esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
When calculating the optimized routes, the time limit, which is estimated through the life span of each vaccine according to the Pfizer conditions of transportation, is the crucial factor. The estimated time leads to two possible transportation scenarios: one that uses overtime, and another that does not. All the presented aspects will be considered in the proposed solution. After testing the two scenarios, a follow-up analysis will be conducted. In this analysis, both the total cost and the total CO\({}_{2}\) emissions of each route will be calculated. This will aid in the development of a set of criteria for selecting the optimized route, considering not only the route's total cost but also which route will be the most efficient. The two initial hypotheses to optimize the distribution of the COVID-19 vaccines and solve the initial questions are as follows:  
* Hypothesis 1.: _The scenario that considers 480 min provides the most optimized solution for the distribution of COVID-19 vaccines for each cluster._
* Hypothesis 2.: _The scenario that considers 585 min provides the most optimized solution for the distribution of COVID-19 vaccines for each cluster._
* Hypothesis 3.: _Both scenarios provide the same optimal solution for the distribution of COVID-19 vaccines for each cluster._
* Hypothesis 4.: _When the CO\({}_{2}\) emissions value is minimized, the total cost of the route is also minimized in each cluster._  
Through a literature review, it was concluded that this study contributes to:Identifying methods and algorithms to explore pandemic containment delivery operations. This case focuses on Central Portugal, however, it can be extended or applied to other locations and conditions;
* Highlighting the social impact of VPRTW through decreasing the vaccine's delivery time by optimizing the routes, due to the urgent need to properly vaccinate the population;
* Studying the economic and sustainability impacts of the COVID-19 vaccine distribution process through further analysis (after route optimization);
* Providing a replicable methodology to optimize the COVID-19 vaccine's distribution route. This method can then be applied to other fields, such as when there is a distribution problem for perishable products;
* Using VRPTW to address a social problem as opposed to a commercial problem for which efficiency is associated with minimizing costs. In this case, it is used to minimize time;
* Ensuring the vaccines are delivered within the established quality parameters;
* Ensuring the vaccines are delivered in secure conditions. However, the main contribution of this case study is the proposed methodology, which associates each center's demand, vehicle capacity, number of vehicles, and vaccine distribution needs. The study also proposes a deep analysis of the possible roads, in order to maximize the safe delivery of the vaccines.
# An Overview of Fog Data Analytics for IoT Applications
## 4 Current State-of-the-Art
### Smart Factories  
IoT has enabled automation and obviated the need for human interaction, adding new capabilities, such as logging every event and making intelligent decisions at certain times. Having such advantages, IoT is most suitable for driving automation at various levels in the industry. For IoT technology used in the industry, there exists another specific term: the industrial Internet of things (IIoT). These IIoT devices include actuators, RFID tags, robots, flow control and conditioning devices, and sensors for various manufacturing, testing, and quality control applications. The cloud with these devices provides centralized control and helps to automate tasks with deploying applications on the cloud, managing IIoT devices communication and data analytics [41]. However, this implementation needs to deliver the expected value for some time-critical systems because of latency issues due to the round-trip distance between IIoT devices and cloud services. Fortunately, the problem can be remedied by enabling fog computing with IIoT to handle real-time data processing and analytics. Fog nodes host monitoring and control services that make it possible to serve low-latency requirements as they are closer to the IIoT devices, along with the implementation of SDNs to develop control plane behavior and employing time-sensitive network (TSN) protocols for optimizing and sustaining the network's effectiveness [42]. With fog nodes with a sufficient processing power, real-time data analytics, such as the prediction of quality and estimation of faults, are generated with a high availability.
## 3 Results  
### Sem/Xrd  
According to SEM imaging, the morphology of the ZnO powder consists mainly of isolated rods that may also appear branched together (Figure 1a,b). No amorphous material was observed in all images. Moreover, the ZnO rod diameter ranged between 70 nm and 1 \(\upmu\)m.  
SEM imaging at 1000\(\times\) magnification (Figure 2a-d) revealed the same situation as above. Additionally, C0 sample decomposition was indicated by its porous surfaces. The same, but to a lesser extent, applied to C0.1 samples, whereas C0.5 and C2 samples were free from pores. Furthermore, spot imaging on the white areas of Figure 2d ascertained the presence of ZnO.  
Additionally, the SEM energy dispersive spectroscopy of C2 samples at INT2880 (Figure 3), and of C0.5 samples (Figure S1 Addendum), revealed satisfactory ZnO dispersion, portrayed by the group of blue dots (Zn). However, Zn presence was not detected on samples of C0.1 filler concentration, but the oxygen variation was found for untreated C0 samples 17.23 at% and for AWE-treated samples 25.81 at% (C0), 25.33 at% (C0.1), 27.65 at% (C0.5) and 31.76 at% (C2), due to increasing ZnO content and the oxidation of the material.  
Figure 1: (**a,b**) SEM images of ZnO powder at various magnifications.  
Figure 2: _Cont._  
Figure 3: Energy dispersive spectroscopy on the scanning electron microscope for C2 samples. Green, orange and blue embedded images indicate the presence of carbon (C), oxygen (O) and zinc (Zn), respectively.  
Figure 2: (**a–d**) SEM images of INT2880 sample surfaces at ZnO loadings (**a**) C0, (**b**) C0.1, (**c**) C0.5, (**d**) C2. The white arrow indicates a zoomed-in pore area and black arrows point to cracks of the corresponding surfaces.  
Figure 4a shows the XRD pattern of the as-produced ZnO powder. The XRD diffraction peaks were characterized according to the literature [29; 34; 35], and they were assigned to a ZnO hexagonal wurtzite structure with lattice constants a = 3.249 A (a = b) and c = 5.207 A. The faces (100), (101) and (001) of the hexagonal crystallographic system corresponded to m (side structure), +p (regular hexagonal pyramid structure) and +c (regular hexagonal prism structure), respectively [34; 36]. Based on Figure 4a, the ZnO powder consisted of hexagonal rods with mainly hexagonal pyramidal structure. Additionally, there were not any other crystalline intermediate structures; thus, the powder was pure ZnO. The average crystalline size of the ZnO rods (average of L\({}_{\text{c}}\), L\({}_{\text{a}}\)) was evaluated by Williamson-Hall analysis [37] using the peak width at the half maximum of the crystallographic faces (002) (stacking), (100) and (101). Based on the aforementioned information, the synthesized ZnO powder consisted of rods, with an average crystal size D = 46 nm (L\({}_{\text{a}}\) = 49 nm, L\({}_{\text{c}}\) = 43 nm).  
In the same manner, XRD measurements of the samples revealed that ZnO diffraction peaks were clearly detectable on the C2 samples and barely detectable on the C0.5 samples, while on the C0.1 samples only the 101 peak could be attributed to ZnO presence (Figure 4b).
## 2 Evolution, Virulence and Pathogenicity of _E. coli_ O157:H7  
Non-toxic and less virulent _E. coli_ O55:H7 strains are known to be ancestral cells of _E. coli_ O157:H7 [36; 37] that originated through a series of transitional phases [38]. According to the concept model, the locus of enterocyte effacement (LEE) was present in both the O55:H7 and O157:H7 strains, and it is capable of inducing diarrhoea through an attachment-effacement process [39; 40; 41]. Four sequential events that led to the emergence of _E. coli_ O157:H7 include (i) acquisition of an _stx_ operon, which is encoded in the genome of lambdoidophages, (ii) acquisition of the _rfb_ region encoding the O157 antigen, and (iii) loss of \(\beta\)-glucuronidase (GUD) activity [42; 43]. Notably, the three main virulence factors of _E. coli_ O157:H7 include (i) Shiga toxin operons, (ii) locus of enterocyte effacement, and (iii) byproducts of the F-like plasmid pO157 [44; 45].  
Based on the degree of its virulence, the Shiga toxin operon is divided into two major groups, namely, _stx\({}_{1}\)_ and _stx\({}_{2}\)_ genes [46]. Interesting, the Stx1 has three subtypes (Stx1a, Stx1c, and Stx1d), while Stx2 has seven subtypes (Stx2a, Stx2b, Stx2c, Stx2d, Stx2e, Stx2f, and Stx2g). It is widely acknowledged that diverse stx subtypes possess the ability to bind to multiple receptors, exhibiting varying affinities for each receptor. The primary target for Stx2a is the Gb3 receptor, whereas Stx1a can bind to both Gb3 and globotetraosylceramide (Gb4). The Stx2e subtype exhibits a broader spectrum of receptor binding capabilities, including interactions with globopentaosylceramide, pentahexosylceramides featuring Gb4-elongated core structures, and Gb4 with a preference for Gb5 [47]. Several other genes, apart from _stx_, have been associated with the pathogenicity and virulence of O157:H7 [48]. These genes differentiate _E. coli_ O157:H7 from non-pathogenic strains of _E. coli_ and are presented in Table 1.
## 1 Introduction
For this purpose, algorithms are used that limit signal sampling depending on the nature of the input data [27; 28; 29]. However, limiting the amount of data transferred may lead to the loss of some data [30]. Signal sampling is also controlled through the use of dedicated electronic systems that allow for the detection of a specific nature of monitored signals [31]. Effective management of radio data transmission is important in the sensor systems for motion acquisition, which allows to reduce the energy demand of electronic systems and reduce network traffic [32; 33]. One of the methods of radio transmission optimization in the case of generating a large amount of data by sensors is, for example, the selection of a minimum data set necessary to describe the monitored process [34]. The article presents a new transmission protocol for the sensor network in the form of an algorithm designed both to increase the energy efficiency of the radio link and to reduce the load on the Wi-Fi sensor network. This approach to radio data transmission management seems to be universal, as the mentioned aspects are important for the practical implementation of the developed algorithm. The proposed method allows for transmission control only on the basis of data from sensors. In addition, the transmission process is controlled without the need for two-way communication between the sensor system and another device supervising the transmission or routing process (e.g., PC server). The developed transmission method does not require the transmission of additional control frames, which reduces the network load. Furthermore, the transmission control algorithm does not modify the 802.11 MAC layer, nor does it introduce additional routing protocols, which simplifies its implementation, in particular in sensor networks with limited hardware resources. Dedicated electronic systems are also not required to control the transmission. Therefore, the proposed algorithm can be used in sensor network nodes with a reduced architecture [35; 36; 37]. Moreover, a constant sampling frequency is maintained for all MEMS sensors. The transmitted data do not require additional processing on the part of the receiving device. The article is divided into three main parts. Sections 2 and 3 describe the measuring stations used in the research cycle as well as the hardware and logical architecture of the developed wearable system with a dedicated transmission control algorithm. Section 4 concerns testing the presented hardware solution and the algorithm in terms of energy efficiency and load of the sensor network as well as the quality of traffic mapping. Section 5 is related to the analysis and discussion of the obtained results.
###### Abstract  
Near-infrared spectroscopy (NIRS) is an efficient, non-destructive method for evaluating the chemical composition of various compounds. This study aimed to assess both the proximate composition, fibres, and fatty acid (FA) content of Total Mixed Ratios (TMRs) in dairy buffalo nutrition. A total of 240 TMR samples were collected from ten dairy buffalo farms across four seasons to develop predictive models using Partial Least Squares Regression (PLSR). Calibration models for dry matter (DM), crude protein (CP), either extract (EE), and starch demonstrated good predictive accuracy, with coefficients of determination in cross-validation (R\({}^{2}\)cv) around 0.90 and Residual Predictive Deviation (RPDcv) values exceeding 3.0. Fatty acid models showed slightly lower R\({}^{2}\)cv values, ranging from 0.80 to 0.90. A good predictive performance was observed for linoleic acid (18:2 n-6) and \(\alpha\)-linolenic acid (18:3 n-3), with RPDp values above 3.0, indicating reliable predictions. The inclusion of omega-3-rich compounds in the diet provides significant benefits for both animal health and product quality, highlighting the importance of ration monitoring. The findings confirm that while NIRS is effective for assessing chemical composition, further refinement is needed to improve FA prediction accuracy. These results support the use of NIRS as a practical tool for nutritional monitoring in lactating buffalo.  
near-infrared spectroscopy; fatty acids; total mixed ration; chemometric evaluation
# 3D Void Handling Geographic P2P-RPL for Indoor Multi-Hop IR-UWB Networks
###### Abstract
GVA-P2P-RPL is a modified version of the P2P-RPL standard by applying the greedy forwarding methodology to reduce routing overheads while still discovering near-optimal routes. In advance of the geographic routing step, IR-UWB ranging-based positioning is conducted to obtain accurate indoor 3D position information. With the obtained location estimates, GVA-P2P-RPL greedily unicasts a routing packet to a neighbor node closest to the destination. When a local minimum is encountered, GVA-P2P-RPL switches its mode to the void recovery mode and temporarily broadcasts routing packets until a node closer to the destination is discovered. Then, GVA-P2P-RPL switches back to the greedy mode. In addition, instead of the standard trickle algorithm [8] used in the P2P-RPL standard, adaptive trickle timer (ATT) in 3D GVA-P2P-RPL is proposed. The ATT algorithm, to reduce route discovery time, uses a smaller minimum trickle timer interval value, adaptively shortens its listen-only periods and gives the highest transmission priority to the neighbor node closest to the destination. To show the effectiveness of the proposed GVA-P2P-RPL, we present its simulation results along with the state-of-the-art RPL-based P2P routing protocols, P2P-RPL, PF-P2P-RPL (partial flooding-based P2P-RPL) and ER-RPL. The rest of the paper is organized as follows. In Section 2, we survey related works that have inspired the proposed algorithm. In Section 3, a detailed description of the proposed algorithm is brought. Section 3.1 describes the multi-hop self localization algorithm, and Section 3.2 elaborates upon the novel GVA-P2P-RPL routing protocol. In Section 4, the simulation setting and results are shown. Routing performances are evaluated mainly among four routing protocols, P2P-RPL, PF-P2P-RPL (partial flooding-based P2P-RPL), ER-RPL and GVA-P2P-RPL. Section 5 summarizes the research.
## 3 Experimental Design
### Equation Validation  
In pursuance of validating the 2D pixel representation equations, the following experiment was performed. The angle \(\beta\) was set to 90\({}^{\circ}\), whilst \(\gamma\) was set to four values: 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\) and 90\({}^{\circ}\). The distance \(D\) varied from 20 cm to 200 cm stepped off 20 cm. At each position (different distance and angle) 40 pictures were taken with different color combinations for the turned on LEDs. The acquired photos were processed for determining the location of the light sources, their geometric shape (in our particular case, the form should match a disc) and their centroids' coordinates. With this information, the experimental distance between the LEDs (horizontal and vertical) was calculated and compared with the theoretical ones obtained from Equations (1) and (2). The horizontal mean error was 0.91777 pixels and the vertical mean error 0.86238 pixels (0.91070 pixels for the set of far LEDs, and 0.81119 pixels for the set of close LEDs). Since in both cases the mean error was less than one pixel, we assumed that the equations were validated.
# Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction
## 1 Introduction
Second, spectral clustering adopts an improved weighted k-means algorithm to adjust the clustering performance, which takes both spectral curve shape and distance into consideration to alleviate the large-scope spectral variability. Third, a set of candidates with a small number of pixels is obtained after quadratic clustering, which reduces the complexity of the simplex-based algorithm. The major contributions of this paper are threefold. First, a quadratic clustering-based endmember extraction method is introduced to better deal with spectral variability, as it is one of the current limitations to improve the hyperspectral unmixing performance. Second, parameter analysis is conducted to choose the optimal value of each parameter involved in CSVM, thus enhancing the applicability of CSVM to different datasets. Finally, experiments using real hyperspectral prove that our algorithm has great improvements in endmember extraction, abundance estimation and anti-noise performance, which also indicates that the spectral variability cannot be ignored in hyperspectral unmixing.
## 4 Experimentation and Verification  
To verify the effectiveness of the present method for recognizing PD in an indoor environment, experiments were conducted in the insulation withstand voltage experimental platform shown in Figure 11a. This experimental platform consisted of an AC voltage withstand test system, a test transformer, and a capacitive voltage divider, which could generate a voltage of 0-100 kV for simulating the occurrence of PD. A PD defect was set inside the switchgear, and then the switchgear was connected to the experimental circuit. At the same time, a smartphone was placed on the side 40 cm away from the switchgear for recording audio files, and the sampling rate of the smartphone was 48 kHz. The background noise was recorded for 15 s before powering up the experimental platform, and then the voltage level of the experimental platform was gradually increased. When the voltage level reached 7.2 kV, the PD produced audible sound waves for 30 s, then we reduced the voltage and ended the recording. The total duration of the recording was 60 s and was recorded as Audio 1.  
In addition, to verify that the present method could also recognize the audio signals of mechanical vibrations, a mechanical defect was set up in the switchgear cabinet shown in Figure 11b. When the switchgear was operated under power, the mechanical defect could be clearly noticed as generating vibrations accompanied by audible sound waves. The handheld smartphone first recorded 15 s of indoor background noise and then 45 s of mechanical vibration signals 40 cm closer to the switchgear, and the total duration of the recording was also 60 s, recorded as Audio 2.  
Figure 10: Accuracy of defective audio recognition under different noise components. (**a**) Mechanical vibration; (**b**) PD.  
The recognition was performed on two sets of audio files on a PC, and the recognition process started with data preprocessing; then, the frequency domain feature \(v_{1}\)-\(v_{5}\) were extracted for each frame and finally input into the SVM classifier TF1 for recognition, and the results are shown in Figure 12. For audio 1, the recognition value from the 21 st to the 51 st second was 2, which corresponds to PD, and the rest of the recognition values were 0, which corresponds to background noise. This recognition result was consistent with the experimentally set discharge duration of 30 s. For audio 2, the recognition value from the 15 th to the 60 th second was 1, which corresponds to mechanical vibration, and the rest of the recognition values were 0, which corresponds to background noise. This identification result was in line with the 45 s vibration duration of the experimental setup. The above experiments demonstrated that the present method could accurately identify background noise, mechanical vibration, and PD from audio signals.
# Remote Spectral Identification in the THz Band with Reflection Spectroscopy in an Open Atmosphere
## 3 Methods  
The spectral response measurement of the material was carried out by tuning the source and collecting a data point for each frequency. Due to the different duration of the pulses at different frequencies (Figure 4), information about the spectral properties of the test material was determined on the basis of the total pulse energy. Pulse time width depends on the frequency. In a broad range, the value was 20-25 ns. For each frequency, the pulse width varies, but no more than 1 ns. The relation between the frequency and pulse width is nonlinear. Taking into account the fluctuations in the shape of the pulse and the changes in the optical path length that led to changes in the position of the pulses in time, each received signal was processed separately. Based on the parameters from the 1067 nm trigger signal, the oscilloscope processed the corresponding THz pulse. The pulse energy was calculated by integrating the signal envelope within the appropriate limits. To minimize the measurement uncertainty and impact of other layers, each frequency point was averaged from at least 30 pulses.  
Figure 3: Schematic diagram of the sample holder illustrating the interference reduction: (**a**) view from the side and (**b**) view from the top.  
Figure 2: Measurement setup in the reflection configuration.  
The sample absorption characteristics \(A(f)\) were determined by measuring the reference material, a 13 mm, 100% polyethylene, 400 mg pellet, followed by measuring the prepared 10% material. The sample spectral characteristics were determined by processing the gathered spectra according to the formula  
\[A(f)=log_{10}(I_{1}(f)/I_{0}(f)), \tag{1}\]  
where \(I_{1}(f)\) is the sample measurement, and \(I_{0}(f)\) is the reference measurement.  
To determine the spectral reflectance characteristics \(R(f)\), we needed to perform a reference measurement. In case of the reflection measurements for the reference data, a gold mirror was used, and for the experiment reference measurements, a well-polished flat disc with a 30 mm diameter was used. The reference measurement was followed by the sample measurement, and the spectral reflectance of the sample was determined by processing the gathered spectra according to the formula  
\[R(f)=I_{1}(f)/I_{0}(f), \tag{2}\]  
where \(I_{1}(f)\) is the sample measurement, and \(I_{0}(f)\) is the reference measurement.