P_{0} \tag{2}\]  
\[A(l)\ =l^{1.5}a^{l} \tag{3}\]  
\[a=10^{\alpha(f_{c})/10} \tag{4}\]  
\[\alpha(f_{c})\ =0.11\frac{f_{c}^{2}}{1+f_{c^{2}}}+44\frac{f_{c}^{2}}{4100+f_{c ^{2}}}+2.75\times 10^{-4}f_{c}^{2}+0.003 \tag{5}\]  
in which \(E_{t}(l)\) indicates the energy utilization to transmit, and \(E_{r}\) indicates the energy utilized for receiving. \(T_{t}\) represents the time for the node to transmit the packet, and \(T_{r}\) shows the time to receive the packets. \(l\) denotes the distance among transmitting and receiving the nodes. \(\alpha(f_{c})\) represents the absorption coefficient in dB/km and \(f_{c}\) shows the frequency in kHz.
## 3 Results and Discussion
### Optimization by NSGA-II  
The Pareto frontier obtained by solving the optimization model with the NSGA-II algorithm was shown in Figure 8. Lr was represented on the abscissa, and Dr was represented on the ordinate. For multi-objective optimization problems, it was impossible to make each objective optimal at the same time. However, coordination and tradeoffs between the objectives can be performed to satisfy each objective as much as possible. This means that all solutions on the optimal frontier can be used for scheme optimization. According to the reference of GB/T 21961-2008 (State Standard of the People's Republic of China of Test Methods for Maize Combine Harvester) and GB/T 21962-2008 (State Standard of the People's Republic of China of Technical Requirements for Maize Combine Harvester), the permissible range of Lr and Dr is 5% and 5%, respectively. Dr is less important than Lr within the permissible range, therefore the weight of Lr is greater than weight of Dr. In order to make Lr and Dr as optimal as possible, the drum speed was selected as 384.1 rpm, the feed rate as 8.6 kg/s and the concave clearance as 40.5 mm, according to the requirement of corn harvest. Therefore, the best operational performance of a longitudinal axial threshing cylinder on frozen corn was obtained. The Lr was 1.98% and the Dr was 3.49%. Under this condition, Lr has reached the normal threshing indexes of threshing corn at normal temperatures. Dr was lower than the normal threshing indexes of threshing corn at normal temperatures.  
Figure 8: Pareto-Pareto optimal sets with NSGA-II.  
Figure 7: Response surface diagrams of interaction: (**a**) Interaction of Ds-Fr; (**b**) interaction of Ds-Cc. (**c**) Interaction of Fr-Cc.
## 5 Conclusions  
In this work, an FDNN model is proposed to predict the deformation of the initial support. It employed a feature extraction and fusion method that integrates various tunnel parameters to enhance the performance. The influence of tunnel parameters on the performance is analyzed. The main findings are summarized as follows:  
1. Compared to the DNN, XGBoost, DT, and RTR models, the FDNN model demonstrates better predictive performance in terms of deformation prediction. The fusion of various data enhances the model's robustness;
2. The training data composition had a large impact on the results. The degree of data influence is in the order of geological data, support data, and construction data;
3. The performance of the model gradually rises with the increase in data types. Different tunnel feature data overlap and are superposition for model performance enhancement.  
The construction machine operation information and geologic forecast information were not considered in the model in this study, and the machine operation parameters and geologic forecast data contain rich geologic information, which can be considered in the model in future studies.  
Conceptualization, J.Z. and M.M.; methodology, Q.F. and J.W.; software, X.H. and G.S.; formal analysis J.Y.; writing--original draft preparation, Q.F. and J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Key Project of High-speed Rail Joint Fund of National Natural Science Foundation of China (U1934210) and Natural Science Foundation of Beijing (8202037).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
Author [PERSON] and [PERSON] are employed by the Road & Bridge International Co., Ltd. Author [PERSON] and [PERSON] are employed by the Beijing & Hebei Construction and Development Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 1 Introduction  
Acne vulgaris is a common skin disease resulting from sebum production in hair follicles on the face, neck and chest, induced by androgen, altered keratinization, inflammation and bacterial colonization. Among them, _Cutibacterium acnes_ is known as a major cause [1].  
Probiotics are live bacteria that have beneficial effects on the host; they have been proposed as an alternative to antibiotics, as the indiscriminate use of the latter can cause multidrug resistance or other side effects [2; 3]. The direct antibacterial effects of probiotics against pathogens may be observed by inhibiting the growth of pathogens by the production of antimicrobial peptides and colonization resistance [4]. Therefore, probiotics have potential for use as cosmetic or medicinal ingredients against _C. acnes_.  
Prebiotics are defined as nondigestible food ingredients, such as oligosaccharides, inulin and lactulose, which promote the growth and survival of probiotics; they are found in vegetables and fruit [5; 6]. In human skin, prebiotic substances can be selectively applied to modulate microbial systems to achieve beneficial effects [7]. In order to develop natural prebiotics, various medicinal plants have been investigated. Among them, turmeric (_Curcuma longa_ rhizome) is well known for its anti-inflammatory, antimicrobial and antioxidant activities [8]. A previous study reported that _Curcuma longa_ rhizome extract (CLE) reduced the levels of reactive oxygen species (ROS) and cytokines produced by _C. acnes_[9]. However, few studies have focused on CLE as a prebiotic, and none has reported synergistic effects of combinations of probiotics and CLE as synbiotics on _C. acnes_.  
Therefore, in this study, the synergistic effects of probiotics and prebiotics against C. _acnes_ were investigated. First, the antibacterial effects on C. _acnes_ of five potential probiotic strains were assessed. Then, the extracts of twelve natural medicinal plants that have prebiotic potential were screened for their antibacterial activities against C. _acnes_ using antibacterial assays. It was found that only CLE displayed inhibition zones. Finally, each of the five lactic acid bacteria (LAB) species was applied with CLE to examine synergistic antibacterial effects on C. _acnes_.
## 3 Methodology
### Piecewise Approach of the Proposed Methodology  
Since it has been demonstrated that a PV inverter has an anomalous behavior when it operates in regions far from its rated values [33], the described methodology is applied considering the signals in a piecewise approach. Therefore, the design variables and the objective function are divided into four sections through a day. Considering that the maximum expected irradiance value is around the 1000W/m\({}^{2}\), a threshold of the 20% of this value is set for defining two regions: one at the beginning and another at the end of the day. These two sections are named S1 and S4, and they are the regions where the values of the irradiance are below the threshold value, i.e., they represent the low power operation regions of the PV inverter. The remaining data of the signals are divided into halves to obtain two other sections called S2 and S3. This way, a total of four sections (S1, S2, S3, and S4) are obtained and a different set of weights is estimated for each one.
# Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11 ah
## 6 Performance Evaluation and Discussion
### Static Traffic Patterns
#### 6.2.3 Transmission Interval Estimation Accuracy  
Figure 9 depicts the accuracy of transmission interval estimation for both HT and LT scenarios with and without hidden nodes (H.N.). The \"accuracy\" represents the ratio between the estimated transmission interval and the real transmission interval, averaged over all stations. As such, a value equal to 1 means no estimation error, higher than 1 means overestimation and lower than 1 means underestimation. For the HT scenario, the estimation is quite accurate for traffic load \(T=\{0.85,\,0.75\}\) Mbps (\(\eta=81\%,71\%\)) as the accuracy is close to 1. While for high traffic load \(T=0.12\) Mbps (\(\eta=114\%\)), the transmission interval is highly overestimated, since the traffic is overloaded and each station get less transmission opportunity than it requires. The same conclusion can be drawn for the LT scenario.  
Figure 8: Performance comparison between TAROA and EDCA/DCF for both the HT and LT scenario with hidden nodes for different traffic loads and number of stations. **(a)** throughput, HT; **(b)** packet loss, HT; **(c)** latency, HT; **(d)** throughput, LT; **(e)** packet loss, LT; **(f)** latency, LT.
# Energy Sustainability in Wireless Sensor Networks: An Analytical Survey
## 4 Algorithm-Based Energy Sustainability in WSNs
### Communication Technologies Used in WSNs  
Modern scientific and technological progress enabled the development of advanced standards and technologies for wireless communications that aim to empower the exploitation of WSNs and Internet of Things (IoT) and support corresponding applications. Actually, there is not a specific technology developed that is able to be efficient when used in all kinds of wireless communications. Instead, there are numerous technologies with various characteristics that have been proposed. Therefore, it is very important to select the appropriate technology in order to suit best the requirements of particular types of applications [45].  
For instance, the transmission range is a metric used in order to categorize such technologies as either short-range (with coverage \(\leq\) 10 m), medium-range (with coverage 10-100 m), or long-range (with coverage \(\geq\) 100 m). Bluetooth (in its classic version) and Radio Frequency Identification (RFID) are probably the most widely used examples of the first of these categories [46]. Likewise, Ultra-Wideband (UWB), Thread, Wi-Fi, ZigBee, along with two newer versions of Bluetooth, i.e., Bluetooth Smart (which is known also as Bluetooth Low Energy-BLE), and Bluetooth Long Range, are characteristic examples of are medium-distance wireless communication technologies [47]. Likewise, LTE-M, LoRa, NB-IoT and Sigfox are representative paradigms of LPWA (Low Power Wide Area) technologies, which is an emerging family of long-range wireless communication technologies [48; 49; 50].  
In Table 1, some of the most important communications technologies that have been proposed for use in WSNs are enlisted along with some of their technical characteristics.
## 7 Prospective Aspects of WebAssembly
### Memory64  
Wasm utilizes a page-based measurement system for linear memory objects, where each page consists of 65536 (\(2^{16}\)) bytes. In the current version of Wasm (version 1), the maximum number of pages allowed is 65536, resulting in a total memory size of \(2^{32}\) bytes (4 gibibytes). This limitation extends to memory instructions, which currently employ the i32 type as a memory index. Consequently, these instructions can address a maximum of \(2^{32}\) bytes of memory. For most applications, the 4 gibibyte memory limit proves to be sufficient. In such cases, utilizing 32-bit memory indexes offers the advantage of smaller pointers in the producer language, leading to memory savings. However, certain applications may require more memory than the current limit permits. Regrettably, the existing Wasm feature set lacks straightforward solutions to address this challenge. However, introducing the capability for a Wasm module to select between 32-bit and 64-bit memory indexes effectively resolves both concerns. Furthermore, considering that Wasm serves as a Virtual Instruction Set Architecture (ISA), hosts may prefer to employ the Wasm binary format as a portable executable format while supporting other non-virtual ISAs. With the widespread adoption of 64-bit memory addresses in most ISAs, hosts may be reluctant to maintain support for 32-bit memory addresses in their Application Binary Interface (ABI).
## 5 Research Findings  
In this SLR, we explored the topic of CPLS abuses as C&C channels, focusing particularly on understanding the attack techniques employed and countermeasures proposed. This abuse is not limited to a particular type of service; instead, it is pervasive across a variety of platforms, including cloud storage, social media, business communication platforms, and more.  
Our findings indicate that botnets have evolved significantly over the years, adopting diverse methods to abuse the CPLS for C&C communication. For instance, we observed techniques such as steganography, cryptography, COM hijacking, process injection, and COMSPEC environment variable exploitation. Further discussion and additional attack techniques are presented in Section 6.  
Many mainstream platforms were found to be popular targets of abuse: cloud storage sites such as Dropbox and Google Drive, social media sites like Twitter and Facebook, business communication platforms like Slack, developer repositories (GitHub), online clipboard sites (Pastebin), push services for iOS and Android notifications, video and photo sharing sites (YouTube and Instagram), email services (Outlook and Gmail), digital distribution platforms (Discord), and instant messaging software (Telegram).  
The countermeasure strategies proposed to date, both in computer and Android environments, involve detecting anomalies in user behavior, CAPTCHA verification, reputation score calculation, and causality measurement between user activity and network traffic. However, each of these strategies come with their own limitations as outlined in Section 7.