esa-sceva/satcom-chunk-collection huggingface dataset:
# Discovering Trends in the Digitalization of Shipping:
## 3 Methodology  
The general field of this research is natural language processing. NLP is a subfield of Artificial Intelligence (AI) concerned with the interaction between computers and human language. It aims to develop computational techniques that enable computers to understand, interpret, and manipulate natural language in its various forms (spoken, written) [17].  
In the domain of Natural Language Processing (NLP), a suite of techniques exists to unlock meaning and structure from textual data. Keyword extraction serves as the foundation, identifying the most significant words or phrases that illuminate the core content of a document. Sentiment analysis delves deeper, classifying the emotional tone--positive, negative, or neutral--expressed within the text. Topic modeling ventures beyond individual documents, uncovering hidden thematic structures that bind a collection together, revealing underlying topics. Named Entity Recognition (NER) homes in on specific elements within the text, identifying and classifying named entities like people, organizations, and locations. Finally, text summarization automates the creation of concise summaries, capturing the essence of a document while streamlining its length.  
This paper aims to extract significant trends and identify emerging weak signals from a collection of PDF-embedded presentations, grouped by year, sourced from a website focused on digitalization in the shipping industry. The website provides open access to an archive of presentations made by industry practitioners over a 10 years plus period. As such, the content reflects the ongoing efforts made in real world terms and addresses the issue from a practicality point of view rather than from a theoretical or hypothetical point of view.  
Our methodology is aligned with the analytical approaches of [PERSON] [7; 8], and [PERSON] and [PERSON] [18], employing Bidirectional Encoder Representations from Transformers (BERT) to unearth new insights.  
To unearth weak signals, we employed an increase ratio of words strategy, consistent with the methods of [PERSON] [19], and [PERSON] [20]. Our study leveraged a semiotic model to assess the signal and issue dimensions through the degree of visibility (DoV) and the degree of diffusion (DoD), reflecting the frequency and distribution of keywords, respectively.  
Our research process began with acquiring and preprocessing text data, followed by classifying and extracting keywords, which bifurcates into four categories: principal topics, fine-grained topics, topic-related keywords, and document-specific keywords. These classifications aid in identifying primary and emerging trends, and in the modeling and detection of weak signals.  
For classification, we utilized BERT, specifically BERTopic [21; 22; 23], for topic definition and KeyBERT [24] for the keyword extraction from documents. Guided topic modeling is enhanced through similarity calculations, employing methods like Spacy cosine similarity, Scikit-learn cosine similarity, and Jaccard similarity [25; 26; 27].
## 2 Frame Model Design and Structural Analysis
### Establishing Finite Element Model  
#### 2.2.1 Finite Element Theory  
1. The structure is discretized, transforming the ductile continuous medium into a relatively limited number of simple basic elements. These elements are generated based on node connections, and the load is also transmitted through these nodes. Different types and numbers of elements can be selected during the entire process of discretization, which directly affects the accuracy and stability of the numerical values in the finite element analysis of the solid model;
2. After the discretization is constructed, to better represent the displacement, stress, and strain of the element with the nodal displacement, the displacement function formula can be used for description: \[\mathbf{f}=\mathbf{N}\mathbf{\hat{e}}^{\mathbf{e}}\] (1) In the equation: \(\mathbf{f}\) refers to the displacement column vector of a point in the element. \(\mathbf{N}\) refers to the shape function matrix of the element. \(\mathbf{\hat{e}}^{\mathbf{e}}\) refers to the node displacement column vector of an element. According to the geometric equation in elasticity, the element strain correlation indicated by the element node displacement column vector is deduced: \[\mathbf{\varepsilon}=\mathbf{B}\mathbf{\hat{e}}^{\mathbf{e}}\] (2) In the equation: \(\mathbf{\varepsilon}\) refers to the strain column vector at any point in the element. \(\mathbf{B}\) refers to the strain matrix of the element.  
Based on the principle of virtual work, the relationship between node load and node displacement of the function on the element was created, and the bending stiffness equation of the element was derived:  
\[\mathbf{R}^{\mathbf{e}}=\mathbf{K}\mathbf{\hat{e}}^{\mathbf{e}} \tag{3}\]  
where \(\mathbf{K}\) refers to the stiffness matrix of the element, \(\mathbf{\hat{e}}\) refers to all constructed node displacement space column vectors, and \(\mathbf{R}\) refers to the load column vector of the overall structure.
3. Solve, synthesize all bending stiffness equations, and obtain the equilibrium equations for all constructions, namely: \[\mathbf{R}=\mathbf{K}\mathbf{\hat{e}}\] (4) The bending stiffness equation is expressed as: \[\mathbf{K}=\mathbf{E}\cdot(1-\mathbf{v})/[(1+\mathbf{v})\cdot(1-2\mathbf{v}) \cdot\mathbf{C}\] (5) where \(\mathbf{E}\) is the [PERSON]'s modulus matrix, \(\mathbf{v}\) is Poisson's ratio, and \(\mathbf{C}\) is the linear elastic stiffness matrix of the element.  
In order to introduce the penalty parameter, \(\mathbf{E}\) can be modified to obtain [PERSON]'s modulus matrix after penalty:  
\[\mathbf{E}^{*}=\mathbf{E}\cdot\mathbf{x}^{\mathbf{p}} \tag{6}\]among them, E\({}^{*}\) is [PERSON]'s modulus matrix after penalty, x is the design variable in topology optimization, and p is the penalty parameter. By adjusting the penalty parameter, the continuity and optimization degree of the material can be controlled. When the penalty parameter p approaches infinity, some parts of the material are disabled to optimize the structure. When p is equal to 1, the continuity of the material is completely maintained.
# The Impact of Pitch Error on the Dynamics and Transmission Error of Gear Drives
## 4 Discussion
### Design Implications for High-Speed E-Drive Gears  
* Pitch tolerances are as critical as flank form CpK. Even a few micrometers of spacing error generated a shaft order tone that masks any benefit of ideal flank design.
* Digital twins must capture both micro-geometry and pitch deviation; omitting the latter underpredicts peak-to-peak TE by roughly a factor of two, as shown in Figure 1. Mesh side-bands offer a rapid audit metric; the \(\pm\) 16.66 Hz skirts around the 383 Hz mesh-frequency line, as shown in Figure 2, and can be monitored during production tests to flag excessive pitch error [11].  
The relevance of pitch quality is also reflected in international standards. ISO 1328-1:2013 [18] specifies that the single-pitch error for precision class 5 helical gears should not exceed 4 \(\upmu\)m at the reference diameter, while DIN 3967 [19] recommends even tighter limits for e-drive noise classes. Our simulation indicates that exceeding these values by only two to three micrometers already doubles peak-to-peak TE, confirming that the tolerance bands prescribed by the standards are well aligned with NVH requirements [18; 19].
## 4 Experimental Results and Discussion
### Output Power Fluctuation and Longitudinal Mode Shift of the SLM Laser  
#### 4.2.1 Results About Output Power Fluctuation and Longitudinal Mode Drift  
In the process of SLM power measurement in Figure 2, we observed the longitudinal mode drift and the resulting output power fluctuation. Then, we recorded these two changes with the insertion of 4% and 80% reflectivity K9 etalons in the laser. The results in those two conditions are plotted in Figure 3.  
In Figure 3a,d, gray lines represent the output power fluctuation, and, for clear reading, red lines are 2000 points Savitzky-Golay smooth of the output power. The output power fluctuation with 4% reflectivity K9-glass etalon was captured at the pump power of 1.1 W. From Figure 3a, this fluctuation had quasi-periodicity, and its period was about 1.5 min. Figure 3b was the partially enlarged drawing of Figure 3a in one period. The laser was at SLM operation at the peak and valley of Figure 3b (covered by purple and pink frames, respectively). Nevertheless, the oscillating longitudinal modes at two moments were not the same longitudinal mode, as depicted in Figure 3c. Those two longitudinal modes oscillated simultaneously during the intervals between peak and valley values in Figure 3b. We adopted a Fabry-Perot interferometer to measure the waveform of longitudinal modes. The free spectral range (FSR) of the Fabry-Perot interferometer was 10 GHz, and the corresponding measurement time was 3.9 ms. As the measured time difference between the two longitudinal modes Figure 3c was 0.77 ms, the calculated frequency difference was 1.97 GHz.  
The power fluctuation with 80% reflectivity etalon was different. For nearly 40 min of measurement in Figure 3d, the output power dropped drastically at three moments. We captured an apparent longitudinal-mode drift in 10-25 min, as displayed in Figure 3e. A mode-hop occurred after the oscillating longitudinal mode continuously drifted 0.66 GHz. The frequency separation between the two longitudinal modes was 2.05 GHz.  
Figure 3: The laser’s output power fluctuation and longitudinal mode shift with (**a**–**c**) 4% and (**d**,**e**) 80% K9 etalon.
## 3 Results and Discussions  
### Proximate Composition of Flows  
The proximate composition of WF and RFSF is reported in Table 2. The data show that the two flours have complementary nutritional profiles. The moisture content of any flour is an important quality criterion for its preservation, packaging and transport as well. WF had eight times more humidity than RFSF (14.55% compared to 1.64%, respectively). The low moisture content for RFSF can be explained by the prior roasting process which removed most of the sample's water content. Comparison of the carbohydrate content of the two samples presented a similar pattern: WF presented high levels of carbohydrates (74.45%), whereas RFSF contained 21.76% of total carbohydrate. However, RFSF presented higher levels of protein (21.40%), fiber (8.75%) and fat content (42.50%) compared to WF.  
Therefore, a composite flour can be successfully used to obtain biscuits with improved nutritional value. These results are in agreement with those obtained by [PERSON] et al. [26] and [PERSON] [50] who found the fat content of flaxseed flour was 38.2 and 40.5%. The protein, crude fiber, and ash content of RFSF, in our study, was observed to be 21.40%, 8.75%, and 3.95%, respectively. These values correlated well with the values reported earlier by [PERSON] et al. [19], [PERSON] et al. [26], [PERSON] [29] and [PERSON] [50].  
The valuable chemical composition of RFSF along with the high carbohydrate content of WF, especially starch, led to a composite flour with good characteristics for biscuit manufacturing. [PERSON] and [PERSON] [51] showed that flour with high carbohydrate content and low in gluten is highly recommended for biscuit manufacturing. Therefore, we considered that the composite flours with 25% RFSF could be successfully used for further processing. WF contributes to the final characteristics of biscuits with the pasting properties of the starch and with the gluten content. Moreover, the high protein content of RFSF could contribute also to the pasting property on the finished product and to the dough viscosity, as it was reported previously in the development of protein-enriched composite flour for biscuits production [52].
## 4 Simulation Setup
### Aerodynamic Disturbance Model
#### 4.1.2 Aerodynamic Force on the UAV  
The wind velocity can be calculated through Equations (30)-(33). In order to compute the aerodynamic force acting on the UAV due to the wind velocity, the model described in [31] is adopted. This model has the advantage of being simple to implement compared to other ones that can be found in the literature. The model gives the intensity of the force exerted on the UAV, \(F_{w}\), as a function of the wind velocity:  
\[F_{w}=S_{e}Aw^{2} \tag{34}\]  
where \(S_{e}\) is the effective area of influence, and \(A=0.61\mathrm{kg/m^{3}}\)[27]. For simulation purposes, we use the components of Formula (34) as projections on the axes of the inertial frame:  
\[\begin{array}{l}F_{w_{x}}(t)=S_{e_{x}}A\left(w_{x}^{2}(t)+w_{z}^{2}(t) \right)cos\psi_{w}\\ F_{w_{z}}(t)=S_{e_{x}}A\left(w_{x}^{2}(t)+w_{z}^{2}(t)\right)sin\psi_{w}\end{array} \tag{35}\]  
where \(\psi_{w}\) is the inclination angle of the wind velocity vector with respect to the horizontal, and \(S_{e_{x}}\) and \(S_{e_{z}}\) represent the projections of the effective area on the planes perpendicular to the \(x_{I}\) and \(z_{I}\) axes, respectively. For simplicity's sake, the UAV is represented as a cylinder in the computation of the effective area. The expressions of the effective area projections are as follows [31]:  
\[\begin{array}{l}S_{e_{x}}=\beta\pi r^{2}sin\theta+\alpha\pi rh\ cos\theta\\ S_{e_{z}}=\beta\pi r^{2}sin\phi+\alpha\pi rh\ cos\phi\end{array} \tag{36}\]  
where \(\theta\) and \(\phi\) are the pitch and roll angles, respectively, \(\alpha\) and \(\beta\) are the fill factors for the cylinder area (introduced to take into account that the UAV does not fill the entire cylindrical volume in which we inscribe it, and they depend on the UAV design), \(r\) is the cylinder radius, and \(h\) is the cylinder height. The parameters used in Formula (36) are listed in Table 3. The values of the fill factors \(\alpha\) and \(\beta\) are derived from the DJI S1000 CAD model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Cylinder radius, \(r\) & 454 mm \\ \hline Cylinder height, \(h\) & 175 mm \\ \hline Base area fill factor, \(\alpha\) & 0.2736 \\ \hline Lateral area fill factor, \(\beta\) & 0.2768 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters in Formula (36).  
In this work, the roll angle variations are small and, therefore, we assume that \(S_{\varepsilon_{z}}\) remains constant. Force profiles associated with the wind speed profiles of Figure 5 are shown in Figure 6.
# A Wideband Eight-Element Antenna with High Isolation for 5G New-Radio Applications
## 3 Measurement Results and Discussion
 \tag{5}\]  
\[\rho_{ii}=\left(1-|S_{ii}|^{2}-|S_{\bar{y}j}|^{2}\right) \tag{6}\]  
\[\rho_{\bar{y}}=-\left(S_{\bar{y}i}^{*}S_{\bar{y}}+S_{\bar{y}i}^{*}S_{\bar{y}j} \right)\text{ for i,j}=1\text{ or }2 \tag{7}\]  
The computed values of ME and CCL of the presented antenna are displayed in Figures 16 and 17, which are better than 70% and smaller than 0.2 in 3.3-5 GHz. Figure 16: Computed ME of the designed antenna. Figure 15: Computed ECC and DG of the designed antenna. Table 1 provides the operating frequency band, isolation, total efficiency, values of ECC, and measurement of a single antenna compared with other referenced antennas. It demonstrates that the isolation of the designed antenna is better than the antennas which are presented in [5; 15; 17; 19; 20], and the efficiency of the presented antenna is larger than that of the published designs illustrated in [1; 5; 17]. Finally, the designed antenna has lower ECC and is more compact than other published works.
## 3 Results and Discussion
### Hr \({}^{1}\)H NMR Case Study: Time-Dependent Tracking of Aldehyde LOPs in CFOs Exposed to LSSFEs
The cubic spline analyses performed on our time-dependent aldehyde concentration datasets involved a piecewise function of third-degree polynomials; computation of the cubic spline coefficients featured polynomial derivatives [54]. Results from univariate statistical analysis performed by an ANOVA model (Equation (1)) for the ‘between-oils’, between-time-points’ and oils x time-points interaction effects were \(p=2.49\times 10^{-44}\), \(1.09\times 10^{-82}\) and \(1.17\times 10^{-38}\), respectively for _trans_-2-alkenals; \(p=5.98\times 10^{-50}\), \(1.39\times 10^{-74}\) and \(2.23\times 10^{-41}\), respectively for alka-_trans_,_trans_-2,4-dienals; and \(p=5.10\times 10^{-28}\), \(1.15\times 10^{-64}\) and \(1.04\times 10^{-16}\), respectively for _n_-alkanals. The highly significant interaction effects observed for all aldehydes indicate the differential time dependencies of their concentrations generated for each oil investigated. point, however, all three of these aldehydes were only detectable in PUFA-rich sunflower and corn oils. Mean concentrations of total _trans_-2-alkenals, alka-(_trans_,_trans_)-2,4-dienals and _n_-alkanals in the PUFA-rich sunflower oil product examined were 3.23, 3.14 and 2.04 mmol kg\({}^{-1}\), respectively at the 20 min LSSFE time-point. \({}^{1}\)H NMR spectra of heated sunflower and canola oils also contained signals assignable to aldehyde precursors, in particular _cis,trans- and trans,trans_-CHPDs (multiplet resonances located within the 5.40-6.60 and 5.40-6.30 ppm spectral regions, respectively, together with broad -OOH function signals centred at \(\delta\) = 8.40-8.85 ppm); and _cis,trans_-conjugated hydroxylenes (\(\delta\) = 5.40-6.50 ppm range), as previously reported [12; 19; 20] (data not shown).
# Software Integration of Power System Measurement Devices with AI Capabilities
## 3 Proposed Integration Framework
### Proposed Software Integration Tools and Alternatives  
Based on the sketch drawn in Section 3.2, a series of open-source tools were selected, as shown in Figure 3. Each of the indicated tools could be replaced by a similar one as long as they meet the requirements described in Section 3.1. The tools used, along with similar performance alternatives, are described below.  
InfluxDB version 2.0 was chosen as the TSDB, which is an open-source TSDB developed by the InfluxData company. It offers a comprehensive ecosystem, including the Telegraf agent [56], which is capable of receiving data from an enormous number of protocols. Another widely used open-source TSDB is MongoDB, although it does not have its own agent that is as versatile as Telegraf, so it would be necessary to resort to another type of middleware to integrate messages, such as Node-RED.InfluxDB, which appears to be the most suitable option due to its versatility [54].  
Figure 3: Proposed framework with each chosen software tool.  
The chosen PDC is OpenPDC, which is an open-source program widely used in the literature [24; 25; 57]. This data concentrator has the capability to forward the information that reaches it to other PDCs through the Streaming Telemetry Transport Protocol (STTP IEEE 2664) [58]. This protocol, still under development by the IEEE, has been optimized to exchange time series data. Due to the protocol's novelty, it has not yet been implemented in the TSDB. To address this, a middleware called STTP2 HIDS, developed by the Grid Protection Alliance (GPA), was used to convert the data into write requests for InfluxDB. There are other proprietary PDCs, such as the enhanced Phasor Data Concentrator (ePDC) developed by Electric Power Group (EPG), or the VCL-PDC by Valiant Communications. Nevertheless, OpenPDC is the only open-source PDC that has been widely validated. All of them have the same problem when transmitting data to a generic TSDB, so STTP2 HIDS would have to be used anyway. In [24], OpenHistorian, another GPA product, was used as the middleware; however, it is much more bulky and cumbersome for this purpose than STTP2 HIDS.  
Lastly, having such a popular TSDB makes it very easy to access the data. Using InfluxDB, it is possible to download the data in the CSV format to analyze them externally offline with any software, such as GNU Octave 9.3 or Python 3.11, or it is possible to access the database online with an API capable of performing periodic queries. In this case, a Python API was used. It allows data to be monitored and processed in real time. Python's versatility enables the use of well-known tools such as TensorFlow 2 or Keras 3 to perform Artificial Intelligence tasks directly on the data. Likewise, it will be possible to monitor the data in real time with a monitoring software such as Grafana 11.4.
# Recent Advances in Plasmonic Sensor-Based Fiber Optic Probes for Biological Applications
## 5 FOPS Performance Evaluation
### Figure of Merit  
Figure of merit (FOM) is one of the important parameters related to the signal-to-noise ratio (SNR) to evaluate the FOPS sensing performance by introducing the spectral width [51]. It can be given by the equation of FOM = S/FWHM, where S is the wavelength interrogation sensitivity and FWHM represents the full width at half-maximum of the spectra [52].
# A Survey on Satellite Communication System Security
## 5 Defense in Satellite Communication Systems
### Summary  
In addressing the diverse threats to SCSs, a range of countermeasures have been developed to safeguard confidentiality, availability, and integrity. For confidentiality, techniques such as beamforming, adding artificial noise, and leveraging optical communication enhance the security against eavesdropping. Cryptographic methods, including quantum key distribution, provide a foundational layer for secure communications. To ensure availability, anti-jamming strategies employ game-theoretic- and filtering-based approaches, while DoS/DDoS mitigation leverages blockchain technology, secure routing, SDN-based solutions, and collaborative defenses to protect the network infrastructure. For integrity, anti-spoofing measures analyze physical parameters, utilize hardware-dependent analysis, and apply cryptographic techniques to detect and mitigate spoofing attacks.  
As a result, the field of research on attacks and defenses is constantly advancing, driven by technological advancements and the evolving paradigm of communication systems. In addition, international politics and policies often dictate the SCS research direction, particularly on security attacks and defense. Therefore, it is important to consider international policies and strategies and keep up with communication technology trends.