esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Long-Short-Range Communication
### Long-Range Communication
#### 5.1.3 Narrowband IoT  
Narrowband IoT technology functions in a GSM-licensed band or LTE and covers a long range with low power required for communication. It relies on LTE (long-term evolution), and its hardware component and is used for effective and rapid deployment [78]. Narrowband IoT can be implemented in different ways and can coexist with available networks. The implantation node is available in standalone, in-band and guard-band forms.  
In standalone mode, carriers of GSM are reframed and deployed for narrowband IoT, entering a newly available bandwidth. In the case of the guard band, it follows the bandwidth of the LTE carrier existing in the guard band. Finally, in the in-band mode, it reserves the physical resource of LTE [79; 80]. One significant advantage of narrowband IoT is that it functions inside the licensed band and has less risk of interference. Its disadvantages are the higher cost of narrowband IoT compared to unlicensed standards. Devices require a connection fee to use LTE, but a decrease in interference makes this technology suitable for use in healthcare systems as QoS. It achieves a high sensitivity of about 164 dB, which covers a long range of up to 15 km with an uplink data rate of 250 kbps [81]. Because of the wide range and significant data rate, it is suitable for healthcare applications. The lifetime of battery functions at 5 wh was found to be 2.3 years in standalone devices, and a 50-byte message can be sent every 2 h in in-band mode, increasing the life of the battery up to 18 years.  
Various applications depend on long-term tracking, which requires multiple transmissions per day, whereas emergency tracking systems send short \"heart beat\" information occasionally. In emergency scenarios, longer messages are required to track the exact status of a patient in real time. Narrowband IoT consumes less energy and therefore requires less interaction by the wearer, supporting a minimum of 52 of 548 nodes in a base station and 40 devices per home, which corresponds to a density equal to that of London, where every person could wear fifteen sensors and communicate successfully with the closest base station. Many other low-power standards have been developed for unlicensed and sub-GHz bands; however, fewer such standards have been deployed compared LoRa WAN and Sigfox. They consist of unique hardware features making them suitable for deployment on wide scales, including weightless, symphony link, narrow band-Fi and nWave.
## 5 Conclusions  
Overall, a total of nine candidate reference genes were selected from the RNA-seq data of \(L\). sargentiae based on their FPKM values in this study. The expression stability of all reference genes was validated under _F. oxysporum_ inoculation by three different statistical algorithms (geNorm, NormFinder, and BestKeeper). Among them, _HIS_ exhibitedthe highest level of stability. The expression patterns of four _F. oxysporum_-resistant genes normalized by HIS were consistent with their responses to pathogenetic fungi in other plants. It indicates that _HIS_ can be used as a suitable reference gene for the standardized qRT-PCR analysis of _F. oxysporum_-resistant genes in _Lilium_. Our findings will help provide comprehensive guidelines for identifying suitable reference genes in other lily species and also contribute to further explorations of the molecular mechanisms in _Lilium_ in resisting _F. oxysporum_.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13063395/s1](https://www.mdpi.com/article/10.3390/app13063395/s1)]([https://www.mdpi.com/article/10.3390/app13063395/s1](https://www.mdpi.com/article/10.3390/app13063395/s1)), Table S1: Candidate reference genes and target genes in RNA-seq libraries of _L. Sargentine_ induced by _F. oxysporum_ and sterile water, respectively.  
Conceptualization, J.W. and Y.Z.; methodology, L.M., Q.D. and G.C.; formal analysis, L.M., Y.Z. and W.J.; software, X.L. and W.D.; validation, L.M. and G.C.; investigation, W.J., Q.D., X.L. and W.D.; resources, Q.D., G.C. and X.W.; writing--original draft preparation, L.M. and X.L.; writing--review and editing, G.C., X.L. and W.D.; supervision, J.W. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (Grant No. 2019 YFD1000400, 2020 YFD10004), the Green Food Brand-Build a Special Project (Floriculture) supported by Yunnan Provincial Finance Department (53000021000000013742), the Joint Lab of Yunnan Seed Industry (202205 AR070001-10) and Open Fund of National Engineering Research Center for Ornamental Horticulture, and Yunnan Key Laboratory of Flower Breeding (FKL-202103).  
Not applicable.  
The authors declare no conflict of interest.
## 5 The Role of Non-Protoozoa Microbial Species in _L. pneumoniae_ Biofilm Colonization  
Environmental biofilms often contain several different bacterial species [86]. These bacterial species may promote the persistence of _L. pneumoniae_ in biofilms, while other species inhibit _L. pneumoniae_'s colonization (Figure 3). For example, _Flavobacterium breve_ and cyanobacterial species can promote _L. pneumoniae_ growth and colonization in biofilms by providing a source of nutrients [87,88]. _In vitro_, the growth of _L. pneumoniae_ is necrotic when heat killed _Pseudomonas putida_ bacteria are given as a nutrient source, however heat killed Gram-positive organisms such _Bacillus subtilis_ and _Lactobacillus plantarum_ did not alter the growth of _L. pneumoniae_, suggesting that _L. pneumoniae_ is capable of replicating without the presence of protozoan species, and that necrotic growth of _L. pneumoniae_ is restricted to certain microbial species [27].  
Summer seasons, which coincide with legionellosis outbreaks, favour the proliferation of _L. pneumoniae_ in cooling tower microbial populations while other _Legionella_ species decrease in number [89]. Based on this shift in abundance it has been hypothesized that _L. pneumoniae_ may inhibit the growth of other _Legionella_ species. In fact, _L. pneumoniae_ produces a surfactant secreted by the protein TolC, which is toxic to other _Legionella_ species, but has no effect on _Pseudomonas aeruginosa_, _Klebsiella pneumoniae_ and _Listeria monocytogenes_[90]. Therefore it is tempting to speculate that _L. pneumoniae_ may influence the growth of other _Legionella_ species in their natural environment.  
One of the most studied bacteria that can influence _L. pneumoniae_'s biofilm colonization ability is _P. aeruginosa_. Although there is a body of evidence suggesting that _L. pneumoniae_ can coexist in biofilms with _P. aeruginosa_, these studies were performed with inoculums from natural environmental sources, which may contain several different bacterial species [11,38]. In contrast to these studies, monospecies biofilms with _P. aeruginosa_ were shown to prevent _L. pneumoniae_ colonization [26,91]. This phenomenon may be mediated by acylhomoserine lactones (AHLs) produced by _P. aeruginosa_ as these AHLs not only inhibit the growth of _L. pneumoniae_ but also its biofilm production [92]. Furthermore, specific AHLs produced by _P. aeruginosa_ can downregulate Lcl production, which is essential for biofilm formation in _L. pneumoniae_[62]. Interestingly, the _in vitro_ inhibition of _L. pneumoniaephila_ colonization by _P. aeruginosa_ is alleviated if _K. pneumoniae_ is present in the produced biofilm [91]. In fact complex multispecies biofilms that contain both _P. aeruginosa_ and _K. pneumoniae_ are permissive for _L. pneumoniae_ colonization [28]. The presence of amoeba seems to also effect whether _P. aeruginosa_ is antagonistic to _L. pneumoniae_ colonization, as biofilms which contain both _Acanthamoeba castellani_ and _P. aeruginosa_, increase the uptake of _L. pneumoniae_ within _A. castellanii_, and the colonization of _L. pneumoniae_ in biofilms [93].
## 3 Frequency Coordination Control Strategy at LCC Terminal
### AGC Controller Parameter Optimization Using DQN Algorithm
By introducing the target network, the correlation between the current Q-value and the target Q-value can be effectively suppressed. It can avoid the difficulty of network convergence during iterative training and greatly improve the convergence speed and stability of the algorithm. By using DQN, the control parameters of AGC can be optimized. The state space \(s_{t}\) is as follows:  
\[s_{t}=(t,K_{P},K_{I},\Delta f,\Delta P), \tag{14}\]  
where the state space includes the current period, \(t\), and the PI parameters \(K_{\text{P}}\) and \(K_{\text{I}}\) of the AGC controller. \(\Delta f\) is the frequency deviation, and \(\Delta P\) is the active power deviation. Action space \(\alpha_{t}\) using DQN is as follows:  
\[a_{t}=(\Delta K_{P},\Delta K_{I}). \tag{15}\]  
Since the main task of the AGC is to keep the frequency in the area stable and the active power of the DC line within the planned value after a disturbance, the ITAE performance metrics are selected to construct the reward function \(r_{t}\), which is as follows:  
\[r_{t}=-\int_{0}^{T}t|\alpha|\Delta f|+\beta|\Delta P_{dc}||dt. \tag{16}\]
## 7 Using IETF Standards to Realize the Internet of Things
### Interoperability  
One of the key factors to ensure the widespread use of IoT devices is to support end-to-end interoperability between different devices. Currently interoperability between embedded devices is enforced by use of a standardized IETF LLN protocol stack. Despite the efforts of the IETF working groups, different interpretations of the standards remain a threat that causes interoperability problems between implementations from different parties. Solving these issues requires identification of the possible problems and clarification of the implementation details where necessary to prevent possible ambiguities in the standards itself. Extensive interoperability testing events, like the ETSI Plugtests for CoAP and in the future possibility for 6 LoWPAN, significantly help to improve the quality of the standards and the implementations.  
Another more fundamental issue to consider is that of low-power interoperability, _i.e._, interoperability when communicating parties employ (possibly different) MAC radio duty cycling techniques. The authors of [145] identify two open issues, namely that existing protocols for LLNs typically have not been designed for MAC duty cycling and that existing MAC duty cycling mechanisms have not been designed for interoperability. While most of the IETF work is situated above the MAC sublayer, it is important that these concerns are addressed in the future so that end-to-end IPv6 connectivity with embedded devices, that employ heavy MAC duty cycling, is possible.
## 3 Results
### Antimicrobial Activity of the Strain ess_amH1  
The antimicrobial activity of solvent extract of ess_amH1 is presented in Figure 6. The results demonstrated that varying doses of ess_amH1 solvent extract prompted a zone of inhibition in a dose-dependent manner (Table 7). The highest ZOI (zone of inhibition) was observed in _S. aureus_ at (18 mm) at 80 \(\upmu\)g, followed by _K. pneumoniae_ (17.5 mm) and _S. pneumoniae_ (17 mm) when compared to streptomycin's inhibition zone of 20 mm. The least activity was perceived in _S. aureus_ (7 mm) at 5 \(\upmu\)g. Furthermore, the solvent extract of ess_amH1 was also shown to produce anticandidal agents against _Candidal albicans_ in a dose-dependent manner (Table 7). The highest ZOI (21 mm) was observed in 400 \(\upmu\)g when compared to fluconazole with an inhibition zone of 23 mm. Means \(\pm\) SEs followed by different letters (a-e) within the same row indicate a significant difference (Tukey's test, \(p<0.05\)). Figure 5: GC–MS chromatogram for EtOAc extract of _Streptomyces_ sp. strain ess_amH1. Figure 6: _Cont._Cell Morphology by SEM Examination  
Overall, the highest antibacterial activity was observed against _K. pneumoniae_. Therefore, here, we study the alterations in the outer membrane of the _K. pneumoniae_ using the  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multicolumn{5}{c}{**Bacterial Pathogens (mm)**} \\ \hline
**Concentrations** & **5 \(\upmu\)g** & **10 \(\upmu\)g** & **40 \(\upmu\)g** & **80 \(\upmu\)g** \\ \hline _Klebsiella pneumoniae_ (ATCC 13883) & \(8\pm 1\)\({}^{\mathrm{d}}\) & \(11\pm 1.5\)\({}^{\mathrm{c}}\) & \(15\pm 1.1\)\({}^{\mathrm{b}}\) & \(17.5\pm 1.2\)\({}^{\mathrm{a}}\) \\ _Pseudomonas aeruginosa_ (ATCC 27853) & - & \(8.5\pm 0.14\)\({}^{\mathrm{b}}\) & \(12\pm 0.15\)\({}^{\mathrm{a}}\) & \(14\pm 16\)\({}^{\mathrm{a}}\) \\ _Salmonella typlii_ (ATCC 14028) & \(8\pm 13\)\({}^{\mathrm{d}}\) & \(10\pm 1\)\({}^{\mathrm{c}}\) & \(13\pm 1\)\({}^{\mathrm{b}}\) & \(1.6\pm 0.9\)\({}^{\mathrm{a}}\) \\ _Streptococcus pneumoniae_ (ATCC 49619) & \(9\pm 1\)\({}^{\mathrm{c}}\) & \(10\pm 2\)\({}^{\mathrm{bc}}\) & \(12\pm 1.5\)\({}^{\mathrm{b}}\) & \(17\pm 1\)\({}^{\mathrm{a}}\) \\ _Staphylococcus aureus_ (ATCC 25923) & \(7\pm 1.3\)\({}^{\mathrm{c}}\) & \(8.5\pm 1.5\)\({}^{\mathrm{c}}\) & \(12\pm 1\)\({}^{\mathrm{b}}\) & \(18\pm 1.2\)\({}^{\mathrm{a}}\) \\ \hline \multicolumn{5}{c}{**Fungal Pathogen (mm)**} \\ \hline
**Concentrations** & **20 \(\upmu\)g** & **40 \(\upmu\)g** & **80 \(\upmu\)g** & **200 \(\upmu\)g** & **400 \(\upmu\)g** \\ \hline _Candida albicans_ & \(7\pm 1.5\)\({}^{\mathrm{c}}\) & \(8\pm 2\)\({}^{\mathrm{c}}\) & \(13\pm 1.9\)\({}^{\mathrm{b}}\) & \(15\pm 1.5\)\({}^{\mathrm{b}}\) & \(21\pm 1.6\)\({}^{\mathrm{a}}\) \\ \hline \multicolumn{5}{c}{The mean \(\pm\) SE followed by different letters (a–d) within the same row were significantly different (Tukey’s test, \(p<0.05\)).} \\ \multicolumn{5}{c}{Cell Morphology by SEM Examination} \\ \hline Overall, the highest antibacterial activity was observed against _K. pneumoniae_. Therefore, here, we study the alterations in the outer membrane of the _K.
# Explainable Lightweight Block Attention Module Framework for Network-Based IoT Attack Detection
###### Abstract  
In the rapidly evolving landscape of internet usage, ensuring robust cybersecurity measures has become a paramount concern across diverse fields. Among the numerous cyber threats, denial of service (DoS) and distributed denial of service (DDoS) attacks pose significant risks, as they can render websites and servers inaccessible to their intended users. Conventional intrusion detection methods encounter substantial challenges in effectively identifying and mitigating these attacks due to their widespread nature, intricate patterns, and computational complexities. However, by harnessing the power of deep learning-based techniques, our proposed dense channel-spatial attention model exhibits exceptional accuracy in detecting and classifying DoS and DDoS attacks. The successful implementation of our proposed framework addresses the challenges posed by imbalanced data and exhibits its potential for real-world applications. By leveraging the dense channel-spatial attention mechanism, our model can precisely identify and classify DoS and DDoS attacks, bolstering the cybersecurity defenses of websites and servers. The high accuracy rates achieved across different datasets reinforce the robustness of our approach, underscoring its efficacy in enhancing intrusion detection capabilities. As a result, our framework holds promise in bolstering cybersecurity measures in real-world scenarios, contributing to the ongoing efforts to safeguard against cyber threats in an increasingly interconnected digital landscape. Comparative analysis with current intrusion detection methods reveals the superior performance of our model. We achieved accuracy rates of 99.38%, 99.26%, and 99.43% for Bot-IoT, CICIDS2017, and UNSW_NB15 datasets, respectively. These remarkable results demonstrate the capability of our approach to accurately detect and classify various types of DoS and DDoS assaults. By leveraging the inherent strengths of deep learning, such as pattern recognition and feature extraction, our model effectively overcomes the limitations of traditional methods, enhancing the accuracy and efficiency of intrusion detection systems.  
network; cybersecurity; DDoS; attention; IoT; Densenet +
Footnote †: journal: Future Internet
# Gridded Visibility Products over Marine Environments Based on Artificial Neural Network Analysis
## 3 Results
### Fitting Effect of ANN
The marine data include water temperature, salinity, etc., which are recorded once every 30 s.  
Data used to train the model is from ICOADS, from 2001 to 2015 in the Arctic (180\({}^{\circ}\) W-180\({}^{\circ}\) E, 60\({}^{\circ}\) N-90\({}^{\circ}\) N), and the data used to test the accuracy of the trained model is one-hour average data from CPASE. Since the sample data sets are different when inferring Vis in different months, this paper calculated the relative error in August and September, respectively. The inferred result is shown in Figure 6. The average relative error of all observations in August and September is 19.0% and 12.7%, respectively, which indicated that ANN could well fit the relationship between Vis and its influence factors. Note that the AOD was not considered when calculating Vis here. Figure 5: Composition of the average relative error of each observation point when taking AOD into consideration or not. Figure 6: The relative error of referred Vis from Equation (4) against observations obtained from the Chinese Ninth Arctic Science Examination (CPASE).
# A Novel Channel Calibration Method for Bistatic ISAR Imaging System
## 4 Experimental Results and Analysis
### Verification of the Channel Calibration Performance  
#### 4.2.1 Parameters Setting  
The simulation scenario is shown in Figure 7, in which the red \"\(\lozenge\)\" is the location of the transmitting station, the green \"\(\bigcirc\)\" is the location of the receiving station, the length of the radar baseline is 50 km, the blue line is the target trajectory, the target velocity is 400 m/s, and the red part is selected as the imaging area. The simulation scatter-point model is shown in Figure 8.  
Figure 5: Real calibration satellite echo signal in one period: (**a**) Real part of the signal; (**b**) spectrum of the signal.  
Figure 6: Signal coherently accumulated 1000 periods of real calibration satellite echoes: (**a**) Real part of the signal; (**b**) spectrum of the signal.  
Figure 7: Simulation scenario.  
The parameters of a bistatic ISAR radar system used in simulation was same as the parameters of the radar system used to collect the real calibration satellite echoes in Section 4.1. The rest simulation parameters are shown in Table 3.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 2 Related Work  
### Research Works on Space Network  
To promote the development of SN, and ensure the efficient interconnection of various SN nodes as well as full sharing of information and resources, various research works have been conducted in fields of the network architecture, the constellation and the network protocol.  
Aiming at the problem of lacking a stable end-to-end path for data transmission, which is caused by long transmission delay and frequent link interruption in SNs, researchers propose a Delay/Disruption-Tolerant Network (DTN) and CCSDS (Consulative Committee for Space Data Systems).  
Constellation system deployment plans have been carried out in recent years, such as OneWeb [6] and Starlink [7] according to different constellation systems, application requirements and optimization objectives, [8] and some other researches present multiple inter satellite topology design algorithms.  
Aiming at the problem that traditional global optimal routing strategies is only suitable for static network topologies, the topologically predictable routing protocol, namely OSPF+ [9], is presented. Pointing at the problem of low data transmission efficiency, which is caused by long transmission delay and high bit error rate in space networks, an erasure code-based LTP (Licklider transmission protocol) protocol [10] is designed. Considering the transmission of the video stream under the harsh communication conditions in the deep space a BSS (Bundle Streaming Service) protocol [11] is proposed. In addition, there are many related works in the fields of Ad-Hoc Networks [12] and 5G [13; 14].
## 5 Conclusions  
It is well known that there is currently a great deal of progress in three-dimensional (3D) technologies in the field of medicine. Methods such as model surgery or cephalometric prediction methods (2D prediction) including video imaging are considered _gold standards_ in orthognathic surgery. Although these techniques are a routine part of the diagnostic and treatment planning process, they have their limitations. The 3D environment adds the third dimension to planning, bringing planning closer to reality and providing us more information to diagnose a wider range of dentofacial anomalies.  
Our results showed differences between male and female patients, both in the overall configuration of points and in individual measurements, including anterior mandibular height with pogonion as one of the measurement endpoints. Average differences were also evident in the facial scans. This correlation between hard tissue and overlying soft tissue suggests that soft tissue does not necessarily compensate for underlying structural changes. It is important to note that our research design did not specifically examine the relationship between hard and soft tissues in the same context, although there was considerable overlap between the three datasets. The presence of sex-related differences may have implications for surgical planning and postoperative management, but our small sample size requires further investigation.  
Future studies should be devoted to more detailed data analysis, the application of computational intelligence to the processing of spatial models, and the application of the proposed methodology to larger numbers of patients and different types of surgical interventions. More detailed visualization of future positions of the soft and hard tissues of the face in 3D space and detailed monitoring of proposed results during future operations should form specific topics of further research.  
Conceptualization: T.D., H.E. and P.U.; methodology: A.P. and T.D.; software: P.U. and A.N.; validation: T.D.; formal analysis: T.D.; investigation: T.D., H.E. and P.U.; resources: P.U.; visualization: A.P. and T.D.; project administration: T.D. and P.U. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of of Charles University, 2 nd Faculty of Medicine and the Motol University Hospital (IRB approval No. EK-973 IGA 1.12/11, 23 July 2019).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author.  
The research was conducted under Project No. 00064203 (FN Motol), project No. MUNI/A/1597/2023 (Masaryk University), and Operational Programme [PERSON] of the European Structural and Investment Funds and the Czech Ministry of Education, Youth and Sports (Project No. SENDISO-CZ.02.01.01/00/22_008/0004596).  
The authors declare no conflicts of interest.
# Tests of the Influence of DAF (Delayed Auditory Feedback) on Changes in Speech Signal Parameters
###### Contents
It should be emphasized that methods effective for cluttering may exacerbate symptoms in cases of stuttering, and vice versa. For this reason, the phenomenon of Delayed Auditory Feedback (DAF) should be approached with care, and an appropriately selected research group should be prepared to evaluate whether such a therapy could serve as a beneficial tool for speech therapists in supporting the primary treatment for either group.