esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Work  
In this paper, an innovative approach is proposed, which adapts sketchy data structures for extracting generic and universal features, and leverages the principles of domain adaptation to improve classification quality in zero- and few-shot scenarios. The experiments and the reported results enable us to conclude that the proposed mechanism can be successfully used in difficult anomaly detection and threat identification scenarios. Although the datasets utilized during the experiments were essentially different in terms of tools (e.g., used for recording the datasets traffic traces), and the techniques used by the adversaries to implement the attack, it was possible to extract common attack patterns that can be successfully used to detect abnormal behaviour of network elements. Although the presented \"domain adoption\" mechanism has already been demonstrated to be a useful tool, the authors plan to further enhance it using additional information sources. In particular, the authors believe that by analysing multi-domain network-flow-based knowledge transfer, it will be possible to extract general patterns that can help form better model decision boundaries when transferring knowledge across various domains.  
Conceptualization, methodology, software, validation, investigation, writing, M.K. and R.K.; formal analysis, review and editing, project administration, M.P. and M.C. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples** & **Class** & **Recall** & **Precision** & **F1-Score** \\ \hline
100 & Benign & 0.9122 & 0.9500 & **0.9307** \\  & Anomaly & 0.9478 & 0.9085 & **0.9278** \\ \hline
500 & Benign & 0.9497 & 0.9730 & 0.9612 \\  & Anomaly & 0.9723 & 0.9485 & 0.9603 \\ \hline
1000 & Benign & 0.9875 & 0.9880 & 0.9878 \\  & Anomaly & 0.9880 & 0.9875 & 0.9878 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Few-shot scenario. Model trained on the SIMARGL dataset (with N additional samples from IoT-23), and tested on IoT-23.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Average F1-Score** \\ \hline Proposed method & **0.99** \\ Adversarial Autoencoders + KNN [31] & 0.97 \\ BiGAN + KNN [31] & 0.97 \\ AdaBoost [24] & 0.83 \\ SVM [24] & 0.59 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of methods.  
**Funding:** This work is funded under the APPRAISE (fAcilitating Public Private secuRity operAtors to mitigate terrorIsm Scenarios against soft targEts) project, with the support of the European Commission and the Horizon 2020 Program, under Grant Agreement No. 101021981.  
**Data Availability Statement:** The study was performed on open, public benchmark datasets.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Quantitative Analysis  
The FAME yields were analyzed by GC (Agilent 6890N, Aligent, Beijing, China) with flame ionization detection equipped with a capillary column (Agilent HP-5, 30 m \(\times\) 0.25 mm ID, film thickness 0.25 \(\mu\)m, Aligent, Beijing, China). The injector and detector temperature of GC was set at 250 \({}^{\circ}\)C. The temperature program was started at 80 \({}^{\circ}\)C and then increased to 175 \({}^{\circ}\)C at a rate of 20 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\), plateauing for 1 min. The rate was decreased to 4 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) from 175 to 235 \({}^{\circ}\)C and kept constant remained for 9 min. Helium was used as the constant carrier gas. Qualitative analysis of the major FAME (methyl palmitoteate (16:1), methyl palmitate (16:0), methyl all-cis-5,8,11,14,17-eicosapentaenoate (20:5) was based on individual FAME peaks and quantitative study of the major FAME was using a five-point calibration curve (0.5-2.5 mg\(\cdot\)mL\({}^{-1}\)) obtained by a series of FAME standards with methyl heptadecanoate (17:0) as internal standard.
# Smart Healthcare Network Management: A Comprehensive Review
## 5 Reference Architecture for Smart Healthcare System Network Management
### Management Interface Layer  
This layer defines the network management interfaces, usually referred to as the southbound interface, needed to connect the management station with the management agents (i.e., managed devices). In the traditional Internet, several protocols can be used for this purpose, such as SNMP. For the WBAN and IoT segments, there is no specific interface protocol defined for network management. Existing management protocols are not appropriate for resource-constrained WBAN/IoT devices. In the literature, there are several attempts to define these interfaces. Some of these attempts suggested using SNMP to manage sensor nodes [57], while other work suggested using SNMP over traditional IP networks and using other protocols over WBAN/IoT network segments and then linking the two segments through a proxy [56]. [PERSON] et al. [54] proposed a lightweight RESTful web service approach to enable device management of wireless sensor devices, motivated by the recent development of 6 LoWPAN and CoAP protocols for resource-constrained devices. They developed a prototype and demonstrated that the proposed solution was efficient and effective for managing wireless sensor devices. Another work [92] investigated how existing IP-based network management protocols, SNMP and NETCONF, can be implemented on resource-constrained devices. They conducted a resource requirement analysis of SNMP and NETCONF on an 8-bit AVR-based device. They implemented light versions of the SNMP and NETCONF protocols and identified some challenges in messageprocessing, session establishment and maintenance, and security. In their implementation, the authors did not mention how they implemented MIBs and how they could be used in resource-constrained devices.  
Other widely used lightweight machine-to-machine IoT protocols are MQTT (Message Queuing Telemetry Transport) [93] and oneM2M [94]. MQTT is a publish/subscribe messaging protocol that is extremely simple and lightweight. It is designed for constrained devices and low-bandwidth, high-latency, or unreliable networks. It can be used in sensor networks and bridged to SNMP-enabled IP networks [95]. The oneM2M protocol is a global initiative to standardize a common M2M service layer platform for globally applicable and access-independent M2M services. The current M2M solutions are proprietary systems, which make them difficult to extend to support new services, integrate new data, and interoper with other M2M systems [96]. [PERSON] et al. [97] proposed a oneM2M-based IoT system for Personal Healthcare Devices (PHDs). They implemented a protocol to convert between ISO/IEEE 11073 [98] and oneM2M protocol messages. The conversion protocols are deployed in gateways located between the PHD and the management server. Bedside monitoring systems in hospitals use the ISO/IEEE 11073 standards. With minor modifications, the ISO/IEEE 11073 standards can be successfully applied to wearable, wireless, point-of-care systems in home healthcare [99].
### Glucosinolates and Isothiocyanates in SO  
As in other plants, glucosinolates (GLSs) are present in SO with their corresponding ITCs, formed by enzymatic hydrolysis by myrosinase and/or by chemical hydrolysis that could occur during the plant manipulation, storage and processing. The formation of ITCs from GLSs via a Lossen's type rearrangement has been extensively reviewed [9; 10; 11]. The relative content of GLS and ITC is often difficult to establish due to the dynamic equilibrium between the two classes of compounds and to the very different chemical features of these functional groups; GLSs are quite polar and water soluble, while ITCs are mainly low polar and volatile compounds. Analytical methodologies for their determination are therefore different; UV spectroscopy and/or HPLC are mostly used for GLSs analysis, while gas chromatography-mass spectrometry (GCMS) is more suitable for volatile ITCs.  
The literature [12] identifies glucoputranjivin (compound **1**, Figure 1) as the main glucosinolate in SO; its corresponding isothiyanate is isopropylisothiocyanate (IPITC, compound **2**).  
In addition, 2-butylisothiocyanate (2-BITC, compound **4**) from glucochoclearin **3** has been previously identified in SO [5] by the GCMS of hydrodistillates. The presence of sinigirin **5** and its corresponding allylisothiocyanate (AITC) **6** is controversial; they have been identified in the fresh plant [13] but in a following report, they have been stated to be absent in SO [14]. Nevertheless, sinigirin **5** is the only commercially available standard for GLS and therefore is universally used as the reference compound for the qualitative and quantitative determination of GLSs in SO extracts [12], used also in the French pharmacopea method [15].  
Figure 1: Chemical structures of compounds **1–6** reported in this paper.  
AITC 6 from sinigrin is the molecular marker of sinapis; it is responsible for the pungency and irritation of many food plants including mustard, wasabi and many others. AITC is also the reference compound to test the activity of chemicals on the TRPA1 somatosensory channel.
###### Abstract  
(1) Background: since the technologies of anesthesia and surgery were advanced, video-assisted thoracic surgery (VATS) under local anesthesia (LA) has been widely carried out and is considered a robust surgical technique to prevent the recurrence of pneumothorax in patients with recurrent primary spontaneous pneumothorax (PSF). In this study, postoperative clinical outcomes were compared to evaluate the feasibility and efficacy of VATS under LA compared with general anesthesia (GA) in patients with PSP. (2) Methods: 255 patients underwent wedge resection underwent VATS for PSP in our hospital from January 2014 to June 2019. Of them, 30 patients underwent the operation under LA and the others underwent the operation under GA. Except for the anesthesia method, the same surgical technique was adopted for all patients. All medical records were retrospectively reviewed. (3) Results: the total operation time and total hospital days were relatively shorter, post-chest tube drainage was significantly shorter (0.04), and visual analog scale (VAS) scores in the outpatient clinic were significantly lower in the LA group than in the GA group (\(p=0.01\)). The incidence of postoperative recurrence after discharge in the LA group (3.3%) was also lower than in the GA group (18.67%) (\(p=0.001\)). In the LA group, there were no cases of conversion to intubation. (4) Conclusions: our results showed relatively better clinical outcomes in VATS under LA with sedation than under GA in the treatment of PSP. Hence, LA with sedation can be considered as a robust anesthetic technique for VATS and as applicable in the surgical treatment of PSP.  
primary spontaneous pneumothorax (PSF); local anesthesia (LA); general anesthesia (GA); clinical outcome +
Footnote †: journal: Applied Sciences
# Extracting Weld Bead Shapes from Radiographic Testing Images with U-Net
### Research Methods  
This study used the deep learning-based U-Net [8] algorithm to automatically examine weld bead shapes in radiographic testing images and determine the shapes and locations of the beads for detailed defect classification. For increased accuracy of the bead detection, image preprocessing was performed via histogram equalization [9] to improve the contrast of the images before the model training. Furthermore, to diversify the data and improve the deep learning performance, data augmentation was performed to artificially expand the image data [10]. It was checked whether the performance of deep learning was improved through training results before and after image preprocessing and data augmentation were applied. The weld images were captured using radiographic testing, and the radiographic images were marked with information to classify and distinguish them. For training, mask images were created by performing a labeling process that removed areas other than the bead area from the weld images. The weld images and the data with labeled bead areas were combined to create the training dataset to train the bead areas, such that the bead shape extraction could be automated. The images that constituted the dataset were divided into training and testing images to determine the training accuracy. The images had a variety of pixel sizes of approximately 1030 \(\times\) 300 or greater. It was found that high-quality images reduced the learning speed. To efficiently train the images, the image pixel sizes were reduced to 256 \(\times\) 256, then training was performed [11]. After completing the training, the shapes and locations of the predicted weld beads were compared to those of the actual beads. Image post-processing was performed from the predicted bead shape and location image to extract only the weld bead.
## 2 BIM and DT Idea and Applications
### Development of the BIM and DT Applications--Key Challenges and Gaps
#### 2.1.2 Design Challenges and Gaps  
It is possible to encounter difficulties at any stage of the integration process. One challenge is the multitude of file formats and data exchange standards used in various BIM software programs and DT platforms. One of the key priorities in the development of tools to translate data between different formats is to ensure that this process is as efficient as possible. It is inevitable that information will be lost when data are transferred between stages [52].  
Another problem is the detail and breadth of the projects. BIM and DT models that are too detailed can be too heavy and time-consuming to manage. Models that are too detailed may not provide sufficient information to enable decisions to be made. There are serious difficulties in adapting the level of granularity to the needs of a given stage of a project. One potential solution may be LOD (level of development) models, defining levels of detail for BIM models, as discussed in [56]. This concept is considered in the context of implementing the FM strategy in buildings and direct support for facility managers to address the challenges of information reliability, interoperability, and usability related to the service and operation of building infrastructure [57]. The advancement of this tool could also be considered and used in a DT environment.  
In the context of designing building infrastructure and building automation systems supported by BIM and DT tools, it is important to consider the problem of fragmentation and granularity that arises from the technical nature of these systems [58]. Modern BACS are distributed in nature, and assuming their integration with IoT solutions, other fieldbus networks, and edge-level modules [2; 10; 54; 59; 11], the granularity will increase, which is a problem and a challenge in building effective BIM but also in the organization and integration of data acquisition structures for DT, as discussed in [60].
## 1 Introduction
Hence, the driving force behind this study is to explore the practicality of the hardware implementation of optical transceivers and optoelectronic interfaces. The goal is to develop a VLC system that can be utilized for various use cases, including the transmission of UHD video in a passenger van. With this in mind, a conceivable use case of this VLC system intends to leverage the reading lights pre-installed on the interior roofs of vehicles. This aims to equip public transportation with the ability to provide passengers access to UHD audio-visual content on handheld devices such as laptops, tablets, and smartphones. In order to play these audio-visual contents, users are required to connect a purpose-built transceiver or dongle to their devices' USB port. The audio-visual content must be stored on a server that is accessible within the vehicle. The proposed system encompasses two principal components: (i) a LiFi router and (ii) a USB dongle, as graphically represented in Figure 1.  
In the ensuing sections, the components and methods utilized in the development of the optoelectronic interfaces will be outlined. Initially, the proposed optoelectronic interface system will be detailed, covering its application in both the downlink and uplink. This will be followed by a more thorough elaboration on the design and implementation of the system components, including the LED and photodiode (PD) driver. Subsequently, we will describe the experimental set-up, testing procedure, and the system's deployment in a passenger van. Finally, the most representative measurements characterizing the system will be presented, leading to the final conclusions.
# Pharmacokinetics of Marine-Derived Drugs
## 3 Pharmacokinetics Studies in Animals
### Elimination
#### 3.4.5 Marine Sponge-Derived Compounds  
The elimination of ilimaquinone (**15**) (IQ) after peroral (10 mg/kg) administration to rats was relatively quick (T\({}_{1/2}\) = 1.2 h, Cl = 316 mL/h/kg). However, IQ was still detectable 24 h post-administration [115]. The elimination of IQ and epi-IQ after i/v injection of their mixture was much faster (T\({}_{1/2}\) = 0.4-0.6 h) compared with peroral administration. The Cl for epi-IQ was three-fold greater compared with IQ (Table 2). Two hours after i/v injection, the concentrations of both IQ iso-forms were below the detection limit (Table 1). The elimination half-life time for IQ after peroral administration of the mixture was prolonged by approximately three-fold compared with the peroral administration of IQ alone. Although the T\({}_{1/2}\) values for both iso-forms were equal after mixture administration, the clearance of epi-IQ was much greater (Table 2). The absolute bioavailability after peroral administration of the mixture was calculated as 38% and 13% for IQ and epi-IQ, respectively [46]. The results of this study highlight the importance of pharmacokinetic investigations of stereoisomers. This study indicated that ilimaquinone shows higher absorption and faster elimination compared with its epi-form. This specificity must be considered in the formulation development phase.  
The alkaloid manzamine A (**16**) circulated in the blood for a long time after i/v injection, with T\({}_{1/2}\) of 53.7 h and a slow Cl of 5.1 mL/min/kg. About 100 ng/mL of manzamine A was detected in plasma 96 h after both administration routes. The absolute bioavailability after peroral administration to rats was calculated to be 20.6%. This reasonable bioavailability could be explained by the metabolic stability and good absorption of manzamine A [56]. The results of pharmacokinetics investigations (long T\({}_{1/2}\) and low plasma clearance) are important for pharmacodynamic implications considering the previously reported promising antimalarial activity of this compound [119].
## 5 Discussion
### Analysis of Typical Landslides
#### 5.4.1 Hujiayuan Landslide  
Two time series on the trailing edge and leading edge of the Hujiayuanzi slope are extracted and shown as points P1 and P2 in Figure 16a. The cumulative deformation of point P1 detected by ALOS/PALSAR\(-2\) is \(-64.54\) mm, and that of Sentinel\(-1\)A is \(-44.01\) mm, with a difference of 20.53 mm. Meanwhile, the deformation of point P2 detected by ALOS/PALSAR\(-2\) is \(-36.25\) mm, and that of Sentinel\(-1\)A is \(-23.35\) mm, with a difference of 12.9 mm. In order to make a more intuitive comparison, the consistency analysis of the time series trend was performed after the time difference was removed from the cumulative deformation of the two data. Figure 16d shows the cumulative time-series results after the time difference was removed at point P1. High consistency can be observed in the monitoring results, and the trend in the deformation of point P1 is close to linear. Figure 16e shows the time-series results of point P2 after the time difference was removed. High consistency is also observed here. The trend in the deformation of the landslide is relatively steep before January 2021 but relatively slower after that. It is clear that in areas with relatively low vegetation coverage, the deformation trends detected by the two data sources are relatively consistent. The time-series results verified that the deformation rate detected by Sentinel\(-1\)A is generally lower than that detected by ALOS/PALSAR\(-2\) because of the unavailability of summer monitoring data.
## 4 Results of the Information Sources Review
### Courses Review Results  
In this section the results of the courses review are presented. Figure 2 shows the number of courses for each country.  
The courses are part of twenty-six bachelor's degree and thirty-eight master's degree programs in which partners and associate partners from the PLANET4 consortium are involved, for a total of sixty-four courses. As these data come from several countries worldwide, it could be considered a significant sample to infer the current situation of university training programs.  
The semesters in which the AI, IoT, and EC courses are delivered in bachelor's and master's degrees are both presented in Figure 3. The data collected shows a large difference in the semester of delivery of the courses related to Industry 4.0 between bachelor's degree (median = 6, minimum = 3, maximum = 8, first quartile = 5, third quartile = 7) and master's degree (median = 1, minimum = 1, maximum = 3, first quartile = 1, third quartile = 2). Since the bachelor's degree in all the countries of the selected universities lasts three to four years, it can be observed that all the courses are preferably delivered at the end of the degree program, whereas in the master's degree this type of courses are offered in the beginning of the degree programme.  
The number of AI, IoT, and EC university training programs analyzed is presented in Figure 4. The most taught discipline is AI (78% of courses), where IIoT and EC are less taught. Observing how the disciplines are taught together, IIoT and EC are frequently addressed in courses where another discipline is delivered.  
The main programming languages for AI, IoT, and EC courses are presented in Table 1. In the AI area, the selected courses use more Python, Matlab and R programming languages, while in the IoT area, R programming language is used more often and in the EC area the courses use mainly Python programming language.  
Figure 4: Percent of university AI, IoT, and EC training programs (total 64).  
Figure 3: Semesters in which the courses are delivered.  
Figure 2: Number of courses per universities’ country.  
The main software applications, environment, or services used in the analyzed courses are presented in Table 2. The main environment used in the AI area courses is Matlab, but there is also significant variability in the software used. There is little evidence to show a preference in environment choice in the IoT and EC area courses, although Azure services and Arduino SDK are mainly used. Teaching methodologies applied in the selected training are presented in Table 3.