esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Microcapsule Formation  
O/W/O-type microcapsules were formed by introducing the aqueous, inner oil, and outer oil phases into a glass microchannel (depth: 200 \(\upmu\)m, Figure S2). The microchannel was fabricated by bonding three layers: a top plate with through-holes for the inlets and outlets, a middle plate with through-holes forming the channel structure, and a bottom flat plate. The device was manufactured by KADOMI Optical Industry Co., Ltd., Japan. To form O/W/O-type microcapsules within a single microchannel, the wettability of the microchannel was partially modified using a method described in previous studies [24]. In summary, the microchannel was rendered hydrophobic by filling it with 1 vol% octadecyltrichlorosilane/toluene solution for 10 min. Then, the microchannel was partially irradiated with ultraviolet light through a photomask using a vacuum ultraviolet irradiation device (Min-excimer, Ushio Inc., Tokyo, Japan) for 120 min, and only the irradiated areas were hydrophilized. A polytetrafluoroethylene tube (inner diameter: 0.8 mm, outer diameter: 1.58 mm) was used to connect the glass syringe and microchannel. The aqueous, inner oil, and outer oil phases were filled into each syringe and introduced into the microchannel using a syringe pump (Legato 210, KD Scientific, Holliston, MA, USA). Microscopic images of microcapsule formation in the microchannel were obtained using an optical microscope (BX50, Olympus Corporation, Tokyo, Japan) equipped with a high-speed camera (FAST-CAM Mini UX50, PHOTRON LIMITED, Tokyo, Japan). Microcapsules were collected in a glass-bottom dish (D11130H, Matsunami Glass Ind., Ltd., Osaka, Japan) and microscopic images of the microcapsules were obtained using an inverted optical microscope (BZ-X710, KEYENCE CORPORATION, Osaka, Japan). The dimensions of the microcapsules were analyzed using image analysis software (Image), version 2.14.0). The mean diameter and coefficient of variation (=standard deviation/mean diameter) were calculated from the diameter values obtained for each microcapsule. Surface tension was measured using a surface tension meter (CBVP-Z, Kyowa Interface Science Co., Ltd., Saitama, Japan).
_Article_  
**Automatic Language Identification Using Speech Rhythm Features for Multi-Lingual Speech Recognition**  
**[PERSON] 1 and [PERSON] 2,*()**  
1 Department of English Linguistics, Hankuk University of Foreign Studies, Seoul 02450, Korea; [EMAIL_ADDRESS]  
2 Department of English Linguistics and Language Technology, Hankuk University of Foreign Studies, Seoul 02450, Korea  
Correspondence: [EMAIL_ADDRESS]; Tel.: +82-02-2173-8814  
Received: 21 February 2020; Accepted: 20 March 2020; Published: 25 March 2020  
**Featured Application: This research can be applied for a multi-lingual automatic speech recognition system that handles input speech with two or more languages. Such a system requires the rapid identification of a language from input speech to transmit the speech to a recognition server targeting the language.**  
**Abstract:** The conventional speech recognition systems can handle the input speech of a specific single language. To realize multi-lingual speech recognition, a language should be firstly identified from input speech. This study proposes an efficient Language IDentification (LID) approach for the multi-lingual system. The standard LID tasks depend on common acoustic features used in speech recognition. However, the features may convey insufficient language-specific information, as they aim to discriminate the general tendency of phonemic information. This study investigates another type of feature characterizing language-specific properties, considering computation complexity. We focus on speech rhythm features providing the prosodic characteristics of speech signals. The rhythm features represent the tendency of consonants and vowels of languages, and therefore, classifying them from speech signals is necessary. For the rapid classification, we employ Gaussian Mixture Model (GMM)-based learning in which two GMMs corresponding to consonants and vowels are firstly trained and used for classifying them. By using the classification results, we estimate the tendency of two phonemic groups such as the duration of consonantal and vocalic intervals and calculate rhythm metrics called R-vector. In experiments on several speech corpora, the automatically extracted R-vector provided similar language tendencies to the conventional studies on linguistics. In addition, the proposed R-vector-based LID approach demonstrated superior or comparable LID performance to the conventional approaches in spite of low computation complexity.  
**Keywords:** language identification; rhythm metrics; Gaussian mixture model; linear mixed effect model; i-vector; convolutional neural network
## 6 Results and Discussions
### Assembly Clearance  
As shown in Figure 6, the variation trends of contact pressure for Models 1-3 with the axial-directional nodes are similar. For the same axial node of each component for the three models, there exist the following features: (1) comparing with Model 1, the contact pressures of Model 2 and 3 were reduced to different degrees; (2) Model 2 has less reducing trend, whereas Model 3 has more reducing trend; and (3) the assembly clearance has the greatest influence on contact pressure on the mating surfaces of the shaft and the sleeve.  
Compared with ideal Model 1, the contact pressure recorded for Model 3 decreases by approximately 50 MPa in Figure 6a. Therefore, the assembly clearance is positively proportional to the contact pressure on the mating surfaces. The maximum clearance has a great impact on the mating surfaces, whereas the minimum clearance has a minimal impact.  
Figure 5: The distribution of Von Mises stress of the turbine shrink disk using the ideal model.
# Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches
## 6 Trends and Promises
### Future Research Directions  
Based on the insights drawn in Section 4 and the comparative results in Section 5, we see three promising directions for future research. First, a promising research direction would be to investigate the implementation options based on the limitations of the IoT devices and the IoT system architecture overall. In many IoT system scenarios, one or multiple gateways are used as the main connection point between IoT devices and the cloud. Therefore, the gateway could be considered as a key security layer in the IoT architecture. As the gateway has more computational power and would support the implementation of security solutions, we suggest installing a hybrid AIS solution to secure the IoT on the gateway. A hybrid AIS solution combines multiple AIS techniques for malware detection to achieve a better detection accuracy rate. However, the IoT gateway is the main connection point for the IoT devices, so a downside to implementing a security method on the IoT gateway is that it could be a single point of failure. This obstacle could be overcome by having a backup security solution.  
Second, conducting a quantitative analysis by calculating the detection accuracy rate and the F1-Score to evaluate given security solutions is another promising research direction. Using only a particular dataset to validate the results might not be sufficient for certain system architecture. Therefore, we suggest using different datasets conducted using different network scenarios and employing different file features to evaluate malware detection methods in the IoT.  
The third promising research direction is as follows: to evaluate a security solution's ability to detect unknown malware files, the solution should be implemented in an actual IoT network. Creating different IoT system scenarios with different setups and processing power is key to evaluating a security solution in real time.
# Plant-Origin Additives from _Boswellia_ Species in Emulgel Formulation for Radiotherapy Skin Care
## 4 Discussion
### Composition and Characterization of Formulations
#### 4.1.1 Visual Assessment and pH  
In the literature, the use of _B. serrata extracts_ in emulgels as topical delivery systems (drug) was studied by [PERSON] et al. [33]. The _Boswellia-serrata_-loaded emulgel (BS-loadedemulgel) was an anti-rheumatoid agent. Carbomer was used as a hydrogelator. The obtained formulations were creamy, with a smooth homogeneous appearance. Also in this study, the produced emulgels were characterized by a creamy consistency and white color, which is a desirable feature in preparations for sore, damaged skin [34].  
The odor of the obtained formulations was intense and herbal. At a later stage of modifying the formulation's recipe, the odor should be slightly eliminated as it could disturb people using this preparation after radiotherapy, for whom strong odors may enhance the effect of nausea.  
According to [PERSON] et al. [35], the pH of skin subjected to radiotherapy is close to the pH of non-irradiated skin, whereas in the area of the treatment wound, the pH increases significantly and is higher (above 7.5) than that of non-irradiated skin (pH in the range of 4.5-6.7). pH-gradients play a crucial role in recruiting keratinocytes inwards from the wound edges. Also, the radiation-induced damage of epidermis and adnexal structures may impair the maintenance of an acidic pH in the stratum corneum. The alkalization of the stratum corneum following radiotherapy may impair skin barrier function and may also impair wound healing and predispose radiation-damaged skin to bacterial (especially _Staphylococcus aureus_) and fungal (_Candida albicans_) infections, which grow better at neutral pH [35]. Thus, it is important that formulations used in skin care after radiotherapy have an acidic pH [36], which is consistent with our results. The acidic pH of the prepared formulations containing plant-origin additives from _Boswellia_ species is also consistent with the results of research by [PERSON] et al. [33]; the obtained emulgels had a pH of 6.3-7.09. In this case, _Boswellia serrata_ extract was added to the aqueous phase at a concentration of 1%.
# Mobility-Multihoming Duality
## 7 Discussion
### Mobile Servers  
If a server is mobile and changes its connectivity (which could also be due to dynamic multihoming), the remote hosts that try to access it must be able to resolve at least one of its current IL-Vs in order to connect to it. ILNP has defined DNS records [68] that are supported in commercial software such as BIND. However, when an L64 value for a server changes, it needs to be updated within the DNS entry for that server. The DNS already has a mechanism defined for a Secure Dynamic Update [69] for performing such updates, and it is widely implemented. However, this would need to be enabled, configured, and maintained for those sites that wanted to have mobile servers.
# Cooperative Opportunistic Pressure Based Routing for Underwater Wireless Sensor Networks
## 4 Simulations and Results
### Aus
On average Hydrocast makes 3.1 transmissions for delivering a data packet to the sinks deployed at the surface. Whereas, with the increase of nodes, the number of transmissions decreases that shows that Hydrocast performs better in the sparse deployment. In our proposed schemes, the deployment of sensor nodes at strategic locations in the network, not only reduce the long recovery paths when the network density is low but also help some other candidate nodes to transmit their data to the destination in less number of transmissions. The number of hops are less in our schemes because they use AUVs to gather data from sensor nodes. Average number of hops in Improved Hydrocast are 3.05 which is slightly better than Hydrocast. Co-Improved Hydrocast also uses strategically deployed nodes, but due to opportunistic cooperation, the average number of transmissions for a data packet delivery slightly lower than the Improved Hydrocast, Co-Hydrocast, and Hydrocast. When network is sparse, its average number of hops are less (2.8 transmissions) which increases as the density increases because it starts establishing shorter routing paths to conserve the network energy however still less than the other compared schemes. The average propagation delay is plotted in Figure 15. Co-Hydrocast has higher latency than Hydrocast due to cooperative transmission, while both suffer from lengthy recovery paths issue in sparse networks which yields high average delay. In Hydrocast, when the network is sparse, the average propagational delay is 2.25 s and in Co-Hydrocast it is 2.6 s due to relay cooperation in order to obtain signals within the predefined SNR. As the network density increases, the average propagational delay also increases due to increase in number of collisions that results in path loss and increases the delay. Improved Hydrocast outperforms the compared scheme and has lower propagation delay (2.10 s) due to short path from the source to the destination with the help of strategic deployment of nodes. Its delay increases due to short paths that increase the number of hops between source and the destination resulting in more delay however, it has lowest delay amongst all the compared existing scheme and proposed schemes. Co-Improved Hydrocast also takes advantage of the strategic  
Figure 13: Average end to end energy consumption. locations however due to cooperation, it has slightly higher average propagation delay than Improved Hydrocast scheme. Cooperative techniques show improvement in the throughput, however at the cost of propagation delay as shown in Figure 15.
# Collision Avoidance Geographic P2P-RPL in Multi-Hop Indoor Wireless Networks
## 3 Collision Avoidance Geographic Point-to-Point RPL
### Software Architecture  
We implemented the Collision Avoidance Geographic P2P-RPL in Contiki OS [36]--an open source operating system for LLNs and IoT. We made use of the ContikiRPL implementation [37] and P2P-RPL implementation [38].  
The software architecture of Collision Avoidance Geographic P2P-RPL is illustrated in Figure 6. The arrow in Figure 6 depicts the P2P-DIO packet flow.  
The key components are as follows:  
Figure 4: Example of a Collision Avoidance Elastic Trickle operation, \(k=1\).  
Figure 5: Trickle operations of (**a**) Collision Avoidance Geographic P2P-RPL, (**b**) P2P route discovery with only the location-constrained forwarding zone and (**c**) P2P-RPL, \(k=1\).  
* RPL ICMP packet management communicates with ICMPv6 to send/receive RPL control messages including MO, P2P-DIO, and P2P-DRO. When the ICMPv6 message is received, the RPL ICMP packet management component first checks the type of message and extracts the payload. Then, it notifies RPL DAG management component. Cost measurement is implemented inside this component.
* RPL DAG management contains all the logic related to DAGs with the IPv6 routing table management and the preferred parent determination based on the rank result of the RPL objective function component.
* The RPL Objective Function is the objective function that calculates the routing metric to select the parents. In ContikiRPL, OF0 [39] and MRHOF [40] are implemented.
* Cooperative Self Localization includes the B-box algorithm with utilizing UWB-based ranging measurements and particle filter-based inertial navigation. It receives the location states of one-hop neighbors, which are extracted by the RPL ICMP packet management component. After estimating the current location state of a node, it forwards the result to the location-constrained forwarding zone component.
* The location constrained forwarding zone is responsible for deciding whether to join the temporary DAG formation advertised by the incoming RPL ICMP packet (i.e., P2P-DIO). Given the location information extracted from the incoming packet, this component computes the four corners of the location-constrained forwarding zone \(\mathbf{B}_{fud}\). It drops the packet if there is no intersection between its estimated region and the forwarding zone.
* The Collision Avoidance Elastic Trickle Timer holds the calculation of the distance and angle difference and the dynamic selection of the listen-only period based on the transmission priority constant \(s\) in addition to standard trickle-related logic.  
Figure 6: The software architectureof Collision Avoidance Geographic P2P-RPL on Contiki OS.
# Evaluation of Low-Cost GNSS Receiver under Demanding Conditions in RTK Network Mode
## 2 Materials and Methods  
### Low-Cost U-Blox Global Navigation Satellite System (GNSS) Receiver  
The C099-F9P application board has been used to test the u-blox ZED-F9P GNSS receiver. It is a two-frequency, four-system receiver--it receives GPS (L1C/A, L2C), GLONASS (L1 OF, L2 OF), Galileo (E1B/C, E5b) and BeiDou (B1I, B2I) signals [22]. It offers RTK and RTN operation with high frequency (up to 20 Hz) and accuracy (\(\pm\)1 cm + 1 ppm). In conditions of good satellite visibility, the receiver quickly resolves its position (cold start \(<\) 24 s, reacquisition \(<\) 2 s). Also, anti-jamming and anti-spoofing algorithms are implemented into the receiver, allowing the assumption that it can discard unwanted signals. It has a wide operating temperature range, low power consumption, light weight and a large number of physical inputs/outputs and communication capabilities. The parameters of this device declared by the manufacturer, its price, as well as programming libraries available on the Internet, provide great opportunities for testing this receiver.
## 2 Architecture of HEVs/PHEVs  
To fully understand the potential of HEV/PHEVs as an approach for sustainable transportation, it is important to examine the architecture of HEV/PHEVs and the EMS that govern their performance. EMS are the core determinant of HEV/PHEV performance and are closely linked to the vehicles' architecture. Thus, we will provide a comprehensive overview highlighting the key components that make up these vehicles and their respective roles in the energy management process.  
Generally, the structure of HEVs/PHEVs offers additional flexibility to optimize their engine operation regions compared with ICE vehicles, as the latter can only adjust their engine speed to regulate their torque in response to a driver's power demand. The key characteristics of HEV/PHEVs which are different from the ICE vehicles, are listed as follows [3]:  
* Recover the regenerative braking energy as much as possible;
* Reduce the idling energy cost by turning off the engine;
* Achieve an optimal distribution of power among various power sources;
* Reduce the size of the ICE while ensuring that the vehicle's maximum requirements are still met;
* Tend to be more complex and costly, as they necessitate additional controllers;
* Have a weight that is 10-30% greater than that of ICE vehicles.  
As depicted in Figure 2, a motor assists the engine to operate in a higher efficiency area in an HEV/PHEV, which is able to achieve better fuel efficiency. To accomplish this, HEVs/PHEVs need to distribute power among various power sources (e.g., the engine and battery) in response to varying driving conditions. Typically, there are three types of HEV powertrains, which are also utilized in PHEVs, except that PHEVs have a charging port that allows the battery to be charged directly from the grid. The three types are series hybrid, parallel hybrid, and combined(series-parallel) hybrid, respectively, [4]. In a series hybrid powertrain system, a motor/generator set is powered by the engine to drive the vehicle; in a parallel hybrid system, either the battery with a motor/generator set is used or the engine is used to drive the vehicle according to the torque demand; In a combined hybrid system, vehicles have the ability to operate as a series or parallel hybridization.
# Compact Spatial Pyramid Pooling Deep Convolutional Neural Network Based Hand Gestures Decoder
## 8 Comparison with Other Methodologies  
Modern deep learning-based hand gesture classification research has been solely focused on accuracy-based classification and regression. Depending on the image dataset, the size and computational complexity of such classification algorithms are increasing rapidly. In this study, we have introduced a new real-time approach to classical gesture decoding. [PERSON] et al. [26] approached DCNN-based classification while using the same dataset as used in this research and reported 94% accuracy over all input data. However, that method involved a background subtraction process along with data image resizing and gray-scale conversion. Moreover, integrating a multi-support vector machine (MSVM) into the DCNN might further improve classification accuracy, but ultimately the algorithm lacks practicality for real-time applications. [PERSON] et al. [40] approached gesture recognition in a similar fashion as the present research, but the image resizing and limitation of gesture classes to numerical hand gestures limits its utility in real-time applications. Our previous approach to node pruning with a DCNN yielded a similar increase in prediction response time, but the image resizing restriction was still present [43].  
The proposed compact SPP-based DCNN model eliminates the image resizing restriction. We utilized a large ASL dataset to create a new gesture or ASL finger-spelling decoding algorithm that works in real time. Although the proposed pruned model with the SPP layer achieved the same accuracy as the latest relevant research using this dataset, the combination with the newly proposed video-based decoding upgraded the result to a maximum of 99% accuracy in real time with very low processing time. All of the previous algorithms focused on accuracy improvement. In this research, we have combined the improved accuracy with a novel algorithm to achieve a new benchmark system that provides a practical technique for the development of future applications that are based on gesture recognition.