esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Functional Group Characteristics of Freeze-Dried ACM
In the same way, studies by other authors revealed that amide I bands were found in the range 1600-1700 cm\({}^{-1}\) with bands that show C-O stretching vibrations of protein-peptide bonds which is consistent with the results obtained in the ACM [28]. The distinctive absorption bands of 3301 cm\({}^{-1}\), 1634 cm\({}^{-1}\) and 1539 cm\({}^{-1}\) in the ACM designates the presence of peptide bonds [28; 64; 65; 70]. ACM also showed wavelength peaks of 1136 cm\({}^{-1}\), which suggested the possibility of anti-symmetric glycoside bond, C-O strong bond stretch [29; 68; 70; 73] investigated the physicochemical characteristics of glycoprotein present in green algae and suggested that asymmetric stretch of bonds with intense bands around 1130 cm\({}^{-1}\) could be used to identify glycosidic linkages. Furthermore, FTIR analysis of brown algae polysaccharides indicated peaks around 1135 cm\({}^{-1}\) which are characteristics of vibrational stretches of the glycosidic C-O group of bio-polymers. The authors stated further that broad peaks in the range 1120-1270 cm\({}^{-1}\) pointed to S=O stretch in sulphate groups for molecular branches of alginic acid or fucoidan [29; 70]. Hence, the wavelength peak of 1136 cm\({}^{-1}\) recorded in the spectra of raw FTIR spectra of ACM could be attributed to the anti-symmetric glycoside bond. Several authors have established that the presence of the glycosidic bond in glycoproteins is the reason a glycan can covalently attach to polypeptide side-chains and form stable structures that are suitable for biological processes [74; 75; 76]. It can be inferred that the raw FTIR spectrum of ACM probably has glycosidic linkages similar to other glycoproteins.
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 13
#### 2.1.1 Mode of Operation  
With the limited bandwidth requirement, NB-IoT can be deployed in three different modes i.e., standalone, in-band, and guard-band, as depicted in Figure 2. In in-band and guard-band modes, NB-IoT occupies one PRBs of 180 KHz in LTE spectrum both in the downlink and uplink. It can also be allocated as standalone where it occupies the 200 KHz bandwidth by \"refarning\" the GSM spectrum. These flexible deployment possibilities enable fast integration and coexistence with legacy LTE and GSM systems.
# Shiga Toxin--A Model for Glycolipid-Dependent and Lectin-Driven Endocytosis
## 1 Introduction  
Shiga toxin is a pathogenic protein that is produced by _Shigella dysenteriae_, while enterohemorrhagic strains of _Escherichia coli_ secrete Shiga-like toxins, which are also termed verotoxins [1]. Notably, the verotoxins of _E. coli_ strain O157:H7 are responsible for pathological manifestations that can lead to hemolytic-uremic syndrome (HUS), the leading cause for pediatric renal failure in the world [2]. These toxins are also a threat to adults as it became apparent in 2011, when an outbreak with _E. coli_ strain O104:H4 in Germany and neighboring countries claimed dozens of adult victims, and thousands of adult patients who were hospitalized with severe symptoms. The most life-threatening extra-intestinal disease manifestations are renal failure and central nervous system complications. To date, no specific treatment options exist, and clinical management of HUS remains purely supportive [3].  
In the cytosol of target cells, the A-subunits of Shiga/verotoxins catalyze the deadenylation of position 4324 of 28S ribosomal RNA, leading to protein biosynthesis inhibition and subsequently to cell death. The A-subunits alone cannot enter cells, however. For this, they need the non-covalent interaction with receptor-binding homopentameric B-subunits. All of these toxins share a common cellular receptor: the neutral glycosphingolipid (GSL) globotriaosylceramide (Gb3 or CD77) [1].  
Toxin trafficking from the plasma membrane of target cells to cytosolic ribosomes has been particularly well studied for Shiga toxin, on which this review will be focused with an emphasis onrecent literature. The principal steps are receptor binding and toxin clustering at the plasma membrane of target cells, the formation of membrane invaginations, and tubular endocytic pits, the scission of these invaginations to form endocytic carriers, and their intracellular trafficking to endosomes and the retrograde route (Figure 1). These steps are discussed in the following sections of this review.
## 4 Results and Discussion
### Overall Results  
Table 5 summarizes the energy performance of the whole system, including the SUS and SDS, and Figure 7 shows the overall energy flow diagrams for each configuration. The following conclusions can be drawn:  
1. In all the studied configurations, the heating provided by the heat pump (Q\({}_{\rm{AHP}}\)) is always lower than that of the heat exchanger (Q\({}_{\rm{HE}}\)). In the case of the double effect chiller, it is only possible to provide heat with the external heat exchanger. The best efficiency for heating (COP\({}_{\rm{H,tot}}\)) is given by configurations (a) and (d) using single effect absorption units.
2. The production of cooling is relatively small in all the cases, with the most efficient production of cooling being that of configuration (b).
3. The system configuration DA + SE/H&C has the highest heating capacity (Q\({}_{\rm{H,TOT}}\)) but at the expense of a much higher waste heat demand. Thus, the heating efficiency is the lowest for the case configurations (e) and (f) using double absorption systems in the SUS.
4. Using a double absorption heat transformer in the SUS and double lift absorption units in the SDS in configuration (f) DA + DL/H&C ensures the highest hot water supply temperature (60 \({}^{\circ}\)C), but the heating (COP\({}_{\rm{tot}}\)) and cooling (EER\({}_{\rm{tot}}\)) efficiencies are among the lowest.
5. Configuration (d) produces cooling with quite similar performance indicators (EqES, exergetic efficiency, etc.) with respect to configuration (a) that only produces heating. However, two absorption units are required. Therefore, the cost could be a serious problem even in the case that a cooling tower is not required neither in the SUS nor in the SDS.
6. The optimal configuration should be one that does not require the rejection of heat to the ambient atmosphere as is the case of configuration (a) for the production of only heating. The best performance in terms of the avoided consumption of electricity, emissions, and exergetic efficiency is given by the configuration that provides only heating. In the case that cooling is also required  
Figure 7 illustrates the use of the energy input in each system configuration using the data provided in Table 5. The proposed system using absorption units for the transport of heat and the final distribution of it would be very useful for applications that require heating at low temperatures and relatively low amounts of cooling.
## 4 Numerical Simulation Analysis of the Hole-by-Hole blasting and Three-Hole Simultaneous Blasting
### Ways of Blasting  
To better analyze the blasting effect of the three-hole simultaneous blasting method, this study divided the numerical simulation process into two scenarios: one scenario represented the three-hole simultaneous blasting method, and the other represented the hole-by-hole blasting method.  
The three-hole simultaneous blasting method had no delay time between the holes. On the other hand, the hole-by-hole blasting method detonated three holes sequentially from left to right, with a delay time of 25 ms between holes. This set time was consistent with the delay time adopted between the onsite test sections. The delay time setting of the numerical simulation borehole is shown in Figure 14.
## 3 The Cooperative Beam Association and Power Allocation Scheme
### Complexity Analysis  
The proposed MGBA-SPA scheme solves the beam correlation sub-problem and the power allocation sub-problem successively, forming Algorithms 1 and 2. The computational complexity of Algorithm 1 is determined by the maximum number of swaps in the swap match process, which can be represented as \(O((K+1)^{2}(2 ML+L^{2}))\). In Algorithm 2, the computational complexity of using the interior-point method in CVX is \(O((KL)^{3.5}N_{i})\)[16], where \(N_{i}\) is the iteration times of the SCA method. Therefore, the total computational complexity of the MGBA-SPA scheme with the worst case is \(O((K+1)^{2}(2 ML+L^{2})))+(KL)^{3.5}N_{i})\). The proposed MGBA-SPA scheme can be calculated by the ground gateway station in polynomial time.
## 1 Introduction
Traditional methods for feature extraction include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA), Locally Linear Embedding (LLE), Vector Quantization (VQ), and Non-Negative Matrix Factorization (NMF) [15; 16; 17; 18; 19]. NMF, which was initially introduced by [PERSON] and [PERSON] and published in the _Nature_ journal, is a matrix decomposition technique that ensures all decomposed components are non-negative. This property enables additive descriptions and facilitates dimensionality reduction. In this study, NMF is utilized to extract features from time-frequency image data with the intention of reducing dimensionality while preserving differentiation information among samples [20]. Consideration of the nonlinearity and non-stationarity characteristics of CVQKD data is crucial in this study. To extract nonlinear features from the data, we employ kernel functions and compute the objective function using the L-norm. Moreover, we incorporate a graph regularization term to capture the geometric structure of the feature space, which is referred to as Kernel-Robust Manifold Non-Negative Matrix Factorization (KRMNMF). To address this, we begin by showing the fundamental structure of CVQKD and explaining the classical form of [PERSON]'s attacks. Then, we introduce the defense method for CVQKD based on neural network models and analyze its vulnerability to adversarial attacks. In our previous work [21], we proposed a defense method against adversarial attacks in CVQKD. However, this method faced challenges such as the limitations imposed by local features. To overcome these challenges, a novel defense framework that utilizes the KRMNMF method is proposed for CVQKD adversarial attacks. This method recognizesthe nonlinear and non-stationary characteristics of CVQKD signal data and addresses these issues by incorporating KRMNMF and leveraging manifold learning theory to capture the geometric structure of the feature space. This algorithm exhibits strong feature extraction capabilities compared to traditional methods. By reconstructing the samples using this algorithm, the influence of adversarial perturbations is minimized, leading to accurate classification by the CVQKD neural network model and effective adversarial defense. By employing our strategy, CVQKD network models can effectively defend against adversarial samples in real-time across various physical scenarios. Consequently, implementers of our approach can assert with confidence that they have enhanced overall security against potential attacks from adversarial samples. This paper is organized as follows: In Section 2, we first summarize the principle of the CVQKD system and then demonstrate the impact of adversarial attacks on CVQKD attack detection networks. In Section 3, we introduce the proposed KRMNMF algorithm. We provide a detailed description of the defense framework and experimental setup to demonstrate the effectiveness of our method in Section 4. Finally, A brief conclusion is given in Section 5.
## 2 Related Work
### Sensors and Ranging Technology
#### 2.5.3 LiDAR  
LiDAR (Laser Detection and Ranging) is a technology that utilizes an infrared laser beam to calculate the distance between the sensor and nearby objects. In modern LiDAR systems, a rotating mechanism sweeps the laser beam across the field of vision. The lasers emit pulses, and these pulses bounce off objects. The resulting reflections generate a collection of data points, known as a point cloud, which accurately depicts these objects in a digital state. [PERSON] and [PERSON] affirmed in [62] that LiDAR imaging systems are currently one of the most popular subjects in the optronics sector, as they will be necessary for perceiving the surroundings of any self-driving vehicle. Furthermore, a summary of characteristics of the light sources and photodetectors particular to LiDAR imaging systems most employed in real-world scenarios is also mentioned in the paper. Lastly, the authors discussed unresolved matters for the advancement of LiDAR in autonomous vehicles.  
As shown in Figure 4, the Sick TIM3 XX Series Laser Scanner LiDAR Sensor provides advanced capabilities.  
The fundamental LiDAR imaging operating principle is based on the theory used for measurement in LiDAR imaging, where depth is measured by calculating time delays in events in the light emitted by the source. Therefore, \"LiDAR is an active non-contact range detection technology, in which an optical signal is projected onto an object\" as stated in [62]. Table 5 displays the most well-known LiDAR manufacturer companies.
# Fibers of Failure: Classifying Errors in Predictive Processes
## 2 Materials and Methods  
### Topological Data Analysis (TDA)  
TDA stems from topology and displays three important properties; coordinate invariance, deformation invariance, and compression [54]. These three properties differentiate TDA from geometry-based data analysis methods.  
**Coordinate invariance:** TDA only considers the distances between data points as a notion of similarity (or dissimilarity). This means that a topological model can be rotated freely in space in order to enhance the visual analysis of the data. Compare this property with a common data analysis tool, such as principal component analysis (PCA), which ultimately decides the visual outcome of the data due to its projection of the data into maximum variance space of two or three dimensions. Figure 1a illustrates the coordinate invariance property for an arbitrary dataset.  
**Deformation invariance:** Topology explains shapes in a different way compared to geometry. For example, a sphere and a cube are identical (homeomorphic) according to topology. Likewise, a circle and an ellipse are identical. White noise is inherent to any dataset and can be considered as a deformation of the underlying distribution of the dataset. Due to the deformation invariance property, TDA is a suitable method for analyzing noisy datasets and thus presents a more accurate visualization of the underlying dataset. An example of deformation invariance is the figure-8-shaped dataset in Figure 1b).  
**Compression:** This property enables TDA to represent large datasets in a simple manner. Imagine having a dataset of millions of data points that have the shape of the letter Y. See Figure 1c). The compression property enables TDA to approximate the dataset using 4 nodes, which contains the data points, and 3 edges, which express the relations between the data points. This property makes TDA highly scalable.
# Event-Related Coherence in Visual Cortex and Brain Noise: An MEG Study
## 3 Results  
Based on the obtained normalized distributions of the source power, we calculated for each subject the average power of source activity in the visual cortex, \(D_{avg}\), in FieldTrip. It should be noted that we determined the visual cortex using the automated anatomical labeling (AAL) brain atlas [57] in FieldTrip. The average spectral power \(D_{avg}\) in the visual cortex was plotted in Figure 1 against estimated brain noise to phase synchronization (in units of inverse kurtosis) for every subject.  
One can see a linear correlation (with \(p\)-value equal to 0.039 and an \(R^{2}\)-value of 0.309) of \(D_{avg}\) and noise level, although the scatter is significant: \(D_{avg}^{min}=0.04\), \(D_{avg}^{max}=1.34\); \(Noise^{min}=0.22\), \(Noise^{max}=0.39\).  
Figure 1: The average power of source activity in the brain volume corresponding to visual cortex versus brain noise for all subjects (numbers denote the subjects). The line is a linear approximation fit (\(p=0.039\), \(R^{2}=0.309\)).  
Figure 2 shows typical distributions of normalized source power \(D\) predominantly activated within the visual cortex for subjects with low (subject 2) and high (subject 6) brain noise. Subject 6 is characterized by more pronounced high-amplitude activity spanning a larger volume in the visual cortex than subject 2.  
We will show now the results of the alternate analysis pipeline in Brainstorm. The values of average event-related coherence over visual areas V1 and V2 were compared with the same estimated brain noise as used in Figure 1 for all subjects. A linear relation was established with a \(p\)-value of 0.048 and an \(R^{2}\)-value of 0.267, as seen in Figure 3. The distributions of average event-related coherence over the cortex for typical subjects with low and high noise levels are shown in Figure 4 as per the cortical analysis in Brainstorm.  
The methodology to calculate the normalized difference of power on a 3D volume in FieldTrip was adapted to fit the 2D source model generated in Brainstorm to have a closer comparison. Figure 5 shows the corresponding linear regression model with a \(p\)-value of 0.209 and an \(R^{2}\)-value of 0.118 (\(D^{min}_{avg}=0.08\), \(D^{max}_{avg}=2.18\); \(Noise^{min}=0.22\), \(Noise^{max}=0.39\)). Although the model fails to capture any significant relation, the relative positions of the subjects in the power-noise state-space of Figure 5 are quite similar to those which we observe in Figure 1.  
Figure 2: Distributions of normalized source power \(D\) within the visual cortex plotted superimposed on anatomical MRI in orthogonal cut view (**a**,**c**) and slice mode (**b**,**d**) for subject 2 (**a**,**b**) and subject 6 (**c**,**d**). The blue crosses in (**a**,**c**) define the cutting planes. The arrows in (**b**,**d**) indicate the direction of movement along the slices.
## 4 Dataset Development
### Dataset Generation  
To generate the dataset, 2D images were rendered from the simulated environment using Blender's Cycles ray-tracing engine. For each image, the surface was deformed (detailed in Section 4.4) to ensure variability in the dataset with an associated depth map \(\mathcal{D}\)  
Figure 3: Simulation environment setup for generating the dataset. The environment includes a fixed camera, a fixed pattern, and various surface settings to replicate realistic deflectometry scenarios. An orthogonal sinusoidal fringe pattern is projected onto specular objects, and the reflected fringes are captured by the camera. This setup ensures the generation of a robust and varied dataset, essential for training the VUDNet to accurately reconstruct 3D surfaces from single-shot 2D images. Since this image is a direct screenshot from the Blender environment, the surface is reflecting the simulated world background. The reflection of the pattern plane on the surface is visible to the camera.  
Each 2D image was rendered based on the specific surface reflectance characteristics, camera settings, and surface topology. The optical ray tracing setup in Cycles was configured with a maximum sample count of 4096, ensuring high-quality image generation. Total bounces were set to 12, diffuse bounces to 4, glossy bounces to 4, transmission bounces to 12, and transparent bounces to 8. In this work, the measurement range is defined as the relative depth of the reconstructed surface, which was normalized to the range \([0,1]\) during preprocessing. This normalization ensures consistency and stability across varying surface geometries and enables the network to generalize to different scales. As a result, the proposed method can handle surfaces with different depth ranges without requiring additional re-calibration or re-scaling [52].
## 3 Smart and Secure HIE Systems: SSHES
### Integrating AI and Blockchain into HIE Systems: Practical Challenges and Solutions
#### 3.6.4 Interoperability  
Interoperability is a critical element in the implementation of HIE systems and is defined, according to ISO3 [52], as the communication efficiency across multiple domains without compromising the integrity of the distributed electronic health records. Most of the proposed AI and blockchain systems have pushed towards patient-driven interoperability with the ability to integrate new models and techniques for information sharing in HIE systems. In the literature, we distinguish between two types of interoperability challenges in HIE systems where researchers have significantly investigated each of them and proposed efficient solutions:  
* Semantic interoperability: This represents the tools and models utilized in designing interoperable HIE platforms. The authors of [53] gave an overview of different solutions and standards to overcome semantic interoperability challenge such as HL7, openEHR, ISOs, CCR, CDD, etc.
* Syntactic interoperability: This includes the platform, design, or development of compatible interfaces according to the health entities and their specific guidelines. The authors of [54] provide a taxonomy of syntactical interoperability solutions and frameworks proposed for HIE systems including representational forms (UML, XML, etc.), knowledge object models (DCM, archetypes, etc.), technical message development, etc.
## 4 Case Study--Numerical Experiments
### Inter-Facility Material Transfer Process
#### 4.1.1 Development of the DES Model for Inter-Facility Material Transfer Operations  
The DES model for the inter-facility material transfer operations was developed based on the abovementioned details, as illustrated in Figure 7.  
Anylogic University edition (version 8.8.1) simulation software was employed. The model represents all service stations (nodes) as finite queue capacity service blocks. The decision variables, namely the buffer sizes for each node, were introduced as parameters in the optimisation configuration. The objective function was formulated to calculate the total throughput of the system. The trucks carrying three different types of materials were incorporated into the system through three sources. Each service block is coupled with a single resource pool to ensure the single-server phenomenon. Furthermore, the model accounted for the blocking phenomenon known as blocking after service (BAS), which occurs when a truck will not leave the station even after servicing if there is no space in the next node.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Node \#**} & \multirow{2}{*}{**Process**} & \multicolumn{2}{c}{**Aver.**} & \multirow{2}{*}{**Node \#**} & \multirow{2}{*}{**Process**} & \multicolumn{1}{c}{**Aver.**} \\  & & **(min)** & & & **(min)** \\ \hline
1 & Gate operation & 1.5 & 7 & Weigh bridge 2 & 1.5 \\ \hline
2 & Weigh bridge1 & 1.5 & 8 & Unloading—A & 2.5 \\ \hline
3 & Loading—A & 6 & 9 & Unloading—B & 2 \\ \hline
4 & Loading—B & 4 & 10 & Unloading—C & 3.5 \\ \hline
5 & Loading—C & 7 & 11 & Return trip & 6 \\ \hline
6 & Quality check & 2 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Node details and average service times.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Raw Material (Product Class)**} & \multicolumn{2}{c}{**Full Truck Load (Tons)**} \\ \hline A & 7 \\ \hline B & 3 \\ \hline C & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Full truckload weights.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Scenario \#**} & \multicolumn{2}{c}{**Number of Trucks**} & \multirow{2}{*}{**Total Number**} \\ \cline{2-2} \cline{4-4}  & **A** & **B** & **C** & **of Trucks** \\ \hline Scenario 1 & 3 & 3 & 3 & 9 \\ \hline Scenario 2 & 4 & 4 & 4 & 12 \\ \hline Scenario 3 & 6 & 6 & 6 & 18 \\ \hline Scenario 4 & 4 & 5 & 6 & 15 \\ \hline Scenario 5 & 6 & 4 & 5 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scenarios used for optimisation and performance analysis experiments.