esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The influence of Al, Ni, and Si substitutions on the intrinsic magnetic properties of Ce\({}_{2}\)Fe\({}_{14}\)B was studied. We observed that increasing the concentration of Al, Ni, or Si substituting for Fe in Ce\({}_{2}\)Fe\({}_{14}\)B reduces \(M_{\rm S}\) and \(H_{\rm A}\). Ni or Si substitution increases \(T_{\rm C}\), while Al reduces it. For Ce\({}_{2}\)Fe\({}_{14}\)B containing Ni, a maximum \(T_{\rm C}\) of 210 \({}^{\circ}\)C at \(x\) = 1.45 (8.71 atom % Ni) was observed, while a higher value of 252 \({}^{\circ}\)C was found for Ce\({}_{2}\)Fe\({}_{14}\)B containing \(x\) = 2.26 (13.60 atom % Si). The lowest value of 46 \({}^{\circ}\)C was found for Ce\({}_{2}\)Fe\({}_{14}\)B containing \(x\) = 2.04 (12.22 atom %) Al. The maximum \(T_{\rm C}\) of 252 \({}^{\circ}\)C observed at \(x\) = 2.26 Si (14 atom %) in the Ce\({}_{2}\)(Fe\({}_{14-x}\)Si\({}_{x}\))B solid solution makes it more promising as a lower cost PM material than the undoped Ce\({}_{2}\)Fe\({}_{14}\)B compound (\(T_{\rm C}\) = 152 \({}^{\circ}\)C). However, in reality, this might not be the case since the \(M_{\rm S}\) and \(H_{\rm A}\) both decrease with increasing Si content. Therefore, an intermediate composition, with a compromised \(T_{\rm C}\) but with better \(M_{\rm S}\) and \(H_{\rm A}\) might be the most promising Ce-Fe-B magnets containing Ni or Si. Al doping is not recommended because it diminishes all of the studied intrinsic magnetic properties.  
The authors gratefully acknowledge financial support from NSERC and General Motors. The authors wish to thank [PERSON] and [PERSON] for their assistance in carrying out this research.  
[PERSON] and [PERSON] conceived the project; [PERSON] and [PERSON] designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON], and [PERSON] analyzed the data; [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# A Dynamic Framework for Community-Facility Siting with Inter-Community Competition
## 2 Literature Review
### Contributions  
The main contributions of this model can be summarized as follows:  
1. This study is the first to introduce the issue of inter-community-facility competition in the community-facility location problem, and it simulates this issue through the implementation of a spatial allocation strategy constraint termed facility-community allocation.
2. By incorporating a social equity objective into the framework and defining the methods and processes to compute this objective, this study aims to maximize the equity of community facilities' distribution among different demographic groups.
3. This study establishes a dual dynamic model in both time and space dimensions (see Section 3.1) to address the incremental demand for community facilities in successive periods of facility allocation, as well as the need to adopt different facility-community allocation strategies as the number of facilities increases.
## 1 Introduction  
The current Highly Pathogenic Avian Influenza (HPAI) epizootic in the United States (US) began in 2022, with subsequent waves occurring in 2023, 2024, and continuing into 2025 [1; 2]. Wild waterforwl are the natural reservoirs and vectors for HPAI, with the current consensus being that they play a significant role in seeding and maintaining the current epizootic [3]. They infect each other by engaging in instintual behaviors (preening, dabbling) that result in them shedding the virus by opening their bills and excreting saliva and nasal secretions [4]. They become infected by ingesting these infected organic fluids, but they can also ingest infectious particles from fecal matter or feathers left behind from infected birds [5; 6]. Most wild waterforwl are still mobile when infected with HPAI and shed infectious organic material while searching for resources on or close to poultry farms [1]. Domestic birds become infected by ingesting infectious material while they forage in their areas on poultry farms. H5N1 was the dominant HPAI strain during the reference period.  
The high prevalence of H5N1 among wild birds is believed to have increased exposure and infection among domestic birds and poultry farm workers in the US. As of the writing of this article, there have been 70 confirmed human H5N1 infections in the US from 2022 to 2024. These cases are directly related to the current US epizootic because there had never been confirmed human H5N1 cases in the US until 2022 [7; 8].  
Iowa's poultry industry has lost the most domestic birds (30 million) to H5N1 since the epizootic began in February 2022 [2]. Response strategies should use both virologic surveillance and the evaluation of surveillance of macro-level risk factors, such as the abundance of possible vector species. _Branta canadensis_ (Canada goose) is an abundant, resilient species of waterforl found throughout North America and specifically in Iowa [9]. The species has been understudied in the US since the beginning of the epizootic. Our study (Figure 1) is positing that this species is an efficient vector of H5N1, and a higher abundance of Canada geese in Iowa counties increased the risk of experiencing an H5N1 outbreak during the reference period (February 2022-December 2023). _Aix sponsa_ (wood ducks), _Anas crecca_ (green-winged teals), _Anas platyrhnclos_ (mallard ducks), _Spatula discors_ (blue-winged teals), and _Anser caerulescnes_ (snow geese) were the other abundant species of waterforl in Iowa during the reference period, and we included them in the study to evaluate whether their abundance was significantly associated with H5N1 outbreaks [1].  
Figure 1: Study diagram.
# DMRVR: Dynamic Milk-Run Vehicle Routing Solution Using Fog-Based Vehicular Ad Hoc Networks
###### Abstract
The messages are directly forwarded without the need to know the network topology [21; 22; 23]. Fog computing paradigm can meet several requirements for the proper functioning of VANETs, by shifting the cloud computing servers to the edge of the network. Fog computing implements an intermediary layer that provides support for geographic data distribution and low latency in communication and processing [24], and offers computing, storage, and communication servicesbetween end nodes and the cloud [25; 26]. These characteristics drove the trend towards integration of fog computing with VANETs to develop new ITSs. This paper focuses on this trend to investigate the vehicle routing problem in milk-run operations. This paper presents a new approach to solve the dynamic vehicle routing problem using VANETs and fog computing. Our proposed solution, called Dynamic Milk-Run Vehicle Routing Solution (DMRVR), is based on an integrated V2V and V2I vehicular network architecture and uses a dynamic routing algorithm to build pickup routes. Supplier delay messages are sent in the vehicular network, so the dynamic routing algorithm can make the changes along the pickup routes when necessary. When a pickup vehicle perceives a route delay due to congested traffic flow, it might change the pickup route to satisfy the agreement. This paper's major contribution is a solution where every decision is made by using the supports of a VANET architecture that includes vehicular fogs, without using Internet services. This paper is an extended version of our previous work [27]. This work adds a new mechanism to provide reliable message exchanges in case of communication failure. Additionally, the solution evaluation was improved, considering new experiment scenarios and metrics. We also compared our simulation findings with the solution proposed in [10]. This paper is organized as follows. Section 2 summarizes and compares a set of related works with the proposed solution. Section 3 gives a detailed description of DMRVR. An assessment of the DMRVR by simulation is described in Section 4, through different performance metrics for the dynamic vehicle routing efficiency. It also analyzes the performance of vehicular network with different vehicle density. Finally, Section 5 concludes the paper and discusses future works.
## 3 Modification of the _RMR_ and \(Q\) Systems Since Their Establishment
### Development in Terms of Support
#### 3.2.2 \(Q\) System  
To classify a rock mass for its quality, \(Q\) system was updated by continuous re-analyzing of the tunnel case record until a reliable connection was achieved between \(Q\) value, the equivalent dimension (\(D_{e}\)) and the actual support used for the stability of the tunnel [9]. These three variables are organized by means of a support chart in the \(Q\) system of rock-mass classification. The \(D_{e}\) is a function of the excavation size and its purpose and this purpose of the excavation was expressed in terms of the excavation-support ratio (\(ESR\)). The conclusion of the support recommendation chart was 38 box categories of permanent support. This support was for the crown of the tunnel. For the tunnel wall support, \(Q\) was increased up to 5 times and then the support was recommended. The recommended supports were bolting (spot or systematic), grouting, shotcrete (plain or mesh-reinforced) and cast-concrete arches (plain or steel-reinforced). In the 1970s, the wet process of shotcrete along with fiber reinforcement was a revolution in tunnel support. The innovation in shotcrete technology was summarized by [PERSON] and [PERSON] [79]. In 1993, mesh-reinforced shotcrete was replaced by fiber-reinforced shotcrete [68]. The 38 box categories are simplified to the graphical method (9 reinforcement categories) based on 1050 more tunnel case records. Rock-bolt spacing was correlated to the rock-mass quality value in both shotcrete and non-shotcrete area. Out of the 9 reinforced categories, reinforced ribs of shotcrete were also added for one category. Based on numerical modelling [80,81] and empiricism, the \(Q\)-system support chart was again updated by [PERSON] for reinforced ribs of shotcrete [82]. The thickness of shotcrete was increased due to the widespread application of shotcrete in tunneling. The diameter of the rock bolt was specified as 20-25 mm in diameter. In the latest support chart [25], the bolts spacing is quantified for a 20-mm diameter and detailed adjustments are included for the tunnel wall support. It is also mentioned that the shotcrete thickness for excavation with \(D_{e}\) less than 3 m is just supposed and is not empirically based. As in the \(RMR\) system, bolt spacing is a function of \(Q\) value and thickness of shotcrete is function of the \(D_{e}\) along with the \(Q\) value.
# NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies
## 2 Materials and Methods
### Data  
Two types of NPP-VIIRS data were utilized for this study: the DNB Sensor Data Record (SDR) and the Cloud Cover Layer (CCL) Environmental Data Record (EDR). The DNB SDR provides radiance and the corresponding geolocation data (including longitude, latitude, height, and lunar zenith angle). The DNB radiance was used to quantify light emission from artificial light. The geolocation data were used for lunar radiance removal and edge-of-swath pixel discarding in the data preprocessing process. CCL data provided information on cloud cover fraction and were used to filter out the DNB contaminated by cloud. These NPP-VIIRS data were retrieved from the National Oceanic and Atmospheric Administration (NOAA)'s Comprehensive Large Array-data Stewardship System (CLASS) [59].  
The Moderate Resolution Imaging Spectroradiometer (MODIS) Terre V005 500 m Surface Reflectance product (MCD09A1) was used as auxiliary data to quantify the reflected lunar radiance observed by the DNB sensor. MODIS data are available through the EARTHDATA website powered by the Earth Observing System Data and Information System (EOSDIS) [60].  
The Damage Proxy Maps (DPMs) and Flood Proxy Maps (FPMs) produced under the NASA Advanced Rapid Imaging and Analysis (ARIA) Project [61] were used for validation and for comparison with the affected area detected by NTL. The DPM was generated using an algorithm that detects damage by comparing deformation of land structures using pre- and post-disaster X-band interferometric synthetic aperture radar (SAR) data from the Italian Space Agency's COSMO-SkyMed (CSK) satellite constellation. It has a resolution of about 30 m. The FPM was derived from ALOS-2 ScanSAR data and has a resolution of about 50 m.  
We used building range maps from Open Street Map (OSM) [62] to extract building footprints. Land Cover Maps for each event site, at a 30-m resolution for 2010, acquired from the NationalGeomatics Center of China [63], were also used to represent surface coverage type. Administrative boundary data were obtained from the Global Administrative Areas (GADM) database [64].
# Effects of Temperature on the FT NIR Raman Spectra of Fish Skin Collagen
## 4 Conclusions  
The analysis of the recorded Raman spectra of fish skin collagen at room temperature revealed a relatively high content of \(\alpha\)-helix structure in a film made of collagen gel. The high intensity of the bands of amide I and amide III at \(\sim\)1666 cm\({}^{-1}\) and \(\sim\)1274 cm\({}^{-1}\), respectively, suggested a significant content of \(\alpha\)-helix structure in the studied material. The intensities of the above bands were much higher than the intensities of the corresponding bands for the BAT collagen.  
The effect of temperature on the Raman spectra of FS collagen was insignificant up to the temperature 358 K. Above this temperature, the content of \(\alpha\)-helix structure was reduced and beneficial to the \(\beta\)-sheet structure and other disordered structures. From this temperature, the Raman spectrum of FS collagen resembled the Raman spectrum of BAT collagen. Therefore, one could conclude that FS collagen is thermally stable up to \(\sim\)358 K and maintained its native structure. Opposite to fish skin collagen, studies on temperature effects on Raman spectra of BAT collagen did not reveal any change in the secondary structure up to temperature 403 K.  
These results coincide with the results of studying the thermal stability of FS collagen foil by means of the DSC method presented in [40; 41]. It was shown in these works that the thermal stability temperature of the foil collagen from swim bladders obtained from a variety of fish is 338.9-347.8K [60], whereas for the collagen films from silver carp skin it is 350K or 352K [61; 62]. This indicates an ideal agreement of denaturation temperature of collagen foil determined by means of the DSC method with the temperature at which the changes in the secondary structure of FS collagen revealed by Raman spectroscopy were observed in the present work.  
A high content of \(\alpha\)-helix structure in fish skin collagen suggests its high biocompatibility and a variety of possible applications in regenerative medicine and cosmetology.  
Authors equally contributed to this work. All authors have read and agreed to the published version of the manuscript.  
The experiment was supported by the National Centre for Research and Development within the Framework of Applied Research Programme, PBS3/B7/28/2015.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Amide I** & **Amide III** & **Amide I** & **Amide III** \\  & **I\({}_{\text{(-1660)}}\)/I\({}_{\text{(-1450)}}\)** & **I\({}_{\text{(-1270)}}\)/I\({}_{\text{(-1450)}}\)** & **I\({}_{\text{(-1660)}}\)/I\({}_{\text{(-1450)}}\)** & **I\({}_{\text{(-1270)}}\)/I\({}_{\text{(-1450)}}\)** \\ \hline Temperature & \multicolumn{2}{c}{303 K} & \multicolumn{2}{c}{403 K} \\ \hline FS & 1.56 & 1.16 & 0.65 & 0.49 \\ \hline BAT & 0.62 & 0.39 & 0.63 & 0.36 \\ \hline \end{tabular}
\end{table}
Table 2: Raman intensity ratios of amide I and amide III bands to Raman intensity of CH\({}_{2}\) band for FS and BAT collagens.
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 2 Methodology and Foundations
### Resource Management in Network Slicing
Resource allocation quantifies the resources to assign per slice to fulfill, among others, the tenant demands, to meet QoS/QoE, and to maximize the long-term economic benefits of network providers [11]. Resource scheduling programs the time in which the network must allocate resources to each slice to, for instance, minimize the total execution and operation time of the network services, thus guaranteeing improved performance [12]. Resource orchestration mainly manages the service chains, their life cycle management, and the dynamic adjusting of assigned resources, taking into account, for example, performance requirements and network status [106; 107]. The phases mentioned above are detailed in Sections 3-6.
# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 2 Background to Interference Monitoring
### Localization  
Once an interference signal is detected, a permanent solution to stop it is to find and remove it. Therefore, locating interference sources is necessary, and many solutions exist [15; 43; 49; 69; 70; 71; 72]. A popular approach is to collaborate between receivers. The received signal strength (RSS) of the interference signal or the reduction in CN0 (i.e., see the SSC discussion above) are simple measurements used for localization. More advanced systems could use the angle of arrival (AOA) if multiple antennas or a directed receiver antenna are available. Instead, Time difference of arrival (TDOA) is popular, but requires correlation between interference signals, which results in significant ambiguities for periodic signals that limit the applicability for many families of interference signals, such as chirp waveforms. Lastly, frequency difference of arrival (FDoOA) is possible, but is challenging for many waveforms, especially if they are from low-cost imprecise transmitters. For example, broadband indoor localization systems exploit supervised [73] or unsupervised [74] learning strategies for localization (regression of a global position) of user equipment, even in dynamic and multipath situations. Hence, localization is the next expansion of the monitoring system and preliminary performance is presented in this article (see Section 5.1). Therefore, the design considers requirements for localization, such as inter-receiver communication, good timing, and measurement reporting, but in-depth localization analysis is reserved for future research.
## 1 Introduction
The findings revealed that incorporating RHA and recycled aggregate greatly enhanced the performance of concrete. When the replacement ratio of RHA and recycled coarse aggregate was 10-15% and 100%, respectively, the concrete mix ratio satisfied the engineering application requirements. [PERSON] et al. [23] investigated the impact of RHA and fibers on the mechanical properties and acid attack performance of recycled concrete. The results revealed that RHA could enhance the inferior properties of concrete by enhancing the bond between concrete and fibers, and the improvement effect increased with the rise in the RHA replacement ratio. [PERSON] et al. [24] studied the influence of temperature increases on RHA recycled concrete performance. The results showed that a 20% replacement ratio of RHA and a 100% substitution rate of recycled concrete aggregate resulted in a reduction of the compressive strength of concrete. [PERSON] et al. [25] investigated the influence of the partial replacement of cement by RHA on the engineering performance and environmental effects of recycled concrete. The results indicated that the partial replacement cement with RHA not only improved the performance of recycled concrete but also reduced carbon dioxide emissions during the cement production. Although previous studies have investigated the impact of RHA on various mechanical characteristics as well as the durability of recycled concrete, a mechanism analysis of the impact of RHA on the freeze-thaw resistance performance of recycled concrete has not been reported yet. Therefore, it is of great significance to carry out research on the frost resistance of RHA recycled concrete. In view of this, this research aims to investigate the impact of partially replacing Portland cement with RHA by equal mass on recycled concrete's mechanical characteristics and freeze-thaw resistance. The workability, compressive strength, mass loss and dynamic elastic modulus of recycled concrete were determined. In addition, the paste's hydration products and microscopic morphology were analyzed usingscanning electron microscope (SEM) tests. The deterioration mechanism of the freeze-thaw damage of RHA recycled concrete was revealed. The outcome of this research is expected to promote the application of RHA recycled concrete in severe cold regions.
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 3 Scenarios for 5G Network and Its Requirements
### Low-Latency and High-Reliability Communications  
The scenario is linked to ultra-reliable communication and Tactile Internet applications, such as remote surgery and vehicle to vehicle (i.e., Ambulances) communications for driverless cars. The main requirement for this scenario is high reliability and low latency. For example, in [63; 64], the outage probability that is required is approximately equal to \(10^{-7}\) and end-to-end delay is 1 ms.
## 3 Results and Discussion
### Microbial Diversity Analysis
#### 3.8.3 LDA Analysis of Differential Microorganisms among Hams  
LDA Effective Size (LEfSe) analysis enables not only comparisons between multiple groups but also subgroup comparative analyses within groups, thus realising species (i.e., biomarkers) with significant differences in abundance between groups [10]. Species with significant differences were ranked based on their LDA values, where higher values indicated a greater contribution to intergroup differences and greater importance as biomarkers [102]. Species with significant differences were ranked according to LDA values, and those with LDA > 4 were chosen for mapping. In Figure 5, the key microorganisms responsible for differences found in LEFse between normal and spoiled hams were depicted, and the differential bacteria and fungi were more varied in normal hams than in spoiled hams. In the bacterial spectrum, _Staphylococcaceae_, _Alphaproteobacteria_, _Betaproteobacteria_, _Acinetobacter_ and _Oceanospirillales_ were the differential microorganisms in normal ham, while _Bacillusaceae_ predominated in spoiled ham. In the fungal spectrum, _Basidiumycota_ dominated in normal hams, while _Ascomycota_ dominated in spoiled hams. Both normal hams and spoiled hams exhibited greater bacterial variation compared to fungi. Figure 5 demonstrates 16 species with LDA scores of 4 or higher, including 11 bacteria species and 5 fungi species. Among spoiled hams, 5 species had LDA scores exceeding 4, including 2 bacterial species and 3 fungal species. Normal hams displayed the highest variation in both  
Figure 4: Fungal genus level histograms (**A**), Wayne diagrams (**B**) and fungal species level thermodynamics (**C**) of different muscles in normal and spoiled hams. N–SM: normal ham–semimembranosus; N–BF: normal ham–biceps femoris; S–SM: spoiled ham–semimembranosus; S-BF: spoiled ham–biceps femoris.  
bacterium and fungi. These findings confirm the diversity of bacterial and fungal species found in Xuanwei dry-cured hams and the variability between normal and spoiled hams.  
Figure 5: Linear discriminant analysis effect size (LEfSe) of the bacterial (**A**) and fungal (**B**) communities with an LDA score > 4.0 and \(p\) < 0.05. Cladograms indicate the phylogenetic distribution of microbial lineages among dry-cured hams. Circles represent phylogenetic levels from phylum to species. N: normal ham; S: spoiled ham.