esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Basic Study for Calculation Scheme of MD
### MD Calculation Using Frequency Components at Around Peak Frequencies  
As noted, when the method described in Section 3.1 was used, the MD\({}^{2}\) values increased even though the equipment was operating in a steady-state condition. To cope with this problem, an alternative method was considered. Specifically, as shown in Figure 5b, the frequency components used to calculate MD\({}^{2}\) were extracted as 1/12 octave bands around the peaks of the frequency characteristics at 200 Hz, 400 Hz, 600 Hz, and 800 Hz. The reason for this is that background noise components other than peak frequency are included in the data when the energy levels in all of the 1/3 octave bands are used; however, these background noise data are not related to equipment malfunction. Therefore, developing a method that uses only the data around the peak frequencies would seem appropriate.  
In addition to the abovementioned treatment, the minute fluctuations in vibration observed in Figure 6a may affect the increase in the value of MD\({}^{2}\). To establish the periodicity of these fluctuations, the results of FFT processing at the sampling frequency of 13,300 Hz in the 200 Hz band with the largest fluctuation among all the bands in Figure 8 was used as a reference. It was observed that the periodic components in the low-frequency range were dominant. For this reason, a high-pass filter with a cutoff frequency of 4.16 Hz in the sampling frequency of 13,300 Hz was applied to the time-series data of the energy level in each band, and the effects of the fluctuations were cut off to the extent possible. Note that the cycle in 4.16 Hz corresponds to periodic components over approximately four hours. The filtered time-series data are shown in Figure 6b. It can be seen that the minute time fluctuations have been eliminated. Figure 9 shows the calculated MD\({}^{2}\) results. The MD\({}^{2}\) values from the 1 to 13,300 time-series data on the first day have an average of approximately 1.0; the average after the second day is approximately 1.0 as well. Thus, a relatively constant MD\({}^{2}\) is observed.  
Figure 8: Frequency components of the fluctuation in the vibration time-series data for the lifter under normal condition.  
Figure 9: MD\({}^{2}\) calculated based on the optimal selection of the frequency band around each of the peak frequencies.  
From the results in Sections 3.1 and 3.2, when the MT method is applied to a vibration waveform having peak components, relatively stable MD\({}^{2}\) values of approximately 1.0 during normal operation are obtained by using the energy level in the frequency band around the peak components of the target waveform, making this the preferred alternative. Moreover, a periodic fluctuation component longer than approximately six hours should be cut off.
## 2 Endophytes-Current State of the Art
### Endophytes as Inhabitants of Agricultural Plants
#### 2.2.2 An Essential Nutrient Associated with Endophytes in Plants  
Plants require a continuous supply of macro- and micronutrients, most importantly N, P, K and Fe, in order to grow properly [28]. Of all the essential plant nutrients, N is the most important growth-limiting factor, and its deficiency is a major cause of low agricultural productivity worldwide [71]. Endophytes are essential for plant N feeding. They improve the ability of plants to acquire N from both organic and inorganic sources [72]. Other macronutrients also largely affect plant growth and yield [4].  
P plays a role in metabolic processes, signal transduction, macromolecular biosynthesis, photosynthesis and cellular respiration [73]. Unfortunately, most of the available P quickly form complexes with other elements in the soil making it unavailable to plants [74]. Some endophytic microorganisms have been shown to possess the ability to convert insoluble forms of P into a usable form [4; 74]. Among the bacteria capable of phosphate solubilization, the genera _Rhizobium_, _Agrobacteriumium_, _Pseudomonas_, _Burkholderia_, _Erwinia_, _Paenibacillus_, _Bacillus_, _Enterobacter_, _Klebsiella_, _Kluyvera_, _Streptomyces_, _Pantoea_, and _Lysinibacillus_ can be distinguished [74].  
On the other hand, [PERSON] and [PERSON] [75] obtained three species of _Apergillus_ sp. fungi capable of solubilizing K. In addition, the researchers showed that inoculation of maize seedlings with K-solubilizing fungi (KSF) significantly improved plant growth and K uptake in maize and increased the population of KSF in the soil [75].
# Prefix Data Augmentation for Contrastive Learning of Unsupervised Sentence Embedding
## 6 Semantic Similarity Event and Prefix Data Augmentation  
### Semantic Similarity Event  
We assume that all sentences A can be mapped to the vector space Rn; thus, any sentence pair \((A_{1},A_{2})\) can be mapped to the vector space R2n, with the resulting vector denoted as \(x\). Then, we define the sample space \(\Omega(\{\Omega=\text{sim}(x)|x\in R^{2n}\})\), where \(\text{sim}(x)\) represents the vector similarity of the sentence pair \((A_{1},A_{2})\). The event space \(\mathcal{F}\) is the set of combinations of any events (e.g., \(\text{sim}(x)>0.6\)); \(\mathcal{P}\) is the probability function mapping events to \([0,1]\). We define a probability space \((\Omega,\mathcal{F},\mathcal{P})\) based on the similarity of sentence vectors. As such, we can define the occurrence of semantically similar event \(E\) in the sample space as  
\[E=\{\text{sim}(x)>\tau\} \tag{3}\]  
where \(\tau\in[0,1]\) is a threshold parameter. Therefore, the probability of the occurrence of a semantically similar event is represented as  
\[P(E) \tag{4}\]  
In practice, determining whether two sentences are similar is based on specific contextual information. Therefore, only conditional semantic similarity events exist based on different contextual information, and their probability is  
\[P(E|C) \tag{5}\]  
where \(C\) is an event implying contextual information (e.g., \(C=\text{sim}(x)>0.5\), \(E=\text{sim}(x)>0.8\)). In this paper, \(P(E|C)\) represents our trained PrdSimCSE model. For any given pair of sentences, based on the training corpus, \(P(E|C)\) can provide their semantic similarity score, thereby determining whether the pair belongs to a semantic similarity event. According to [PERSON] theorem, the estimation of the probability of a semantic similarity event can be considered as  
\[\hat{P}(E)=\frac{P(C)P(E|C)}{P(C|E)} \tag{6}\]  
The alignment of the sentence vector space using positive samples strongly impacts contrastive learning. This is because the selection of positive samples is directly related to the quality of sentence pairs in the semantic similarity set. Additionally, Prd can expand  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Dataset** & **Spearman** \\ \hline \multirow{3}{*}{SimCSE} & Original & 72.23 \\  & \textgreater{}8 & 70.22 (\(-\)2.01) \\  & \(\leq\)8 & 73.69 (+1.46) \\ \hline \multirow{3}{*}{PrdSimCSE} & Original & 73.75 \\  & \textgreater{}8 & 71.68 (\(-\)2.07) \\ \cline{1-1}  & \(\leq\)8 & 75.59 (+1.84) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performance on sentence embeddings for SICK-R with different sentence lengths.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Original** & **Remove Length \textless{} 6** & **Remove Length \textless{} 8** \\ \hline PrdSimCSE & 80.91 & 84.86 & 87.14 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Performance on sentence embeddings in TREC dataset with different sentence lengths.  
contextual information; namely, the precision of semantic similarity event probability estimation is improved by adding higher-quality negative samples.
# Harnessing the Potential of Emerging Technologies to Break Down Barriers in Tactical Communications
### Motivation: Gap Identification  
Against this background, the main gap detected is that the investigation of emerging technologies in the civil field is diverging and forgetting their application in the military field as well. There is a lack of analysis of the technologies that could be used to improve the tactical scenarios and how the risk posed by a war scenario can be minimized or solved with the help of modern technologies. New techniques and strategies, for example, are emerging in the field of encryption. Everything is being proposed for civil applications, not dedicating or actively focusing on the tactical field. The junction of emerging civil technologies with military or tactical scenarios presents promising opportunities and notable gaps which have not been identified in the literature so far. While there exists substantial potential for leveraging advancements such as artificial intelligence and unmanned systems in defense applications, several gaps must be addressed. Additionally, interoperability challenges between disparate civil and military systems can hinder seamless integration and communication on the battlefield. Moreover, cybersecurity vulnerabilities and data privacy concerns demand robust measures to safeguard sensitive information from adver  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Year** & **Legacy** & **5G Technologies** & **Proposals for 6G** & **Use Cases** \\ \hline
[11] & 2021 & No & Partial & No & Partial \\ \hline
[12] & 2020 & No & Partial & No & No \\ \hline
[13] & 2020 & No & Partial & No & Partial \\ \hline
[14] & 2020 & No & Partial & No & No \\ \hline
[15] & 2018 & No & Partial & No & No \\ \hline
[16] & 2020 & No & Partial & No & Partial \\ \hline Our Proposal & 2023 & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: State-of-the-art review compared to our proposal.  
sarial exploitation. Adequate training and education for military personnel to effectively operate and harness the capabilities of these emerging technologies are also imperative. Bridging these gaps will require collaborative efforts among policymakers, technologists, and military strategists to capitalize on the potential while mitigating risks and ensuring a balance between innovation and ethical principles. For all this motivation, a first analysis and compilation of the technologies that can be used in the different military uses must be carried out, which is the origin of this work and base for future research.
# Management Platforms and Protocols for Internet of Things: A Survey
## 8 IoT Device Management Platforms
### Management for the IoT (ManIoT)
#### 8.1.2 Global Management  
The global manager seeks to standardize the actions performed in different scenarios. It has two layers: Application and Services layers, as shown in Figure 23.  
The global services have the functions as those development in the local scope, as shown in the second layer of Figures 22 and 23. Global scope services handle larger data sets and provide support for more comprehensive applications. For example, in the context of electrical management, the global manager must have the ability to manage possible power outages in several residences in a neighborhood. The actions defined by the global services are sent and executed in the devices of the respective local managers, using a TCP/IP connection.  
_Pros: Context aware and scalability_  
_Cons: Privacy, security, supports few native protocols._  
Figure 23: ManIoT: Global/Remote Management Architecture.  
Figure 22: ManIoT: Local Management Architecture.  
Use Case  
In the Intelligent Lighting Scenario, the lighting of an environment is adjusted with the presence of people and the existence of natural light. Bulbs are switched conform a person move in the room. Light intensity is inversely proportional to the amount of natural light.  
The ManIoT prototype consumes approximately 0.05% of the bandwidth of these networks in the worst case [93]. These values are justified because of the small amount of data exchanged between the prototype of the Local Manager and the devices, thus reinforcing the minimal use of hardware resources.  
There were results obtained with scenarios of intelligent lighting and automation of tasks using appropriate metrics to show the capacity of ManIoT to provide a dynamic adaptation and context science to the environment.
# Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
## 5 Management of WSNs Based on SDN  
WSNs are composed of a network of sensors that are deployed to measure parameters such as temperature, pressure and air quality. The sensor network may exist as hundreds or even thousands of resource constrained nodes. Also taking into account the inherent challenges of WSNs in terms of energy requirement which is directly linked to network lifetime, the application specific nature with vertical integration [27] where manufacturers and vendors control the end products of the devices as well as the software and hardware of their infrastructure. Another challenge introduced as the nodes are deployed in the field is the management of the network topology [77] where we have to consider the coverage of the network and Quality of Service in an environment with varying conditions at a limited energy supply. Integration of SDN in WSN has been proposed [14; 21; 24] to mitigate these challenges. Integration of SDN would require consideration of how the network will be managed to ensure efficient performance and resource allocation. The WSN architecture based on SDN can be divided into managerial entities similar to the management functionality abstractions as mentioned by [PERSON] et al. [25]. Figure 2 shows this architecture [28] adapted with a further focus on the management entities we will consider in our review and evaluation of SDN-based management for WSNs. The architecture shown depicts an SDN-based WSN with a centralized controller thus providing a global view of the entire network resulting in efficient management. Management of the network topology, energy, configuration, QoS and security are done centrally in the control plane while management of enabling technologies can be implemented at all levels of the architecture. In the data plane enabling hardware such as SDN-enabled sensor nodes with multiple sensing capabilities and hardware capabilities can be installed while programming of the nodes for various applications can be done and managed centrally in the control plane.
# Self-Reporting Technique-Based Clinical-Trial Service Platform for Real-Time Arrhythmia Detection
## 2 Materials and Methods  
### Overview  
We implemented a new software infrastructure for clinical trials of ICT-based, real-time arrhythmia detection. The newly developed platform mainly consists of four network interfaces: an external network, a demilitarized zone (DMZ), an internal network, and an Amazon web services virtual private cloud (AWS-VPC).  
The self-reporting technique and arrhythmia-detection algorithm are the key elements of this platform. The mobile app--an external network of this platform--was proposed to implement the self-reporting technique aside from ECG monitoring. The acquired self reported information and ECG data were transmitted to the internal network of Seoul National University Hospital Clinical Trials Center (SNUH-CTC), wherein arrhythmia occurrences were analyzed with the arrhythmia-detection algorithm. Results were then saved. The DMZ and AWS-VPC were prepared between the mobile app and internal network. The DMZ was intended to protect the sensitive resources in the internal network from possible security risks generated by untrusted ICT devices and networks [23]. The AWS-VPC was adopted to build a secure cloud-based data-transmission and management system, which provided live-streaming and playback-reviewer services. The web client was prepared to help clinicians access both services at the central monitor station.  
A wearable ECG device was incorporated in the platform. To ensure safe and effective operation, wearable ECG devices, approved as medical devices by the Korea's Ministry of Food and Drug Safety (MFDS), were considered.
## 6 Experimental Simulations
### Picking
#### 6.1.3 The SOP Solution
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \multicolumn{1}{c}{} & \multicolumn{3}{c}{Order 1} & \multicolumn{3}{c}{Order 2} & \multicolumn{3}{c}{Order 3} & \multicolumn{3}{c}{Order 4} & \multicolumn{3}{c}{Order 5} \\ \hline \(t_{l}\) & Zone & SC & \(t_{l}\) & Zone & SC & \(t_{l}\) & Zone & SC & \(t_{l}\) & Zone & SC & \(t_{l}\) & Zone & SC \\ \hline \(\prime\) & 1 & \(\prime\) & / & 1 & / & / & 1 & / & / & 1 & / & / & 1 & / & / & 1 & / \\
47.73 & 10 & 14.10 & 51.26 & 11 & 14.70 & 51.26 & 11 & 14.70 & 47.73 & 10 & 14.10-14.70 & 51.26 & 11 & 14.70 \\
29.46 & 12 & 14.10 & 25.92 & 12 & 14.10 & 16.47 & 10 & 14.10 & 16.47 & 11 & 13.50 & 25.92 & 12 & 14.10 \\
16.47 & 11 & 13.50 & 25.92 & 11 & 13.50 & 16.47 & 11 & 13.50 & 25.92 & 12 & 12.90 & 25.92 & 11 & 13.50 \\
16.47 & 10 & 12.90-13.50 & 25.92 & 12 & 12.90 & 25.92 & 12 & 12.90 & 29.46 & 10 & 11.85-12.90 & 16.47 & 10 & 11.85-12.90 \\
27.06 & 6 & 11.25 & 29.46 & 10 & 9.66-12.90 & 25.92 & 11 & 12.15 & 29.46 & 12 & 11.30-11.85 & 29.46 & 12 & 11.25-11.85 \\
31.76 & 3 & 11.25 & 29.46 & 12 & 9.65 & 25.92 & 12 & 11.85 & 29.46 & 10 & 9.65-12.5 & 29.46 & 10 & 10.65-12.5 \\
61.42 & 12 & 11.25 & 22.04 & 11 & 9.05 & 29.46 & 10 & 11.88-11.25 & 29.46 & 12 & 9.65 & 29.46 & 12 & 9.65 \\
29.46 & 10 & 9.90-12.15 & 16.47 & 10 & 87.5-0.95 & 29.46 & 12 & 11.25-0.65 & 29.46 & 10 & 87.5-0.20 & 29.46 & 10 & 9.05-9.65 \\
29.46 & 12 & 9.65 & 38.82 & 3 & 8.10-8.50 & 29.46 & 10 & 9.65-0.05 & 38.82 & 3 & 8.50 & 16.47 & 11 & 9.05 \\
29.46 & 10 & 6.45-9.65 & 28.24 & 7 & 6.65 & 16.47 & 11 & 9.05 & 38.82 & 10 & 5.85-8.10 & 16.47 & 10 & 8.75 \\
29.46 & 15 & & 23.53 & 10 & 5.86-6.45 & 16.47 & 10 & 8.75 & 29.46 & 15 & 23.53 & 7 & 8.50 \\  & & & 27.06 & 6 & 4.50-4.95 & 38.82 & 3 & 8.50 & & 28.24 & 3 & 8.50 \\  & & & 31.76 & 3 & 3.35 & 38.82 & 10 & 5.85 & & & 38.82 & 10 & 5.85 \\  & & & 68.28 & 15 & 16.47 & 11 & 3.95-6.65 & & & & 16.47 & 11 & 3.80 \\  & & & & 25.92 & 15 & & & & & 42.35 & 3 & 3.35 \\ \hline \end{tabular}
\end{table} 
Table 4: Simulation of travel times for 10 BOPS orders when the SOP optimization is implemented.
# Underwater Acoustic Target Tracking: A Review
## 3 Knowledge of Acoustic Target Tracking Instruments
### Taa  
Traditionally, various types of TASAs have been used to complete the underwater target tracking task, for example, the hull-mounted array and towed array [24; 25; 26]. The hull-mounted array composed of a spherical or cylindrical acoustic sensor array is installed on ships. It is a typical active system which takes the initiative to transmit a certain frequency of sound waves in a row of the array and tracks objects according to the reflection of echoes. The towed array usually made up of a linear array is traditionally dropped into the sea and towed by a submarine, severing as a passive listening sensor which completes the monitoring task completely depending on the radiated noise from the target.  
However, these tracking algorithms based on TASA have fatal flaws in some mission-critical scenarios because they need to be mounted on or towed by a submarine or a ship [27]. The effective tracking area of TASA depends on the trajectory of the tracking platform, which makes intruders easily avoid being detected. In addition, it is impractical and impossible to maintain a 24-h sea patrol, which makes it unsuitable for real-time missions. Among all drawbacks of TASA, the most critical point is that the platform which tows sensor array or on which the array is mounted is going to affect the whole system once an attack or a failure occurs.
###### Abstract
However, the data validation rules are complex and moreover are periodically updated, leading to a substantial amount of recoding effort and correspondingly high maintenance costs with a continued reliance on a specific centralised entity for controlling the whole task. Ontologies offer one possible and elegant solution owing to their ability to integrate the entities defined by a data model with their associated semantic relationships. This means that any change to the data model can be kept in synchronisation with the validation rules that derive from the semantic relationships between the variables. Moreover, basing validation software on ontologies developed in the Web Ontology Language (OWL [9]) helps ensure the veracity of the link to the most updated version release of the data model/semantic relations and further serves to loosen the dependence on any given central entity. Other advantages concern the fact that the validation rules can be formalised in description logic (DL) and that the metadata can be linked semantically to the Uniform Resource Identifiers (URIs) of the related OWL classes. The benefits accruing to CRs from ontologies and semantically linked data have been highlighted by others [10], and ontology-based tools have been deployed for specific CR activities relating to: automatic rule extraction from data sets [11]; information extraction and reasoning from unstructured pathology reports [12]; analysis of disease courses [13]; and inference of clinical stage [14]. Whereas an initial attempt to develop an ontology-based CR-data validation tool has shown promising first results [15], a critical test lies in its ability to handle the whole set of CR validation checks [7]. Although it was demonstrated that the majority of the rules can be modelled with terminological component (TBox) axioms, the checks to validate multiple primary tumours (MPTs) in case histories of patients with more than a single primary tumour are arguably the most challenging to perform from an ontological point of view. MPTs that do not fulfill the explicit conditions described by the international rules on the validity of multiple primary tumours [7; 16] must be considered for reporting purposes as a single primary tumour. The consequence of not performing these specific checks can lead to skewed cancer statistics and provide erroneous comparisons at population level. This paper presents a solution for modelling the MPT checks in an OWL ontology.