esa-sceva/satcom-chunk-collection huggingface dataset:
# Special Issue \"Fundamentals and Recent Advances in Epitaxial Graphene on SiC\"
## 4 A New Look at Possible Applications of Epitaxial Graphene on SiC  
Although the epitaxial graphene on SiC is nowadays reasoned to be utilized in electronics, quantum metrology, and gas/liquid sensing, the unique properties of this material make it promising for use in other non-conventional fields. [PERSON] et al. [7] claim that epitaxial graphene on SiC could be regarded as an excellent platform for formation of twisted few-layer graphene with a magic twist angle that might be useful to control spin orders, ferromagnetism, and superconductivity. The authors have substantiated this claim by the fact that owing to its natural compatibility with the semiconductor technologies, epitaxial graphene-based device processing requires no intermediate graphene transfer steps and thus is more attractive from a technological point of view in comparison to transferred graphene. In this regard, there is a plenty of room for manipulation of the twist angle and for formation of twisted graphene layers on SiC with the desired angle through adjusting the sublimation growth conditions.
# Challenges and Solution Directions of Microservice Architectures: A Systematic Literature Review
## 5 Identified Challenges and Solution Directions
### RQ1. What Are the Identified Challenges in the MSA Domain?
#### 5.1.8 Monitoring, Tracing, and Logging (MTL)  
Monitoring, tracing, and logging are important activities to ensure that systems are able to satisfy availability, performance, and reliability concerns. However, these important activities consist of several challenging points related to identifying strong coupled services, the root cause of anomalies and performance problems, and the heterogeneity of logs.  
The logs from different microservices might be heterogeneous so understanding and traversing the logs emerges as a challenging point. If the trace cannot be established among the logs, the ability to monitor the system is directly affected. Thus, practitioners will not make proper decisions for troubleshooting.  
It is critical to identify these problems and take quick action as soon as possible. Otherwise, the system's availability, reliability, and fault tolerance will be directly affected.  
Expected behavior for the MTL process is that trouble spots are detected, and the system is made more available, scalable, reliable, and fault-tolerant by taking quick actions or making changes in the design if necessary.
## 4 Performance Evaluation
### Result Evaluation
#### 4.3.8 Energy Consumption
From the collision count graph, it can be observed that Q-learning MAC always has a much lower collision count than fixed MAC, proving the low-collision-rate capability of this protocol. The Q-learning MAC protocol shows lower energy consumption in the energy consumption graph Figure 22, which results from the versatility to vary the transmission energy, unlike the fixed MAC protocol that has incessant higher energy use. Last but not least, through the throughput graph it can be observed that the Q-learning MAC protocol has better success rates of successful transmission and shows more stability in its number of successful transmissions as compared to the DCF MAC protocol, thus demonstrating the ability of the Q-learning MAC protocol to learn and adapt to the network environment in order to achieve optimum solutions for the data transmission. Overall, the Q-learning MAC protocol outperforms the fixed MAC protocol in all three key metrics: A bull's-eye scatter plot of the collision rate in terms of energy efficiency and throughput is also shown. Figure 21: Energy efficiency vs. avg. traffic load. Figure 22: Evaluation of the convergence values for Q-learning algorithm.
## 2 Materials and Methods
### The GHSL Data Layers
The dataset that we built to conduct this study consists of circa 10,000 urban centers, delineated from the GHS-SMOD layer for the epoch 2015, and it is a precursor of the _GHSL Urban Centre Database_ (GHS-UCDB). In 2017, a revised image processing workflow was implemented in the JRC Earth Observation Data and Processing Platform (JEODPP) and then applied to the Landsat multi-temporal imagery collection. As a result, an updated version of the GHSL (GHS-BUILT, GHS-POP, and GHS-SMOD) [51] was released in the community of the GEO Human Planet Initiative and it has been tested in this study. The GHSL framework serves multiple applications and analytics methods. GHSL baseline data, GHS-BUILT, and GHS-POP are especially used as exposure layers in the disaster risk reduction sector in support of emergency management services (i.e., Copernicus EMS) [52], alert systems [53], and risk management decision support indexes (i.e., INFORM) [54]. GHS-POP and GHS-SMOD were applied to estimate the carbon footprints of human settlements [55], travel time to major cities [56], and global patterns of human domination [57] and presence on the planet [18]. Through these applications, GHSL has become an established source supporting studies on the human interaction with and the  
Figure 1: Example of GHSL data, three geospatial layers that map built-up areas (GHS-BUILT) (**a**), three geospatial layers that map resident population (GHS-POP) (**b**), three geospatial layers that map settlement typologies (GHS-SMOD), and (**c**) displayed at 1 km spatial resolution in the area of Tokyo (Japan) and compared to the imagery base map. modification of the environment over time. More importantly, the GHSL baseline data (GHS-BUILT, GHS-POP, and GHS-SMOD layers) can be injected in Sustainable Development Goals metadata to estimate SDG indicators. Below, we explore the use of GHSL to estimate the SDG 11.3.1. Multi-Temporal and Spatial Harmonization of Information  
GHSL layers are continuous global raster data, with information on the spatial footprint of settlements--built-up areas that are processed using Landsat imagery at corresponding epochs and a population that is modeled on the basis of the Gridded Population of the World (GPW). The information on built-up areas is extracted through the open workflow [43], and it is fed with multi-temporal imagery from Landsat at corresponding epochs [41; 58]. Information on population is derived from GPW, which is a fine scale, spatially disaggregated layer that is produced from population and housing censuses from which estimates for past epochs are derived and made to be consistent with, and adjusted to, United Nations World Population Prospects [59]. The availability of consistent and harmonized data across the globe and epochs makes the analysis of long term process of human settlement changes (1975-2015) providing valuable tools for urban development analytics, including a new lens with which to observe global urbanization, possible [16].
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 3 Proposed Framework  
The quantum-resistant image encryption framework that we propose operates on a fundamental principle: security and performance optimization must be considered holistically rather than as competing objectives. Our architecture achieves this through the careful integration of post-quantum cryptography with highly optimized homomorphic encryption, structured around three distinct but interconnected phases.  
The initial step in our framework transforms the input image into a vector suitable for FHE using a standard raster-scan operation, where the two-dimensional pixel matrix is flattened into a one-dimensional vector [20]. This encoding is highly efficient for the point-wise operations evaluated in this work, such as brightness adjustment. Recognizing the future need for more complex spatial operations, the framework is designed for extensibility. As shown in Algorithm 1, the client proactively generates rotation keys (\(evk_{rotation}\)). These keys are the essential cryptographic component for performing 2D convolutions and other advanced filters homomorphically, establishing a clear and practical path for expanding the framework's capabilities beyond simple point-wise processing.  
This paper proposes a multi-layered solution to address these challenges:  
Layer 1: Quantum-Resistant Communication (Using CRYSTALS-Kyber)  
To start, the client and the server establish a secure communication channel using the server-side protocol detailed in Algorithm 2. Instead of using traditional encryption vulnerable to quantum attacks, we employ CRYSTALS-Kyber, a cryptographic algorithm standardized by the U.S. National Institute of Standards and Technology (NIST) specifically to be quantum-resistant.  
Layer 2: Private Image Processing (Using FHE with Optimizations)  
* Vectorization: Instead of encrypting one pixel at a time (which would be very slow), the entire image (or a large block of it) is flattened into a single list of numbers (a vector).
* FHE Encryption (CKKS Scheme): This entire vector is then encrypted into a single FHE ciphertext using the CKKS scheme, which is specially designed to work with real numbers (like pixel values). The client generates its own FHE keys and keeps the secret key private.
* SIMD Processing based on Context Amortization: The server performs image operations on the single encrypted vector. Because of a feature called SIMD (Single Instruction, Multiple Data), the operation is applied to all the pixel values in the vector simultaneously. To perform FHE operations, the server needs the client's public key materials. Instead of sending these for every image, the client sends them once per session, and the server caches them. This amortization dramatically improves the effective throughput for batch processing scenarios.
###### Abstract  
Ion-molecule reactions between neutral methyl formate (MF) and proton-bound solvent clusters W\({}_{2}\)H\({}^{+}\), W\({}_{3}\)H\({}^{+}\), M\({}_{2}\)H\({}^{+}\), E\({}_{2}\)H\({}^{+}\), and E\({}_{3}\)H\({}^{+}\) (W = water, M = methanol, and E = ethanol) showed that the major reaction product is a solvent molecule loss from the initial encounter complex, followed by the formation of protonated methyl formate (MFH\({}^{+}\)). Collision-induced dissociation breakdown curves of the initially formed solvent-MF proton-bound pairs and trimers were obtained as a function of collision energy and modeled to extract relative activation energies for the observed channels. Density functional theory calculations (B3 LYP/6-311+G(d,p)) of the solvent loss reaction were consistent with barrierless reactions in each case. The MF(M)H\({}^{+}\) ion also exhibited loss of CH\({}_{4}\) at higher collision energies. The reaction was calculated to proceed via the migration of the MF methyl group to form a loosely bound complex between neutral CH\({}_{4}\) and an ion comprising (CH\({}_{3}\)OH)(CO\({}_{2}\))H\({}^{+}\). Overall, the results indicate that the interaction of methyl formate with atmospheric water can form stable encounter complexes that will dissociate to form protonated methyl formate.  
ion-molecule reaction; collision-induced dissociation; tandem mass spectrometry; RRKM modeling; atmospheric reactions +
Footnote †: journal: Applied Physics Letters
# Leveraging Language Models for Inpatient Diagnosis Coding
###### Abstract
Our goal was to address two important questions: (1) How accurate can a language model-based algorithm for ICD-10 classification be when clinical data of one institution are utilized? and (2) To what extent can the algorithm be used? We investigated the performance of the algorithm on different types of diagnoses, including common diseases and rare and complex cases. We explored the scope of the algorithm and identified specific areas where it might excel or encounter challenges. This can provide valuable insights into whether automatic or semi-automatic coding of diagnoses is ready for application in hospital information systems (HIS). This study makes notable contributions to inpatient diagnosis coding by investigating the effectiveness of language models on a comprehensive dataset spanning a six-year period with the full ICD-10 code sets. We examined both general-purpose language models and a domain-specific language model. Our results illustrate the competitive performance of the language models and highlight their potential to improve coding accuracy. We analysed the performance of the models on different diagnostic codes, providing insights into the impact of sample size on coding accuracy. We used co-occurrence analysis to ensure that the models were able to capture relevant associations between diagnoses. We also identified factors that influence performance, including disease rarity and document length. This work highlights the need for larger datasets, class imbalance strategies, multi-hospital collaboration and integration of multidimensional data to improve automatic coding systems.
# Peer-to-Peer Ultra-Wideband Localization for Hands-Free Control of a Human-Guided Smart Stroller
## 4 Experiments
### Experiment 1: Path-Following Test  
The setup of Experiment 1 is shown in Figure 6a. The trajectory is sequentially composed of a 4.5 m straight line, a quarter circle (r = 1.5), and a 1.5 m straight line around a corner. Participants were instructed to use the three interfaces to operate the stroller and follow the lines on the floor. We utilized the Procrustes Distance to present the dissimilarity between the experimental robot and ground truth trajectories.  
* Procrustes Distance The Procrustes distance is a measure of dissimilarity between shapes based on Procrustes analysis. The Procrustes function finds the best shape-preserving Euclidean transformation between two shapes. In this work, we compare the two trajectories _Traj_1 and _Traj_2 using the Procrustes analysis, the trajectories would be optimally superimposed, including translating, rotating, and uniformly scaling, to minimize the Procrustes Distance between transformed metrics \(m\)1 and \(m\)2. The Procrustes Distance (PD) is calculated by \[PD(m1,m2)=\sqrt{\sum_{j=0}^{n}\sum_{i=0}^{k}\left(x_{ij}^{m1}-x_{ij}^{m2} \right)^{2}}.\] (6) Here, \(x_{ij}^{m1}\), \(x_{ij}^{m2}\) are the coordinates of the \(i\)-th point in shapes \(m\)1 and \(m\)2, separately; n is the number of points on the trajectory; and \(k\) is the spatial dimensions. The Procrustes distances between the experimental robot and ground truth trajectories are calculated using the Python library [39]. The returned numeric scalar is within [0, 1], with higher values representing less similarity.
# Multi-Layer Problems and Solutions in VANETs: A Review
## 3 Discussion
* Cross Layer Design: As discussed in the last section, the cross layer design is an important research area in VANETs these days. Parameters from different layers are fed back to other layers and decisions are made on the basis of those parameters. Ongoing research focus on choosing these parameters in order to achieve the desired performance while preserving the modularity of the OSI architecture. * Autonomous VANETs: The advances in the autonomous technology regarding the vehicular communication will facilitate in the development of intelligent transport system. The autonomous VANETs will have self-organizing, self-healing and self-optimization capabilities. In [78] the author describes the autonomic broadcast VANETs as networks with self-managed architecture, QOS based broadcasting, and optimization of broadcasting protocols. Since the broadcasting of emergency messages is an important aspect of the vehicular communication. Autonomic dissemination method (ADM) has been presented with a simulation to study the behavior of vehicles under the autonomous system. The concept of autonomous clustering has also been proposed in [79]. The concept is similar to the one mentioned in the cluster based VANETs before. A Swarm Intelligence based scalable, self-organizing, and robust Urban Public Transport System (UPTS) concept has been presented in [80].
## 4 Wavefolding in the Digital Domain
### Aliasing Considerations  
As discussed in Section 1, nonlinear waveshaping in the digital domain is susceptible to aliasing distortion due to its frequency-expanding nature. Wavefolding is no exception to this problem. As an arbitrary input waveform is folded, new harmonic overtones will be added to the frequency spectrum. Harmonics at frequencies exceeding half of the sampling rate, or the Nyquist limit, will be reflected into  
Figure 10: Averaged processing times required to compute \(W(x)\) using [PERSON]’s method and [PERSON]’s iteration.  
the audio baseband as aliases. Aliasing is known to cause unpleasant artifacts--such as beating and inharmonicity--that cannot be tolerated in a music computing scenario. Oversampling is commonly employed to mitigate this issue; however, this approach increases the computational requirements of the system by introducing additional operations.  
We propose the use of the first-order antialiasing method presented in [48; 49]. This method is designed to reduce aliasing caused by memoryless waveshaping functions with the form described by Equation (29). The antialiased output of the waveshaping function is defined as  
\[V_{\text{out}}[n]=\frac{F(V_{\text{in}}[n])-F(V_{\text{in}}[n-1])}{V_{\text{in }}[n]-V_{\text{in}}[n-1]}, \tag{45}\]  
where \(F()\) is the antiderivative of \(f()\), the original transfer function. For the case of the Lockhart wavefolder defined by Equation (28), the integrated transfer function is defined as  
\[F(V_{\text{in}})=\frac{\alpha}{2}V_{\text{in}}^{2}-\frac{\eta V_{\text{T}}}{2 \beta}\left[\Psi_{1}(\Psi_{1}+2)\right], \tag{46}\]  
where  
\[\Psi_{1}=W\left(\Delta\exp\left(\lambda\beta V_{\text{in}}\right)\right), \tag{47}\]  
and \(\alpha\), \(\beta\) and \(\Delta\) remain as before. This result showcases an advantageous property of the Lambert-W function \(W()\); its antiderivative is defined in terms of \(W()\) itself. Therefore, computing \(F()\) does not pose a major increase in computational costs with respect to evaluating simply \(f()\). For the case of the Serge wavefolder defined by Equation (39), the required antiderivative is given by  
\[F(V_{\text{in}})=\frac{V_{\text{in}}^{2}}{2}-(\eta V_{\text{T}})^{2}\left[ \Psi_{2}(\Psi_{2}+2)\right], \tag{48}\]  
where  
\[\Psi_{2}=W\left(\frac{R_{1}I_{\text{s}}}{\eta V_{\text{T}}}\exp\left(\frac{ \lambda V_{\text{in}}}{\eta V_{\text{T}}}\right)\right). \tag{49}\]  
When \(V_{\text{in}}[n]\approx V_{\text{in}}[n-1]\), Equation (45) can become ill-conditioned. This is avoided by defining the special case  
\[V_{\text{out}}[n]=f\left(\frac{V_{\text{in}}[n]+V_{\text{in}}[n-1]}{2}\right), \tag{50}\]  
when \(|V_{\text{in}}[n]-V_{\text{in}}[n-1]|\) is smaller than a predetermined threshold [48]. This special case simply bypasses the antialiased form while compensating for the half-sample latency of the method.
# IG-Based Method for Voiceprint Universal Adversarial Perturbation Generation
## 6 Experimental Analysis  
Five pretrained voiceprint classification models, namely 1 DCNN Rand, 1 DCNN Gamma, ENVnet-V2, Sincnet, and SincNet+VGG19, are employed on the UrbanSound8k dataset [10], applying the IG-UAP method to evaluate their performance in untargeted and targeted attacks. Implementation and experimental comparisons were performed on four existing voiceprint universal adversarial perturbation generation methods, which include FGSM-UAP [11], PGD-UAP [12], C&W-UAP [13], and MSCW-UAP [14]. Table 2 displays the experimental environment. The sample sizes were small, so caution in drawing conclusions is advised.  
FGSM-UAP combines the adversarial perturbation generated by FGSM with UAP, generating a universal perturbation applicable to multiple voiceprint recognition models: PGD-UAP generates smaller perturbations, reducing auditory changes but at a slower generation speed and with possible limitations imposed by the input space; C&W-UAP is suitable for attacking specific models and has the capability to generate adversarial samples for multiple classes; MSCW-UAP has a faster generation speed and lower computational complexity than C&W-UAP, but it is more susceptible to limitations imposed by the input space.  
The UrbanSound8k dataset employs down sampling at 16 kHz to train and evaluate the model while generating generalizations against perturbations. The dataset comprises 7.3 h of recordings subdivided into 8732 three-second long audio segments, representing each audio sample as a 50,999 dimensional array. The audio recordings were divided into ten distinct classifications, specifically, air_conditioner (sound of air conditioning), car_horn  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & **Category** & **Item** \\ \hline \multirow{3}{*}{Hardware} & CPU & Xeon(R) W-2123 CPU 3.60 GHz \\  & Random Access Memory & 16 GB 2133 MHz LPDDR3 \\  & Video Card & NVIDIA GeForce RTX 2080 Ti12 GB \\ \hline \multirow{3}{*}{Software} & Operating System & Windows 10 64-bit System \\  & Programming Languages & Python \\ \cline{1-1}  & Third Party Libraries & Pytorch, torchaudio, flask, bootstrap \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test environment.  
(sound of a vehicle horn), children_playing (sound of children at play), dog_bark (sound of a dog barking), drilling (sound of drilling), engine_idling (sound of a car's engine running), gun_shot, jackhammer, siren, and street_music. The training set encompasses 80% of the dataset, while the remaining 20% is employed as a test set. To create opposing disturbances, the 6984 samples in the training set were randomized with a small batch size of 100 samples for the greedy iteration-based method. The perturbations were assessed for the entire test set, consisting of 1748 samples.
# Hybrid Data Hiding Based on AMBTC Using Enhanced Hamming Code
## 3 The Proposed Scheme  
In this section, we introduce a DH to embed secret data in bitmaps and the quantization levels of AMBTC using optimized the Hamming code and DBS method. First, compressed blocks, _trios_, are classified into smooth blocks and complex blocks. Then, DBS is applied to the bitmaps of the smooth blocks, while the Hamming code may be applied to the quantization levels regardless of the block characteristics. The method proposed by [PERSON] and [PERSON] results in the large distortion of the cover image. In Section 3.1, we introduce a way to solve this problem.
# Secure and Efficient Federated Gradient Boosting Decision Trees
## 5 Discussion
### Communication Costs  
In this section, we discuss the communication cost required for each update of each scheme.  
In step 5-step 11, SeFB needs four communications for each update. First, GS sends datagram (weighted gradients and tree-building mark) to builder B. Secondly, the builder B shares the constructed tree with all participants, then the participants transmit the local gradients to the GS, and finally the GS calculates the global leaf weights and transmits them.  
Therefore, after \(U\) update iterations, the total communication cost of SeFB is \(O(U)\), which is the same as TFL and eFL-Boost. However, in each update of FederBoost, the gradient histogram corresponding to each node must be communicated several times according to the depth of the tree, and the total communication cost is \(O(HU)\). SimFLneeds to obtain instance gradients from other participants every time it is updated, and the total communication cost is \(O(|I_{m}|U)\). Therefore, SeFB can train the model with lower communication cost than FederBoost and simFL. Table 2 shows the communication times required for each scheme to update the global model each time, where \(H\) represents the depth of the tree and the \(I_{m}\) represents local instance set.