esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
PEALD lowers the substrate temperature due to the high reactivity of the plasma species, while less thermal energy is required at the surface reaction. Various precursors have been studied to deposit SiN\({}_{\mathrm{x}}\) via PEALD, such as silane (SiH\({}_{4}\)) [10], bis(tert-butylamino)silane (BTBAS) [3; 4], di(sec-butylamino)silane (DSBAS) [4], trisilylamine (TSA) [1; 8], trimethylsilane (TMS) [20], and bis(dimethylaminomethylsilyl)-trimethylsilylamine (DTDN2-H2) [9]. Coreactants such as NH\({}_{3}\) or N\({}_{2}\) plasma in combination with these silicon precursors were used. The PEALD of SiN\({}_{\mathrm{x}}\) using aminosilane and NH\({}_{3}\) plasma leads to a reduced growth rate because the H- and NH\({}_{\mathrm{x}}\)-terminated surface is not undercoordinated, which in turn inhibits precursor adsorption. In contrast, N\({}_{2}\) plasma is able to generate reactive undercoordinated bare surface sites; PEALD using aminosilane and N\({}_{2}\) plasma reveals quite sensible growth rates [21; 22; 23; 24]. In this study, SiN\({}_{\mathrm{x}}\) thin films were deposited by RPALD using CSN-2 and N\({}_{2}\) plasma which were chosen as a precursor and a reactant gas, respectively (two-step). The liquid-type CSN-2 (provided in the DNF solution) used in this study has excellent thermal stability, high reactivity, and sufficient vapor pressure (4.04 torr at 60 \({}^{\circ}\)C). The process window and the physical and chemical properties of the film were evaluated using thin film analysis. A better quality RPALD SiN\({}_{\mathrm{x}}\) film was produced and optimized by introduction of an intermediate H\({}_{2}\) plasma step between the CSN-2 and the N\({}_{2}\) plasma step (three-step). We carried out a comparative study between the two- and three-step processes at 350 \({}^{\circ}\)C to evaluate parameters such as step coverage by TEM analysis and wet etching characteristics to determine film suitability for gate spacer processing which requires a low thermal budget. [PERSON] et al. performed research on SiN\({}_{\mathrm{x}}\) thin films using CSN-2 with PEALD [1]. In this paper, as mentioned earlier, RPALD was used to investigate the physical properties of the SiN\({}_{\mathrm{x}}\) films, such as atomic bonding states and step coverage. We used pure H\({}_{2}\) plasma as a reactant, which was not used in previous studies; in turn, the SiN\({}_{\mathrm{x}}\) thin film and the ratio of the H\({}_{2}\) and N\({}_{2}\) gas mixture will be investigated later. Also, further study will be conducted to compare the difference in the physical properties between SiN\({}_{\mathrm{x}}\) deposited by RPALD and PEALD under the influence of H\({}_{2}\) plasma reactant in the ALD process.
# Secure Delivery Method for Preserving Data Integrity of a Video Frame with Sensitive Objects
## 3 Methods  
### System Structure Design  
Figure 4 shows the software architecture associated with the primary functions of the logger and the verifier in the designed system. The logger generates metadata during thread-based concurrent video frame handling. The logger generates a hash value for each video frame, signs it, and stores it on the verifier. As shown in Figure 4, CID is a given video identifier. The hash value of the video frame and its signature are delivered to the verifier from the logger by the thread. The verifier performs hash verification, constructs a hash tree to verify future video changes, and performs hash tree verification. The verifier stores the integrity verification results on the repository. In the integrity verification process of Figure 4, the verified hash value obtained after verifying the video frame is passed to the build hash tree process, which is the hash tree generation process, as input for the construction of the hash tree. The hash tree is then constructed in units of hash tree construction nodes as defined in the configuration. To deliver the verified hash value to the build hash tree process, a message queue, which is an IPC mechanism, is used.  
For data lifecycle management, a logger plays a role as a software component that records events while a digital content producer captures an image. The producer then checks for the presence of a sensitive object within the image. If found, the sensitive object is encoded. The encoded object, along with related metadata, is securely delivered to a verifier. Finally, the original image is recovered through decoding. Further details are provided in the subsequent Section 3.2.  
The designed logger is used in an edge-based environment to extract features from an image for integrity verification. By extracting features at the edge, which is close to the data source, it ensures low latency in acquiring the information required for integrity checks and delivers it to the verifier, enabling real-time processing and facilitating the design of a distributed architecture. The system for verifying the integrity and authenticity of digital video data consists of a logger and a verifier. Listed below are the requirements for the logger and verifier.  
* The verifier receives video data from the logger, which is the module responsible for acquiring video data, stores them, and performs verification of the video data.  
Figure 4: Overall system structure with logger and verifier.  
Communication between the logger and the verifier is based on Representational State Transfer (REST), using a request-response model, and data transmission and processing are carried out by defining message specifications in advance.
* The logger and verifier operate as entities responsible for signing and verification.
## 4 Results  
### Measurement Tool Assessment  
#### 4.1.1 Results of the Reliability and Validity Tests  
Results indicated that all variables exhibited [PERSON]'s alpha values greater than 0.8, confirming the high reliability of the questionnaire. Subsequent EFA with maximum variance rotation in SPSS revealed that the component matrix aligned with the predefined six dimensions, with each dimension's measurement variables showing high factor loadings (greater than 0.7), and only a few items exhibiting slightly lower loadings (between 0.65 and 0.7). As confirmed in Table 1, all variables were identified as independent. Detailed  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Variable** & & **N** & **\%** \\ \hline \multirow{3}{*}{Gender} & Male & 155 & 50.99 \\  & Female & 149 & 49.01 \\ \hline \multirow{4}{*}{Age} & \textless{}18 & 20 & 6.58 \\  & 18–25 & 126 & 41.45 \\  & 26–35 & 103 & 33.88 \\  & 36–60 & 44 & 14.47 \\  & \textgreater{}60 & 11 & 3.62 \\ \hline \multirow{4}{*}{Education} & Below High School & 35 & 11.51 \\  & High School Diploma or Equivalent & 66 & 21.71 \\ \cline{1-1}  & Associate Degree & 78 & 25.66 \\ \cline{1-1}  & Undergraduate Degree & 115 & 37.83 \\ \cline{1-1}  & Graduate Degree & 10 & 3.29 \\ \hline \multirow{4}{*}{Experience with Mini-map Games} & Absolutely Unfamiliar: Never & 6 & 1.97 \\ \cline{1-1}  & played & 50 & 16.45 \\ \cline{1-1}  & Slightly Unfamiliar: Rarely play & 52 & 17.11 \\ \cline{1-1}  & Moderate: Some experience & 52 & 17.11 \\ \cline{1-1}  & Familiar: Often play & 112 & 36.84 \\ \cline{1-1}  & Highly Familiar: Regularly play & 84 & 27.63 \\ \hline \multirow{4}{*}{Frequency of Using Online Virtual Museums} & Never: Never visited & 4 & 1.32\% \\ \cline{1-1}  & Rarely: A few annual visits & 43 & 14.14\% \\ \cline{1-1}  & Occasionally: Monthly visits & 138 & 45.39\% \\ \cline{1-1}  & Often: Weekly visits & 81 & 26.64\% \\ \cline{1-1}  & Very Often: Multiple visits per week & 38 & 12.50\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The demographic information of participants (\(n=304\)).  
factor loadings and Cronbach's alpha coefficients for the two sections of the questionnaire are provided in Table 1. The KMO measure was 0.928, demonstrating the questionnaire's internal consistency and validity. CR values for all variables exceeded 0.8, indicating sufficient convergent validity [99]. Additionally, as shown in Table 3, factor loadings exceeded 0.703 and all variables' Average Variance Extracted (AVE) values were above the recommended threshold of 0.5, suggesting good convergent validity [99]. Individual item loadings greater than 0.7 indicated that the variance explained by the constructs was greater than the variance due to measurement error [100], thereby supporting the structural measurement's validity.
## 2 Results
### The Intravitreal Injection of an Anti-DNM1L Ab Reduced the RNFL Thickness Loss  
To relate the IOP-induced damage to retinal axons, this study examined the RNFL using OCT. OCT is an imaging technique to record retinal cross-sections in vivo (Figure 11). The analysis of these images is performed using a semi-automatic algorithm, which calculated the thickness of the RNFL calculated in the different regions of the retina, nasal superior (NS), nasal (N), nasal inferior (NI), temporal superior (TS), temporal (T), and temporal inferior (TI) (Figure 12). RNFL thickness (RNFLT) was examined longitudinally at the indicated time points, with average RNFLT calculated across all regions (Figure 12). In the IgG OS group, a steady loss of RNFLT was seen longitudinally. This was also evident in the DNM1L OS group, but the overall loss was significantly less compared with the IgG OS group. RNFLT in the contralateral eyes, IgG OD, and DNM1L OD remained approximately identical for the whole study. At week ten, the RNFLT of the IgG OS group was significantly lower at 85 \(\pm\) 2% compared with the RNFLT of the DNM1L OS group at 97 \(\pm\) 3% (\(p\) < 0.001). These relative values referred to the respective baseline examination. The contralateral eyes had RNFLT of 98 \(\pm\) 2% (IgG OD) and 99 \(\pm\) 1% (DNM1L). The RNFLT difference between the IgG OS and IgG OD groups was also statistically significant (\(p\) < 0.001).  
Figure 12: Cont.  
The regional RNFLT was analyzed for w10. In the temporal superior region (TS, Figure 3C), the RNFLT of the IgG OS group was 81 \(\pm\) 7%, significantly lower than the RNFLT of the DNM1L OS group (94 \(\pm\) 8%, \(p\) < 0.01) and the IgG OD group (102 \(\pm\) 5%, \(p\) < 0.001). In the temporal region (T, Figure 3D), the RNFLT of the IgG OS group had an RNFLT of 82 \(\pm\) 11%, and the DNM1L OS group had a significantly higher RNFLT of 96 \(\pm\) 8% (\(p\) < 0.01). Additionally, a significant different was observed in the RNFLT of the IgG OS group compared with the contralateral eyes (IgG OD, 99 \(\pm\) 5%, \(p\) < 0.01). In the temporal inferior region (TI, Figure 3E), the RNFLT of the IgG OS group was 80 \(\pm\) 14%, whereas the RNFLT in the DNM1L OS group was 102 \(\pm\) 11%, a significant increase (\(p\) < 0.01). The RNFLT of the contralateral IgG OD group (97 \(\pm\) 7%) was also significantly higher than the IgG OS group.  
No significant differences were observed in the temporal sectors NS, N, and NI (Figure 3F-H). Therefore, analysis of OCT data showed that application of anti-DNM1L Ab both longitudinally and cross-sectionally markedly attenuated a reduction in the RNFLT. Furthermore, these effects also appeared regionally, as, in the temporal regions, the RNFLT of the DNM1L OS group was significantly higher when compared to the IgG OS group.  
The differences between the IgG and DNM1L OS groups within the temporal regions were similar. The difference was approximately 15% in the regions TS and TI. The difference in the TI region was about 20%.
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 4 Solution Architecture
### Parameterization for Environments  
Consistent deployments across multiple environments (Dev, QA and Prod) are one of the most important goals of infrastructure as code. Each environment typically has unique configuration needs, such as different IP address ranges, instance sizes, and credentials.  
This project uses Bicep's parameter files feature to ensure maximum reusability of the core template logic while customizing values per environment. The main Bicep modules do not hard-code values for names, locations, SKUs, or credentials. Instead, they define parameters, making the infrastructure definition highly reusable, as shown in Figure 3. Typical differences between development, QA, and production environments include network segregation to ensure no overlapping networks and subnets, as well as SKU sizes, i.e., lower-cost and smaller SKUs in development and production-grade SKUs in production. Additionally, resource-naming conventions are used to avoid collisions and ensure traceability [8; 19].  
Figure 3: Parameter files for all environments.  
Parameterization represents the heart of infrastructure as code maturity. It enables teams to achieve environment parity, reduces manual errors, and supports an authentic DevOps culture where infrastructure deployments are repeatable, auditable, and secure.  
By adopting a parameter-driven approach in Bicep, this project showcases a production-ready design that can adapt to organizational scaling needs while maintaining high security and consistency [5; 20].
## 2 Materials and Methods
### Verification of the Predicted Results Using Laboratory Tests
#### 2.5.4 The Protein Denaturation Level  
Protein denaturation levels were assessed through DSC (DSC 1 Mettler Toledo; Schwerzenbach, Switzerland) [24]. Before the measurement, the instrument was calibrated using pure standards (zinc and indium). Both untreated and heat-treated samples were analyzed. A BUCHI B-400 homogenizer was used to uniformly mix each sample. Subsequently, 10.0 \(\pm\) 0.1 mg of goose samples were placed into a standard 40 \(\mu\)L aluminum pan (No.: ME-51119870) and tightly closed with an aluminum lid (No.: ME-51119871) using a Mettler Toledo Crucible Sealing Press.  
DSC analysis was performed at a heating rate of 10 \({}^{\circ}\)C/min (\(\beta\)) under an argon atmosphere (100 cm\({}^{3}\)/min) within the temperature range of 10 \({}^{\circ}\)C to 100 \({}^{\circ}\)C. The resultant curves were scrutinized using STARe software to identify the initial (T\({}_{\text{on}}\)), maximum (T\({}_{\text{max}}\)), and final (T\({}_{\text{end}}\)) temperatures, along with the enthalpy (\(\Delta\)H). The level of denaturation for myosin, collagen, and actin was ascertained based on the curves, according to the methodology outlined by [PERSON] et al. (2019) [42]. The denaturation level for each protein was calculated by comparing the enthalpy of denaturation between the untreated and heat-treated samples using Equation (7). The DSC analyses were conducted thrice for each sample.  
\[Denaturation\ \%=\frac{(H_{\textit{raw}}-H_{\textit{roasted}})}{H_{\textit{raw}}} \times 100 \tag{7}\]  
where:  
\(H_{\textit{raw}}\)--the proteins denaturation enthalpy of raw goose sample before the heat treatment (J/g)  
\(H_{\textit{roasted}}\)--the proteins denaturation enthalpy of roasted goose sample following heat treatment (J/g)  
The evaluation of the denaturation degree was conducted thrice for each sample to ensure the precision and dependability of the outcomes (three technical replicates). Measurements were carried out across three independent biological replicates (three separate batches) using distinct raw materials for each.  
During the assessment of protein denaturation using DSC, potential sources of error primarily include sample preparation variability, instrument calibration inaccuracies, and baseline drift. These were minimized by employing standardized protocols for sample handling, regular calibration checks, and meticulous data analysis techniques to correct baseline fluctuations, ensuring precise measurement of protein unfolding transitions.
# Overcoming Adverse Conditions in Rescue Scenarios: A Deep Learning and Image Processing Approach
## 3 Results  
In this section, we report on the results of the pipeline and show that it meets the requirements for rescue scenarios. To do so, we evaluate its performance in terms of details recovery, object detection improvement, and efficiency.  
As stated in Section 1, we measure the effectiveness of our method on the data set collected at the Weeze earthquake pilot. Our dataset comprises 282 images that depict earthquake scenarios, taken both indoors and outdoors. The images are available in both RAW and JPG formats and have a resolution of 3840 \(\times\) 5750 pixels. They were captured in a variety of exposure settings, mostly featuring buildings and humans in the scenes.
# A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams
## 5 Preliminary Case Study  
In this section, we present a possible case study of the proposed architecture, demonstrating how it can be used in preventing dropouts.  
Teachers and educational managers are going to face both a new group of university students, as well as already enrolled students. The students have different educational needs and learning paths, and teachers would like to minimize dropouts and improve at the same time the usage of the VLE by the students. It is proven that the decision of students to persist or drop out of their studies is related, and quite strongly, to their degree of academic and social integration at university; as a consequence, the big data stream and fog-based architecture we envisioned can be opportunely used to monitor this situation and consequently foster proper retention strategies.  
In this specific case study, we refer to eCampus University ([[http://www.uniecampus.it](http://www.uniecampus.it)]([http://www.uniecampus.it](http://www.uniecampus.it))), one of the Italian distance learning universities, and in particular to the first period of the 2017-2018 academic year: from August 2017-December 2017. The students considered in the study belong to the Faculties of Engineering, Psychology and Literature, and they encompass 100 new enrolled students (40 for courses belonging to Psychology, 40 for Engineering and 20 for Literature), as well as 100 students already enrolled in past years (with the same figures per faculty). Among these 200 students, 100 of them were asked to employ the VLE in a traditional way (Group A), while the other ones (Group B) were asked to employ the new envisioned architecture in its preliminary implementation, which is described in the following.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 4 Challenges and Open Research Issues
### Dynamic Traffic Load  
The traffic load in a cellular network varies across areas and hours of the day. When resources are not allocated based on the dynamicity of the traffic load, a few base stations are overloaded and others underused. This results in inefficient resource management. Therefore, it is essential to design a resource management mechanism considering this issue. Most of the works that have been performed on traffic-aware resource management provided solutions based on the static traffic load rather than the dynamic traffic environment.  
A feasible solution could be the design of algorithms for traffic prediction such that resources can be allocated accordingly. Deep learning techniques can be applied in that case. The authors in [67; 68; 69] employed DL techniques for mobile traffic prediction. Here, they achieved significantly higher accuracy than conventional approaches by extracting spatio-temporal features.
## 3 Methodology
### Motivations and Deployment Considerations
#### 3.2.4 Cluster Management  
Effective cluster management is crucial for efficient resource allocation and communication in VANETs. Clustering enables forming groups or clusters of nearby vehicles, facilitating localized communication and reducing the overall network load. Proper management of clusters ensures optimized resource allocation, improves scalability, and enhances the overall performance of the vehicular communication system.  
In summary, the motivations for deploying a third-party CA for traffic monitoring, implementing a separate authentication communication scheme, considering the influence of capacity and speed on network signaling overhead, and ensuring effective cluster management are essential considerations in the design and deployment of VANETs. These factors contribute to vehicular communications' security, efficiency, and reliability in urban environments.
## 1 Introduction
### Distributed Ledger Technologies
Its goal is to address the scalability concerns that have plagued previous distributed ledger technologies. Moreover, we have leveraged the \"Masked Authenticated Messaging extension module of the IOTA protocol\" in the proposed approach for the secure transmission, storage, and retrieval of encrypted environmental sensor data. The proposed approach is compared with another method without any data encryption protocol and the performance is measured in terms of time taken in the creation, attachment, and retrieval of payloads. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Column** & **Description** & **Units** \\ \hline Ts & Timestamp of event & Epoch \\ Device & Unique device name & String \\ CO & Carbon Monoxide & ppm (\%) \\ Humidity & Humidity & Percentage \\ Light & Light detected? & Boolean \\ LPG & Liquid Petroleum Gas & ppm (\%) \\ Motion & Motion detected? & Boolean \\ Smoke & Smoke & ppm (\%) \\ Temp & Temperature & Fahrenheit \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data description. Figure 1: Sample JSON payload. In summary, the contribution of this paper is as follows:  
1. Design of and work using an environmental monitoring application that uses a DAG-based blockchain called IOTA to ensure the security and integrity of environmental IoT data. 2. Propose an architecture that uses IOTA nodes to implement an environmental monitoring application. 3. Implementation of a working model with its architectural design and an extensive evaluation of the model's performance with experiments. 4. Performance evaluation and comparison of the MAM protocol against a non-protocol approach with clear results. The remainder of the paper is structured as follows. Section 2 illustrates the background technologies for the proposed framework. Section 3 contains the related literature review for choosing this technology and the proposed work. Section 4 highlights the detailed framework of the proposed model. Section 5 showcases the experimental setup, results, and discussion. Finally, Section 6 focuses on a conclusion and future directions in terms of optimizing the MAM protocol for securing sensory data.