esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy
## 2 Related Work
### Challenges  
Even though recent works are similar to ours in terms of coverage and analysis of the IoE paradigm, some approaches address our review criteria only to a varying degree. To clarify the contributions of the current paper, Section 5 summarizes a brief comparison of the scope of the proposed IoE taxonomy and 76 IoE and IoT previous works, selected in the literature review (presented in Section 3). The scope of the proposed knowledge-based IoE taxonomy is considerably more embracing in terms of visibility of intelligent connections between sensors and actuators in IoE applications than existing works. Some works touch upon knowledge creation and collaboration among IoE devices, while others propose taxonomies concerning specific areas (e.g., observations, infrastructure, sensor type, and analytics for IoT and IoE). They identify design challenges from several perspectives; however, they fall short in the two respects detailed below.  
First, they do not explicitly address the characteristics of knowledge enablers (sensors and actuators) and to what degree they collaborate to improve efficiency in IoE solutions. In general, the identification of knowledge sources in human and non-human sensor nodes requires further research to enhance intelligence services through managing knowledge processes. Furthermore, for knowledge-intensive IoE applications, the governance of knowledge sharing in human-machine relationships are still mostly inadequate. This situation requires a complete taxonomy that leverages awareness from the length and breadth of the knowledge hierarchy, considering knowledge interaction and transformations from sensor platforms in collecting raw data for the foresight and intelligence that drive decision-making processes and provide outcomes and wisdom.  
Second, although they provide an overview of hardware and software components in IoE systems, the studies do not categorize and organize them in a concise manner that provides a contextual understanding of the complexity of IoE enablers. The related works do not categorize the sensors and actuators in terms of knowledge users and providers and how they participate in knowledge creation in IoE applications. We propose a comprehensive knowledge-based IoE taxonomy that optimizes technological and architectural components as integrated resources that drive knowledge creation.  
Providing a broad and forward-looking view of the IoE paradigm is, essentially, the principal contribution of this present study. This paper proposes an IoE taxonomy, based on knowledge, to elicit how intrinsic knowledge in sensors and actuators--in conjunction with enabling technologies and infrastructures--are applied in observations to produce intelligent services in the IoE context.
## 2 Related Works  
In computational auditory scene analysis, standard approaches are primarily composed of two steps: feature extraction and statistical classification. The first stage tackles the issue of developing efficient feature representation of an audio clip for ASC. A wide variety of acoustic features had been investigated, including log-scale spectrogram [1], cepstral features (MFCCs) [6] and gammatone cepstral coefficients (GTCC) [2]. Those features are mainly taken from automatic speech recognition (ASR) field and have been proved to be effective to characterize rich low-level information from the audio signal by using a time-frequency (matrix) representation (TFR). Subsequently, statistical moments, e.g., averaging and standard deviations, are employed to convert time-frequency representations (matrix) to compact feature vector [2]. Besides, various types of summary statistics had been adopted to characterize discriminant information in both time and frequency domain, such as by using zero-crossing rate (ZCR) and spectral kurtosis [3]. In recent years, much attention is paid to apply advanced feature extraction methods for ASC, such as using Bag-of-features [13] and sparse coding models [1] to distill discriminant information from noisy recordings. At the latter part, the typical examples of statistical classification algorithms are Gaussian mixture models (GMM) and support vector machine (SVM) [14]. More recently, taking inspiration from various successful applications in both computer vision and speech recognition, there is an emerging trend in ASC research to shift from conventional classification techniques to deep neural network-based methods. Such tendency is evident according to the latest DCASE challenge series (IEEE AASP Challenges Detection and Classification of Acoustic Scenes and Events) [15]. Many researchers attempted to introduce Convolutional Neural Networks (CNNs) to drive an informative and robust audio data representation for ASC in a data-driven manner, such as in [16; 17]. Specifically, the CNNs-based approach is able to jointly optimize the acoustic feature representation and the classification algorithm. However, the current open datasets for ASC research are much smaller compared with the ones for computer vision, which may lead to under fitting status. Besides, the ensemble of several ASC architectures had been proved to be efficient to boost the ASC accuracy. For instance, in 2017 DCASE evaluation, the well-ranked submissions were based on CNNs aggregating with other deep neural network models, i.e., Recurrent Neural Network (RNN) and Multiple Layer Perception (MLP) [18]. Current work had been carried out based on the survey over state-of-the-art research.  
In this study, we propose novel ASC approach which consists of three major components: spectro-temporal feature extraction using multiple time-frequency representations (TFRs), discriminant content enhancing weights extraction and multi-descriptor aggregation scheme for ASC. Figure 2 shows the processing flow of the proposed ASC system. We introduce the key components as follows.
## 3 Results and Discussion
### Sensitivity and Reproducibility of the Electrochemical Biosensor
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**No.** & **Biosensor Platform** & **Bio-Receptor** & **Linear Range** & **Measurement Time** & **LOD (CFU/mL)** & **Ref.** \\ \hline
1 & CL.aptasensor & **Aptamer** & \(10^{4}\)-\(10^{5}\) CFU/mL & 63 min \({}^{*}\) & \(4.5\times 10^{3}\) & [42] \\ \hline
2 & PCR & Primer & \(10^{2}\)-\(10^{6}\) CFU/mL & 180 min & \(10^{2}\) (without pre-enrichment step) & [43] \\ \hline
3 & ELISA & Bacteriophages & / & 240 min \({}^{*}\) & \(10^{6}\) & [44] \\ \hline
4 & Diffuse reflectance & Dye-labeled & \(5\times 10^{5}\)-\(5\times 10^{6}\) & 45 min & \(2\times 10^{5}\) & [45] \\  & spectroscopy & antibodies & CFU/mL & & \\ \hline
5 & Immunomagnetic & Protein G-lipsonal & \(10^{2}\)-\(10^{6}\) CFU/mL & 275 min \({}^{*}\) & \(100\) (in pure cultures) & \\  & bead sandwich assay & & & & \(3.1\times 10^{10}\) & [46] \\  & & & & & (in mixed cultures) & \\ \hline
6 & QCM (quartz crystal & \(10^{2}\)-\(10^{6}\) CFU/mL and & \(50\) min & \(1.46\times 10^{3}\) & [47] \\  & microbalance)sensor & **Aptamer** & \(10^{2}\)-\(10^{6}\) CFU/mL and & 50 min & \(1.46\times 10^{3}\) & [47] \\ \hline
7 & SPR biosensor & **Fragment of tail** & \(2\times 10^{4}\)-\(2\times 10^{6}\) & 20 min & \(2\times 10^{4}\) & [6] \\  & & lambda & CFU/mL & & \\ \hline
8 & Electrochemical sensor & Antibody & \(6\times 10^{2}\)-\(6\times 10^{5}\) & 120 min & \(6\times 10^{2}\) & [48] \\ \hline
9 & Light-addressable & **Fluorescent** & **Fluorescent** & **Fluorescent** & \(7.1\times 10^{2}\) & \\  & potentiometric sensor & antibodies & / & 45 min & (heat-killed) & [49] \\  & (LAPS) & **Fluorescent** & **Fluorescent** & \(25\times 10^{4}\) (live) & \\ \hline
10 & Electrochemical biosensor & ssDNA & \(10^{3}\)-\(10^{6}\) CFU/mL & Less than 180 min & \(10^{2}\) & [50] \\ \hline  & & Monoclonal antibody & & & \\  & against E. coli & & & & \\
11 & Electrochemical & \(0157\)-\(47\) & \(3.5\times 10^{3}\)-\(3.5\times 10^{8}\) & 165 min \({}^{*}\) & \(1.83\times 10^{2}\) & [51] \\  & immunousassay & functionalized & & & & \\  & & nanoparticles & & & \\ \hline
12 & Electrochemical & \(015\)-\(5\times 10^{7}\) & & & & \\  & biosensor & **Aptamer** & \(5.0\times 10^{2}\)-\(5.0\times 10^{7}\) & & & \\  & & CFU/mL & & & \\ \hline \end{tabular} / means not available in the text. \({}^{*}\) means not available in the text directly; the detection time is speculated based on the experimental steps in the paper.  
\end{table} 
Table 1: Comparison between the proposed method and other reported biosensors for the detection of _E. coli_. To evaluate the repeatability of the developed electrochemical biosensor, the DPV responses were examined 5 times for each of 5.0 \(\times\) 10\({}^{2}\), 5.0 \(\times\) 10\({}^{5}\) and 5.0 \(\times\) 10\({}^{7}\) CFU/mL _E. coli_. The relative standard deviation (RSD) is 5.3%, 4.5% and 1.1%, respectively. The RSD value is only slightly greater than 5% at 5.0 \(\times\) 10\({}^{2}\) CFU/mL concentration, and the rest are less than 5%, indicating that this method had an acceptable reproducibility.
## 4 Results
### Organ-Level Segmentation  
#### 4.1.1 Experiments Using Point Clouds Acquired through Laser Triangulation  
The Pheno4D dataset [86] was utilized as the source of laser scanning data under controlled indoor conditions to evaluate the performance of various semantic segmentation models. Table 3 presents the segmentation results of these models on the Pheno4D dataset, where OA denotes the overall accuracy, mACC denotes the average accuracy of every class, and mIoU represents the average IoU of every class. The majority of the models achieved an mIoU of over 80%, with the exception of PointNet and DGCNN. The ST and PT models exhibited the best segmentation performance, with overall accuracies surpassing 95%. The segmentation outcomes are depicted in Figure 4.  
PointNet and DGCNN exhibited subpar performance on maize and tomato crops, with notable inaccuracies, especially in segmenting the upper stems and leaves of tomato canopies. While other models successfully segmented corn plant stems and leaves, only PointCNN, PT, and ST managed to effectively segment the structurally intricate tomato plants. Challenges persist for other networks in accurately segmenting the upper stems of tomato plants.
# Ferry Scheduling Optimization Considering Arrival Time Uncertainty and In-Place Time Differences
## 4 Numerical Example Analysis  
The actual data of an airport in South China is used as a numerical example. Information for 31 flights during the peak hour was obtained from the transportation control system, which contained 43 flight ferry services. The example information of the ferry service is shown in Table 1. To better adapt to the data format of the model, \"00:00:00\" is defined as 0. We converted time into minutes as the minimum time particle. For example, we converted \"10:00:00\" to 600 and \"12:27:00\" to 747. According to the flight service standards provided by the airline company, we determined the required arrival time of the first ferry based on the ETD. A purely numerical code usually represents a stand number, such as \"217\" in Table 1. A code with letters indicates the location of the terminal at the ferry, such as \"W02\" in Table 1.  
The deviation between the estimated flight time and the actual time cannot be ignored. Through analysis of a large amount of historical data, it was found that there are differences in the distribution of departure time deviation and arrival time deviation. For this reason, the departure time deviation and the arrival time deviation are described separately, as shown in Figure 7a,b. The horizontal coordinate indicates the deviation between the estimated time and the actual time. When the value is negative, it indicates that the actual time of the flight is earlier than the estimated time. The deviation range of departure time is \([-3,16]\), and the deviation range of arrival time is \([-9,8]\). From the process of the ferry, it is clear that the start time of the inbound ferry service depends on the actual arrival of the incoming flight. The start time of the outbound ferry service depends on the departure time of the flight. Consequently, it is a reasonable assumption to use the flight inbound and outbound time deviation distribution to describe the dynamics of the arrival time of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(j\)} & **Flight** & **Initial** & **Target** & & & \\  & **Number** & **Position** & **Position** & **\(u_{j}\)** & **ETA** & **ETD** \\ \hline
1 & JD5228 & 217 & W02 & 1 & 12:27:00 & – \\
2 & MU5252 & 130 & E02 & 2 & 12:35:00 & – \\
3 & FM9309 & W05 & 402 & 3 & – & 12:42:00 \\... &... &... &... &... &... & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The partial service information.  
Figure 6: The mutation operation.  
the ferry service. Therefore, when the actual arrival and departure times of flights have not been determined, it is assumed that the arrival times of inbound and outbound ferry services obey the following discrete distributions.
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
## 3 Phenomena and Problems Caused by Photodarkening
### Mode Instability Threshold Reduction  
Through systematic experiments and simulations, it is found that the thermal composition of PD is an important reason for the mode instability and beam quality degradation of high power fiber laser [66; 67; 68]. In 2015, [PERSON] et al. [66] showed that PD would reduce the threshold of mode instability. It was considered that reducing PD was a promising way to reduce mode instability. The experimental results of PD and mode instability showed that even the negligible power loss caused by PD could reduce the mode instability threshold by two times. In 2016, [PERSON] proposed a theory of quasi-static degradation caused by PD in the fiber amplifier. This creates a persistent absorption grating that remains phase-shifted from the modal interference pattern. This leads to power transfer from the fundamental mode to a higher-order mode, and the wavelength shift is very small. The whole process takes place on a time scale of minutes to hours. This process was modeled in LMA step-index and photonic crystal fibers and was found to produce reasonable threshold values [67].
## 5 Proposed Operational Model: Real-Time Sustainability Monitoring Dashboard
### Model Structuring
#### 5.1.1 F#1: Defining Sustainability Indicators  
The first phase of the process involves defining the sustainability indicators, a fundamental step in establishing the metrics to monitor. The indicators must be chosen strategically to accurately measure the environmental, social, and economic impacts of  
Figure 12: GENIUS model “A GrEen Network for an Integrated bUsiness Strategy to monitor sustainability measures”.  
business operations. The indicators proposed for the dashboard were initially selected according to the principles outlined in ISO 14031:2013, the standard for Environmental Performance Evaluation (EPE). These guidelines provide a framework for choosing, classifying, and designing performance indicators. The results were then compared with the key indicators from the EU monitoring framework for the circular economy, published in 2018, which includes ten key indicators. Afterward, the indicators were further examined in light of those suggested by various researchers in scientific articles on the textile industry. This process allowed for the identification of the most appropriate indicators for the specific sector, ensuring a balanced approach that not only focused on environmental aspects but also encompassed social and economic dimensions. Table 25 shows a set of fundamental indicators for the textile sector, divided by each pillar of sustainability.  
These indicators are relevant to the company's sustainability goals and aligned with international standards, such as those established by the United Nations' Agenda 2030 (see Table 26).  
\begin{table}
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline  
KPIs are essential for identifying areas of success, detecting inefficiencies, and taking timely corrective actions. Furthermore, through KPIs, companies can assess the effectiveness of their initiatives, compare results with industry standards, and ensure that sustainability efforts align with long-term strategic objectives.
# Carousel Greedy Algorithms for Feature Selection in Linear Regression
## 3 Algorithm Description and Preliminary Experiments
### Preliminary Experiments  
The following experiments show how CG evolves the solution from stepwise regression. As shown in Figure 3, CG consistently improves the RSS of the stepwise regression solution gradually in the beginning and stabilizes eventually. A final horizontal line segment of length 1 indicates that no further improvements are possible.  
For the CT slice dataset we are using, the best subset selection cannot completely solve the problems in a reasonable time. When we limit the time of the best subset selection algorithm to a scale similar to CG, the RSS of its output is not as good as CG. Even if we give best subset selection more than twice as much time, the result is still not as good. The results are shown in Table 2 (the best results in Table 2 for each \(k\) are indicated in bold).  
From Figures 4 and 5, some improvements from stepwise regression solutions can be observed. However, as the number of observations (\(n\)) and the number of variables (\(p\)) in the dataset become smaller, the model itself becomes simpler, in which case there will be less room for CG to excel and the number of improvements also becomes smaller. Figure 5b,c show no improvements from stepwise regression. This might mean the stepwise regression solution is already relatively good. In that case, we may want to use other initializations to see if we can find better solutions.  
Figure 3: Improvements from carousel greedy with stepwise initialization for the CT slice dataset. The RSS of stepwise regression is at \(x=0\).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \(k=7\) & & \(k=15\) & \\  & RSS & Time (s) & RSS & Time (s) \\ \hline Stepwise regression & 1,653,208 & 5.55 & 1,029,899 & 16.08 \\ CG with stepwise initialization & **1,471,932** & 28.45 & **973,695** & 97.82 \\ Best subset (warm start + MIP) & 1,570,721 & 28.35 & 1,015,076 & 97.69 \\ Best subset (warm start + MIP) & 1,570,681 & 100.00 & 999,294 & 250.00 \\ \hline  & \(k=25\) & & \(k=50\) & \\  & RSS & Time (s) & RSS & Time (s) \\ \hline Stepwise regression & 829,608 & 38.45 & 623,118 & 132.24 \\ CG with stepwise initialization & **791,960** & 440.14 & **609,722** & 1409.88 \\ Best subset (warm start + MIP) & 819,911 & 441.75 & 611,207 & 1413.88 \\ Best subset (warm start + MIP) & 807,925 & 1000.00 & 610,542 & 3600.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of stepwise regression, CG, and best subset with different time limits.  
Figure 4: Improvements from carousel greedy with stepwise initialization for the Building dataset.
# 5G Mobile Phone Network Introduction in Colombia
## 1 Introduction  
In recent years, the explosive growth in the volume of communications data has exhausted the possibilities of current 4G mobile communications systems. Wireless data traffic is predicted to be more than 131 exabytes per month by the end of 2024, with more than 90% of wireless data traffic coming from cell phones. With the emergence of fifth generation (5G) mobile networks, network performance is improved to between \(100\times\) and \(1000\times\) as compared to existing technologies [1]. This is achieved by increasing spectral efficiency that provides massive connectivity, higher transmission speeds, shorter latency, and lower energy consumption [2]. It enables cloud-based applications, increasing the prospects for emerging technologies such as virtual reality (VR), smart cities, and autonomous car applications that adapt to the rapid evolution of the Internet of Things (IoT) and machine-to-machine (M2M) communication [3, 4, 5, 6]. Meanwhile, software-driven network integration (SDN) and network functions virtualisation (NFV) are considered effective solutions in the optimal performance of 5G network functionality with high scalability. They run on a cloud-based architecture that leverages the 5G core network architecture (5G-CN) model to provide versatile network traffic management with distributed multi-controller architecture delivering a wide range of services [7].  
The aim of our work is to show the relationship between the 5G mobile network and the multiple input/multiple output (MIMO) systems, the millimeter wave (mm-W), the frequencies from 30 GHz up to 300 GHz, and the Sub-6 GHz bands, describing theirmain advantages, challenges, and supporting technology that allows us to achieve higher data transfer rates when compared to previous mobile networks based on 4G. The original document supporting the understanding of this topic was taken from a systematic search and review of the Scopus database. In addition, information has been gathered from the official websites and documents of the Colombian Ministry of Information and Communication Technologies, the National Spectrum Agency of Colombia (ANE), the Global System for Mobile Communications (GSM Association), the Global mobile Suppliers Association (GSA), and the mobile operators, which will manage 5G mobile networks. The Government resolutions have been integrated in order to reveal the steps that the Colombian government is taking to implement the 5G mobile telephone network and the current status of its deployment in Colombia.
# Novel Designated Ownership Transfer with Grouping Proof
## 1 Introduction
Although [PERSON]'s protocol can ensure the simultaneous presence of a group of tags during ownership transfer, it suffers from performance problems. For example, it requires mutual authentication to be performed twice. In this paper, we propose a novel designated ownership transfer protocol with grouping proof that can ensure message integrity and privacy. Although the ownership transfer and grouping proof protocols are designed for different purposes, they have similarities, such as requiring authentication before information is exchanged and generating random numbers to derive fresh messages. By combining the similarities between the protocols, the proposed protocol not only retains the main security and privacy features of the grouping proof and the ownership transfer protocols but also reduces the number of message or responses needed, because ownership transfer and proof generation are performed in one attempt. The remainder of this paper is organized as follows. The proposed protocol is described in Section 2, and a security analysis of the proposed protocol is given in Section 3. Finally, we provide security analysis and a conclusion in Sections 4 and 5, respectively.