esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Diagnosis of the Inter-Turn Shorts  
Figure 5 shows the input (V1) and response (V2) voltage waveforms of various cases investigated for diagnosis of inter-turn shorts. The tests were conducted by applying a switching impulse voltage of magnitude 5 kV (peak) as the input. The waveforms were observed before and after emulating the inter-turn shorts. Figure 5a shows the voltage waveforms when the 2400 \(\Omega\) was not included between the tappings and it represents the signature pattern of the transformer under its healthy condition. Figure 5b shows the waveform when the inter-turn short (Sh-1) was emulated by inserting the 2400 \(\Omega\) between the tappings t1 and t2. Figure 5c shows the waveform when the inter-turn short (Sh-2) was emulated by inserting the 2400 \(\Omega\) between the tappings t2 and t3. Because of the inter-turn shorts, there were some changes in the shapes of (V1) and (V2). Simultaneously, sparks was also visible across the electrodes of the PD model and confirmed the occurrence of PD.  
EEV TF magnitudes for different frequency components were calculated from the FRA data, as per the Equation (1). By taking these magnitudes (dB) in Y-axis and their corresponding frequency in X-axis, the EEV TF plots for the various cases are listed in Table 1.  
Figure 6 shows the EEV TF plots developed for analyzing the usefulness of IFRA approach in the diagnosis of inter-turn shorts. The spikes (dips and ups) and changes in the magnitudes of EEV TF throughout the frequency spectra, which indicates that the FRA conducted at low test voltage level itself is capable of detecting the presence of inter-turn shorts.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S.No** & **Statistical Parameters** & **Formula Used** &
\begin{tabular}{c} **Ideal Value Expected** \\ **(When Two Cases Compared Are Similar)** \\ \end{tabular} \\ \hline
1 & CSD [32; 33] & \(CSD_{x,g}=\sqrt{\frac{\sum_{i=1}^{N}\left|\left(\left(x-\hat{x}\right)^{2}- \left(y-g\right)^{2}\right)\right|}{N-1}}\) & 0 \\
2 & DABS [32; 34] & \(DABS_{x,y}=\frac{\sum_{i=1}^{N}\left|\left(y-x\right)\right|}{N}\) & 0 \\
3 & MM ratio [35] & \(MM_{x,y}=\frac{\sum_{i=1}^{N}\min\left|x\right|\left|y\right|}{\sum_{i=1}^{N} \max\left|x\right|\left|y\right|}\) & 1 \\  & (absolute) & & \\ \hline \hline \end{tabular} Note: \(x_{i}\) and \(y_{i}\) indicate \(i^{th}\) values of the two cases compared. \(\left|\left.\hat{x}\right|\right.\) and \(\left|\left.\hat{g}\right|\right.\) indicate their respective absolute values. \(\hat{x}\) and \(\hat{g}\) indicate their average values and ‘N’ indicates number of frequencies within a sub-band.  
\end{table}
Table 2: Statistical parameters, notations, formula, and their ideal values.  
Figure 5: Comparison of the input and response voltage waveforms for the inter-turn short diagnosis: (a) Normal case (b) Sh-1 (c) Sh-2.
# Visualization of a Multidimensional Point Cloud as a 3D Swarm of Avatars
## 3 Methodology
### Implementation  
Based on the conceptual framework presented in the previous section, the practical implementation of the proposed visualization method involves constructing appropriate transformation matrices, performing dimensionality reduction where necessary, and assigning selected data dimensions to spatial coordinates or avatar visual features.  
The implementation uses the Eigen library (C++), which allows efficient operations on matrices and vectors.  
The input data are stored in a matrix of size \(n\times N\):  
Eigen::MatrixXd X(n, N);  
Each column corresponds to a single point in the data space \(\mathbb{R}^{n}\), and each row represents one of the \(n\) dimensions in that space.
# Lactoferrin Production: A Systematic Review of the Latest Analytical Methods
## 1 Introduction  
### Lactoferrin Structure  
Lactoferrin (Lf) is one of the main proteins of mammalian milk whey [1]. Typically, human milk contains approximately 1-2 mg/mL Lf, whereas cow's milk contains 1-10 ug/mL [2]. It was first reported in 1939 [3] and was subsequently isolated and found to have a similar structure to serum transferrin [4]. It is a glycoprotein consisting of one polypeptide chain with a molecular weight of 80 kDa [1]. Human Lf consists of 691 amino acids, whereas bovine Lf comprises 696 amino acids [5]. The two globular lobes, carboxyl(C) and amino (N), are joined by an alpha-helix and comprise two domains, C1 and C2, N1 and N2, folded into a beta-sheet [1; 5]. The structure visualisation is shown in Figure 1. Each lobe can bind one ferric ion, creating a closed holo-Lf, but when no iron is bound, it creates the open-form apo-Lf [6]. Its positive charge facilitates binding to anionic compounds [5]. Its sensitivity to proteolysis and denaturation is determined by the number of glycosylation sites and their location [7].  
Human and bovine Lf show about 70% structural similarity, suggesting that Lf performs similar bioactive functions in mammals; thus, bovine Lf is a good source of Lf for human use [5].  
The Lf concentration in milk depends primarily on the lactation stage [9], with the highest concentration in colostrum (7 mg/mL in human colostrum vs. 2 mg/mL in buffalo colostrum) [2; 10]. The average Lf concentrations in milk products are presented in Table 1.
## 5 Integrating Language Modeling with STEP
### Lemmatization  
As outlined above, often the NCBO Annotator produces no annotations for terms that vary from their base or dictionary form (_lemma_). We have therefore performed _Lemmatization_[20] on micro-contributions prior to extracting concepts using the NCBO Annotator. Lemmatization, which is the algorithmic process of determining the _lemma (base or dictionary form)_ for a given word, improves the accuracy of practical information extraction tasks. Morphological analysis of biomedical text can yield useful results and is more effective when performed by a specialized lemmatization program for biomedicine [21]. In our experiments we have used the _BioLemmatizer_[22], to conduct morphological processing of micro-contributions prior to concept extraction. It is important to note the distinction between _stemming_ and _lemmatization_; a stemmer operates on a single word without knowledge of the context, and therefore cannot discriminate between words that have different meanings depending on part of speech. We have therefore opted for pre-processing micro-contributions by lemmatization as it involves understanding _context_ and determining the part of speech of a word in a sentence. This is especially important in the concept consolidation phase, where groups of concepts are consolidated and integrated based on annotations common to these groups; common annotations among multiple concept clusters result from the annotation of _lexically different_ but _semantically similar_ entities, where _semantic similarity_ is detected from the _context_ in which they appear. Lemmatization increases the number of annotated terms in micro-contributions of an expert, which can potentially increase the number of common concepts among groups of concepts, leading to an increase in the number of consolidated concept clusters. This will in turn prevent loss of semantics and inaccurate ranking of concepts in expertise profiles.
# A Ka-Band Cylindrical Paneled Reflectarray Antenna
## 2 Design Principle
Figure 2: Single cell characterization at 20 GHz: (**a**) Simulation model of an elementary cell large \(W=\lambda\)/3 (where \(\lambda\) is computed at 20 GHz); (**b**) phase of the reflected field for different angles of incidence; (**c**) amplitude of the reflected field. (Rogers corp., Chandler, AZ, USA) dielectric slab with a thickness of 0.9822 mm. The field incident to each cell was estimated [13] as  
\[a_{ij}=P_{in}\frac{\lambda}{4\pi r_{ij}}e^{-j(2\pi r_{ij}/\lambda)}H_{\rm F} \Big{(}\theta_{ij}\Big{)}H_{EI}(\theta) \tag{4}\]  
where \(P_{in}\) is the total input power, \(H_{\rm F}\) is the hat-feed radiation pattern, \(H_{EI}\) is the single element radiation pattern, and \(\theta\) is the direction of radiation. For the case at hand, \(H_{\rm F}\) was modelled as \(\cos^{2}\!\left(\theta_{ij}\right)\) and \(H_{EI}\) was modelled as \(\cos(\theta)\). The phase of the field reflected by each cell was evaluated as  
\[b_{ij}=a_{ij}S_{11} \tag{5}\]  
where \(S_{11}\) is the reflection coefficient of each cell simulated using the configuration shown in Figure 2a. As it can be observed in Figure 2, fixing the inter-element spacing \(W\) to \(\lambda\)/3 (where \(\lambda\) is computed at 20 GHz), the phase of the reflected field can be fully controlled by varying the width of the strip. The relation between the incident and reflected field, evaluated at different angles of incidence \(\mathbf{\theta}_{ij}\), is then employed to design the reflectarray and to estimate its gain using Equations (1) and (3), respectively.
## 3 Hierarchical Cache Architecture Design  
To bypass the line-speed limitations of ICN in-network caching while providing terabyte-level single-node cache capacity, we adopt a two-tier cache architecture combining DRAM and SSD. As shown in Figure 1, we follow the design of the split architecture from [13], dividing each ICN cache router into two functional modules: a forward module and a cache module. By decoupling packet forwarding and caching operations on the timescale, this approach addresses the issue of SSDs' slow I/O blocking the forwarding pipeline. Specifically, the forward module is responsible for operations such as name-based routing and packet forwarding, while the cache module handles cache-related operations such as replica management and data request service. The cache module consists of two types of storage media: DRAM and SSD. The DRAM cache space is further divided into four parts. One part is used to store the indexes of replicas in the L1 and L2 cache layers, another part is used to store the DRAM virtual cache queue, a third part is used to store the Replica Information Table (RIT), and the remaining space is used to store the actual replica data. The first three parts occupy only a small amount of space, and their specific implementation will be introduced in Section 4. The SSD is deployed in a scalable manner, allowing network administrators to adjust the number of SSDs based on cache capacity requirements. The SSD cache space primarily holds the replicas evicted from the DRAM cache to ensure the exclusivity of the hierarchical cache system [44], meaning that the same replica is not cached in multiple layers.  
Figure 1: Illustration of hierarchical cache architecture.  
The proposed architecture involves three core operations: cache insertion, cache hit, and cache miss, as indicated by the arrows in Figure 1. Specifically, when a new data packet is forwarded to the node, the cache module is responsible for establishing an index and storing the corresponding replica data. When a new request packet is forwarded to the node, it queries the index table in DRAM. If the request hits any cache level, the corresponding replica data are read from the cache device indicated by the index, and the forward module encapsulates it into an ICN data packet to be returned to the requesting node, while updating the corresponding entry in the RIT. If the request misses at any cache level, the forward module forwards the packet to other cache nodes or original servers based on the name-based routing protocol.
## 3 Results and Discussion
### Adsorption Kinetics
This multi-linearity did not pass through the origin. The initial linear region is attributed to the film diffusion, while the intercept corresponded to the film thickness. The second linear line describes the intra-particle diffusion stage and the final horizontal linear line describes the adsorption step. Normally, the adsorption step is assumed to be very fast, and then it is not the controlling step. From this plot, it can be said that the intra-particle diffusion plays an important role in the rate-controlling step in the process, but it is not the sole rate-determining factor due to the deviation of the curve from the origin and multi-linearity distribution with different slopes [35]. To identify the importance of film diffusion, the Boyd kinetic model, as shown in Equation (12), was then performed:  
\[\rm F=q_{t}/q_{e}=1-(6/\pi^{2})\exp(-B_{t}), \tag{12}\]  
where \(\rm F\) is the fraction of dye adsorbed at time \(\rm t\) and \(\rm B_{t}\) is the Boyd's constant, which presents the mathematical function of \(\rm F\) as shown in Equations (13) and (14):  
\[\rm B_{t}=\left\{\pi^{0.5}-[\pi-(\pi^{2}\,F/3)]^{0.5}\right\}^{2}\,for\,\,0 \,\,<\,\,F\,\,<\,\,0.85, \tag{13}\]  
\[\rm B_{t}=-0.4977-\ln(1-F)\,\,for\,\,F>0.85. \tag{14}\]  
All other symbols are the same as above. From Figure 8, it was observed that the plots were linear but did not pass through the origin, signifying that the film diffusion has a significant influence on the dye adsorption [32; 33; 34; 35; 16]. Thus, it can be said that the adsorption of dye on the MCC was controlled by both film and intra-particle diffusion [40; 41]. Figure 8: Boyd’s kinetic parameters for the adsorption of RR120 on the MCC. Figure 7: Intra-particle diffusion model plots for the RR120 adsorption on the MCC.
## 3 Results
### End-to-End Pipeline
Additionally, the specificity of 87.33% for adenomas indicates that the pipeline correctly identifies non-adenomas as not adenomas in 87.33% of cases. These results underline the pipeline's capability to classify adenomas with high reliability, which is the primary focus of this study. In addition to presenting the quantitative results of our pipeline, it is essential to highlight qualitative examples of its performance. Figure 3 showcases correct detections and classifications of adenomas across different patients, highlighting the diversity in adenoma sizes, shapes, and imaging characteristics. The first row of the figure shows T1-W CS OOP slices and the second row presents T1-W CE slices. The green bounding boxes represent the medical annotations and the red boxes represent the model's predictions. Notably, the model successfully identifies very small adenomas (e.g., Figure 3a,d) with more elongated, horizontal shapes, and larger adenomas with more square-like  
Figure 3: Examples of true positive detections for adenomas across different MRI sequences and different patients. Each row corresponds to a different MRI sequence: the first row shows T1-Weighted Chemical Shift (Out-of-Phase) images, and the second row shows T1-Weighted Contrast-Enhanced images. Green bounding boxes are the ground truth; red is the high-score prediction. Subfigures are labeled (**a–f**) for reference. shapes (e.g., Figure 3c,f). Furthermore, the pipeline's robustness in handling variations in image intensity is evident, highlighting its capacity to adapt to diverse imaging conditions, including contrast-enhanced and non-contrast-enhanced sequences. While the pipeline demonstrates strong performance in detecting and classifying adrenal lesions, examining its limitations through examples of incorrect detections and classifications is crucial. Figure 4 presents six subfigures showcasing these errors, organized by the ground truth labels: non-lesion, non-adenoma, and adenoma. For each ground truth, a pair of images illustrates specific challenges: misclassifications, false detections, and missed lesions. All subfigures show the same MRI sequence--T1W CS OOP.
## 3 Results and Discussion
### Characterization of Gypsum/RT18 HC@AC Composite Boards
#### 3.2.2 Thermal Properties of GB/RT18 HC@AC Composite Boards  
The results of the thermal conductivity and specific heat capacity measurements for the reference gypsum board and composite GB/RT18 HC@AC gypsum board with varying RT18 HC@AC contents are summarized in Table S2. Also, Figure 8 presents the graphical correlation of the thermal conductivity (k) and specific heat capacity (Cp) with the increase in percentage content of RT18 HC@AC additive. As depicted in the data, an incremental rise in the percentage content of additive corresponds to noticeable variations in these parameters. Particularly, a positive correlation is observed, signifying an enhancement in the thermal conductivity and specific heat capacity with a higher additive content. This positive relationship is interpreted as the positive contribution of the RT18 HC@AC component to the heat transfer capabilities within the composite gypsum boards.
## 3 Methodology
### SAR Data Preprocessing  
To reduce the effect of speckle on the calibrated RADARSAT-2 images, the fine quad and spotlight mode images were filtered using a \(5\times 5\) and \(7\times 7\) Gamma filter, respectively. Prior to the analysis of remotely sensed data, it is critical that all images are geometrically corrected with the highest accuracy possible. All RADARSAT-2 images were orthorectified using [PERSON]'s rigorous physical model [35], while incorporating Natural Resources Canada's 1:50,000 Digital Elevation Models (DEM) [36].  
As shown in Figure 4a, the PQ orthoimages acquired from different orbits aligned at the pixel level with a relative accuracy of 5 m. Since there are no data available with higher accuracy, the absolute accuracy could not be evaluated. However, the orthoimages were compared with Canada 1:50,000 topographic data (CanVec data) [37] (Figure 4b) from 18 August 2001 which contain a horizontal accuracy of 20-30 m. CanVec is a multi-source product provided by the National Topographic Data Base (NTDB). Additionally, in some locations the CanVec data were also updated using satellite imagery by the CCMEO. The CanVec products used for this study include NTDB areas 107B06 and 107B03. Aside from areas which experienced temporal change, no large errors were noticed on the orthoimages relative to the CanVec data. Since the CanVec data are not up to date, there are temporal differences between the 2014 image and the 2001 CanVec data. These temporal differences are noted in Figure 4b, but the inland lakes that have less erosion match with both datasets, demonstrating the good relative accuracy of the orthoimages.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 2 Background and Related Research
### Nanogrids  
Nanogrids are generally much smaller than microgrids. While an average military microgrid may serve several dozens or hundreds of loads and operate in the 5-500 megawatt range, nanogrids often serve on the order of one to five loads and a few kilowatts up to 5-10 megawatts. Nanogrids generally are technologically simpler than microgrids because they only serve a single building or a few loads [17]. The smaller, simpler design of nanogrids usually allows power production to occur much closer to the point of consumption versus grid infrastructure and even military microgrids which sometimes can encompass many hundreds of square kilometers of service territory. Generation occurring at the point of use significantly reduces the potential negative effects of transmission and distribution lines such as loss of efficiency, single point of failure, etc. [18]. Certain nanogrid configurations (discussed later) where the nanogrid is connected to a microgrid or grid via its own PCC can be very fault-tolerant where they are able to successfully transition to island-mode and continue serving critical loads until microgrid and/or grid power is restored. Many of the same benefits found with the implementation of military microgrids are expected to be realized with the deployment of nanogrids on military bases.  
It is important to note common technical characteristics of nanogrids. Nanogrid voltage levels are often lower than microgrids and often are in the 100-500 volt range for both AC and DC nanogrids. Power generation and consumption is often much smaller in a nanogrid than a microgrid with ranges from 1 watt for the smallest nanogrids powering extremely small loads up to 1 megawatt; however, the upper limit can vary and is defined by the entity implementing the nanogrid [19]. Typical nanogrid loads are on the scale of a single appliance or computer up to a building [17]. Nanogrids can sometimes be used to improve electrical efficiency by 5-13% in some residential applications versus other options [19]. A common nanogrid system architecture uses DC because using DC can be more efficient due to many nanogrid power sources producing DC power and many storage systems (Energy Storage System (ESS)) using DC power which results in fewer inverters and converters [18,19]. However, while DC nanogrids can increase efficiency, they also come with the consequence of needing enhanced protection against short circuit line faults and ground faults [18]. The mitigation of faults is done with arching-type circuit breakers or more advance tactics that involve special mechanical circuit breakers that open against fault currents by forcing currents to zero by external means to extinguish arcs [20].
## 4 Experimental Validation
### Precision Evaluation  
As with the effectiveness evaluation result mentioned above, the RGB-D-based method and SSSE provide effective skeleton extraction results at different distance ranges. Precisions of all extracted joints by SSSE are determined by the deviation values relative to corresponding ground truth joint positions. In the precision evaluation procedure, two Kinects are setup for different purposes. Kinect No.1 is set up 9 m way from human object, capturing RGB frames for human skeletonization based on SSSE. Kinect No.2 is setup 3 m away from human object, capturing RGB-D frames along with 3D human skeletons simultaneously. Since 3 m is inside the effective range of the RGB-D-based 3D skeletonization, the 3D joint positions captured by Kinect No.2 are valid joints, providing ground truth for the deviation calculation. Based on the experimental scenario setup, 1546 frames are captured simultaneously for both methods, of which 1345 effective frames are evaluated.  
For each skeleton extracted from a effective frame, joint positions are normalized relative to the hip center, avoiding deviation introduced by different shot distances.  
Six major joints are considered in evaluation, including head, spine, both keens and both feet. Figure 7 depicts the averaged precision evaluation result.  
As presented in Figure 7, due to the larger scale of upper body shadow on the ground, relative high deviations appear at joints of the head and spine, where averaged deviations reach 14.5 cm and 12.1 cm, respectively. For the remaining joints, the averaged deviations are around 4 cm and the highest deviation remains below 8 cm. In summary, SSSE extracts joint positions in a reasonable precision at 9 m away from the target human, compared with the ground truth Kinect skeletonization result obtained at a position 6 m closer to the subject human.