esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Irrigation Modernisation and Automation
### Irrigation Modernisation in Developed and Developing Countries  
The above discussion on irrigation modernisation and automation has largely focused on Australia. To provide context, this subsection will briefly examine the scenario in the rest of the world, particularly in Spain and the Unites States, two countries which are also major irrigating economies in Europe and North America, respectively.  
According to [PERSON] [6], developed and some emerging countries generally possess conditions favourable for automation and modernisation of especially large-scale irrigation systems, for instance:  
* Organised manner of supply of water to users;
* Irrigation systems that are generally well-maintained, hence less costly to upgrade;
* Strong policy and regulatory environment, and the willingness and capacity to enforce laws related to water use;
* Availability of technical expertise and equipment; and
* Well-developed infrastructure such as roads.  
Background information about irrigation modernisation and automation in Spain can be obtained from a number of articles, for instance [PERSON] [12] and [PERSON] et al. [13]. Significant Spanish Government reforms and modernisation to manage demand of water began in 2002, and as was the case in Australia, the projects were largely taxpayer-funded, and the objectives included revitalisation of the irrigation sector and water conservation. The programs included improvement of the irrigation water distribution network and promotion of water-efficient methods such as the drip system. Similarly, the modernisation of irrigation systems in the United States aimed to improve the water delivery system using methods such as modification of check structures, use of reticulation systems, improved measurement and control, and the use of SCADA systems [14]. However, in a survey conducted by the United States Department of Agriculture (USDA) a few years ago, it was estimated that at least half of the United States cropland was still irrigated with less efficient irrigation methods [15].
## 3 Results and Discussion  
### Lactic Acid Bacteria Counts and Physicochemical Properties of Fermented Products  
Some featured strains, commonly used in the dairy industry, were activated in a milk medium, and the process was terminated at pH 4.6. In these fermented productions, it is appropriate to terminate the process at pH 4.6, as this is the isoelectric point of casein, the primary protein in milk. At this pH, casein molecules lose their solubility in water, leading to denaturation and curd formation. This curd formation is crucial in many dairy products, such as cheese and yogurt, as it contributes to essential organoleptic properties. Additionally, pH 4.6 inhibits the growth of many spoilage and pathogenic microorganisms, playing a vital role in enhancing the safety and shelf life of the product. The time required for different strains to reach pH 4.6 varies, as shown in Table 1. The fundamental reason for the varying durations that different bacteria take to reach a pH of 4.6 is attributed to the differences in the metabolic rates and fermentation capacities of each bacterial species. These strains have different acid production abilities in terms of speed. These variations are influenced by several factors, including the ability of the bacteria to produce organic acids such as lactic acid during their metabolic processes, the growth rate of each bacterium, the carbon sources utilized by the bacteria, and external factors such as environmental temperature and oxygen availability. Additionally, the tolerance of the produced acids plays a significant role in these differences [28]. In this study, according to Table 1, the lowest growth was observed the LH strain with 8.58 log CFU/mL, while the highest was the LB with 9.52 log CFU/mL. The targeted pH values were achieved at the 25 th and 28 th hours of incubation. No statistically significant differences in the solid content of the bacteria were observed based on Table 1.  
As for tittatable acidity values, the highest acidity was determined in the LC-p sample with a lactic acid value of 0.98% (Table 1). This shows that the LC strain has superior lactic acid production capability. Thus, this _L. casei_ strain can especially be recommended for use in the dairy industry alongside commonly used yogurt cultures such as _S. thermophilus_ and _L. bulgarcius_ to enhance acid production. Additionally, as illustrated in Tables 1 and 2A, total organic acid levels were consistent with tittatable acidity values; however, numerical equivalence was not observed. This inconsistency may be attributed to several factors, including the buffering capacity of the food matrix, the presence of weak acids that do not fully dissociate, or the potential loss of volatile organic acids during analysis. Furthermore, organic acids may form complexes with minerals, thereby reducing measurable acidity during titration.
# CERP: Cooperative and Efficient Routing Protocol for Wireless Sensor Networks
## 5 Prototyping and Performances Exploration
### Design of the Proposed CERP  
A sensor node needs to be able to gather and share data on environmental characteristics. In addition, the hardware and software used to run it must minimize power consumption. Our team has deployed a working model of a leak-detecting system for water. The Arduino MEGA 2560 board (ADVANCE-TEC, Monastir, Tunisia), including an  
Figure 5: Example of leak detection in node 6 and the transmission alert to the CH.  
ATmega2560 operating at 16 MHz, is used to manage the pipeline and is one of the fastest Arduino boards available. This is also very typical of UART-based sensors like that in [43]. An XBee S2C pro-IEEE 802.15.4, (ADVANCE-TEC, Monastir, Tunisia) module by Digi [44] provides radio connectivity. As shown in Figure 6, the XBee module is linked to the Arduino through a UART operating at 9600 bauds to allow for bidirectional communication between the XBee module and the Mega board. Each sensor node, which is based on an Arduino, checks the flow rate against a standard. Following the suggested routing protocol, an alarm will be delivered to the CH when the water flow level exceeds the set threshold.  
The sensors' energy constraint requires using lightweight operating systems, such as TinyOS or ContikiOS, as explained in [8]. Due to its simplicity, flexibility, and greater availability, this work employs ContikiOS [45] as the default OS for physical deployment. Intriguing changes were made in the process of porting the code to Arduino. A demonstration is set up in the lab to assess the effectiveness of the suggested remedy. We test out the prototype in a functioning water pipeline. Indeed, putting the offered solution to work in the initial prototype is a foolproof method of guaranteeing its efficacy. We put this initial version through its paces in a working water pipeline demonstration specifically built for our needs, as illustrated in Figure 7.
###### Abstract  
The potential for the use of smartphones in GNSSs (Global Navigation Satellite Systems) positioning has increased in recent years due to the emergence of the ability of Android-based devices used to process raw satellite data. This paper presents the results of a study on the use of SBAS data transmitted by the EGNOS (European Geostationary Navigation Overlay Service) system in GNSS positioning using a Xiaomi Mi8 smartphone. Raw data recorded at a fixed point were used in post-processing calculations in GPS/EGNOS positioning by determining the coordinates for every second of a session of about 5 h and comparing the results to those obtained with a Septentrio AsteRx2 GNSS receiver operating at the same time at a distance of about 3 m. The calculations were performed using the assumptions of the GNSS/SBAS positioning algorithms, which were modified with carrier-phase smoothed code observations and the content of the corrections transmitted by EGNOS.  
GPS/EGNOS; Satellite-Based Augmentation System (SBAS); smartphone positioning Article  
Optimal Global Positioning System/European Navigation Overlay Service Positioning Model Using Smartphone
## 4 SA Enablers and Interface Design Parameters
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Parameter** & **Detail** \\ \hline \multirow{8}{*}{Content} & & This explains the information requirement to be presented in the AR/MR interface, providing designers/developers with a clear idea of it. Additionally, it specifies the level of the SA (1-perception, 2-comprehension, and 3-projection) of the information requirement. \\ \cline{2-3}  & & This denotes the nature of the information requirement according to the data and/or the context in which it is addressed. The information requirement may be explicit (e.g., text, numeric, date, or file, such as attachments or multimedia) or implicit (e.g., mental constructs). \\ \hline \multirow{8}{*}{Presentation} & Spatial association & This specifies which element(s) in the immersive environment should be associated/linked with the information requirement. \\ \cline{2-3}  & & This specifies how to display the information according to its level of SA, type, and context in terms of colors, visibility scope, and position, among other features. \\ \hline \multirow{8}{*}{Interaction} & AR interaction & This specifies the interaction: devices, modes, tasks, and flow for interacting with the information requirements within the AR environment. \\ \cline{2-3}  & MR interaction & This specifies the interaction: devices, modes, tasks, and flow for interacting with the information requirements within the MR environment. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: SA-enabling AR/MR interface design parameters.
# Optimized Implementation and Analysis of CHAM in Quantum Computing
## 3 Quantum Implementation of CHAM
### Fse'20
According to the post-quantum security strength criteria provided by NIST [12], 128/256 satisfies Level 3, but 64/128 and 128/128 do not exceed Level 1. NIST's post-quantum security requirements specify that the cipher should be similar to or higher than the Grover attack cost for AES to ensure security on quantum computers. Accordingly, it may be considered that 64/128 and 128/128 do not achieve an appropriate security level. However, a document provided by NIST [12] states that the estimated costs should be conservatively evaluated if a quantum attack with a significantly reduced attack cost later appears. The AES attack cost estimated by NIST is cited in a 2016 paper by [PERSON] et al. [16], and implementations that further optimize AES quantum circuits have been recently proposed. A recent Grover attack cost estimation study on AES [20] shows that since Level 1 is 2157, it can be seen that parameters with key size \(k=\)128 also satisfy the security strength. ## 6 Conclusions

In this paper, we present optimizing quantum circuit implementations of the CHAM ciphers. We apply linear layer optimization techniques such as PLU factorization and FSE'20 to CHAM's key-schedule to eliminate additional qubits, saving the number of qubits without significantly increasing the depth of the circuit. In particular, in the case of FSE'20, not only the number of qubits but also the number of CNOT gates and the depth of the circuit are reduced. However, the PLU factorization method has limitations as it leads to an increase in both the number of CNOT gates and the depth of the circuit. While the change in depth is trivial, less than 1%, the increase in the number of CNOT gates is not negligible, ranging from 3 to 40%. In our work, we considered this as a trade-off with qubit. In the case of the FSE'20 method, we concluded that it is the most optimal method in terms of reducing the number of qubits, the number of gates, and the depth. Currently, according to [PERSON] et al. [33], this method has a limitation that it can only be used for linear layer matrices of size 32 or less. The performance of future quantum computers is not yet known accurately, and for this reason, it is difficult to predict how secure the existing cryptography will be in the quantum computing environment. Accordingly, NIST proposed to measure security strength through computation resource estimation. The value provided as a security level in this standard is the cost estimated to be incurred when attacking AES through a Grover attack. Evaluating what level of security the ciphers has in the post-quantum environment through this criterion and whether it can be used is a very important task as it prevents attacks that may occur in the future. So, we estimate the quantum circuit cost required for Grover attacks and evaluate the security level. As a result, it can be considered that all methods satisfy the security strength by satisfying the cost presented in the recently proposed paper on optimized quantum circuits in AES [20].
# Experimental and Parametric Analysis of Pull-Out Resistance of Notched T-Perfobond Connectors
## 2 Pull-Out Test
### Specim Layout  
Figure 1 presents the constructional forms of the pull-out model specimens for the per-fobond connector. The standard-model UHPC cube has a size of 500 mm \(\times\) 500 mm \(\times\) 500 mm. The T-perfobond rib is physically attached to the steel beam through welding. In the pull-out test, the prototype steel beam only needs to retain its steel flange and a portion of the web, forming a T-beam. Perfobond shear connectors are vertically welded to the T-beam, and the actuator is connected to the T-beam to transfer the force to the perfobond rib. Considering that the penetrating rebar is not truncated for its full length in the actual project, it is designed to be equal to the side length of the UHPC cube at the geometric center of the hole. The buried depth (\(h\)) of the notched T-perfobond shear connectors is 40 mm. A 6 mm-thick flange is added to the bottom of the connectors to increase the thickness, with an actual buried depth of 46 mm. A 30 mm space is left between the T-beam and the concrete surface to facilitate the observation of damage to the concrete and the perfobond rib. Four specimens are embedded along all four sides of the UHPC cube. Due to the shallow buried depth of the specimens, they do not affect each other, and the design can reduce the use of UHPC materials by approximately 40%.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Specimen** & \(d_{1}\) **(mm)** & \(d_{h}\) **(mm)** & \(d_{r}\) **(mm)** & \(t_{p}\) **(mm)** & \(t_{f}\) **(mm)** & \(b_{f}\) **(mm)** & \(l_{p}\) **(mm)** & \(c_{w}\) **(mm)** & \(T_{u}\) **(kN)** & \(T_{u,\text{erg}}\) **(kN)** \\ \hline \hline PFR-1 & 40 & 20 & 8 & 6 & 6 & 26 & 80 & 0 & 140 & \\ PFR-2 & 40 & 20 & 8 & 6 & 6 & 26 & 80 & 0 & 124 & \\ PMFR-1 & 40 & 20 & 8 & 6 & 6 & 26 & 80 & 16 & 102 & \\ PMFR-2 & 40 & 20 & 8 & 6 & 6 & 26 & 80 & 16 & 99 & 100.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model grouping and experimental results.  
Figure 1: Layout of pull-out test specimens. (**a**) Front view; (**b**) side view; (**c**) top view; (**d**) 3D view. (Note: The black lines indicate the concrete cube, the black triangles indicate the concrete fill; the blue lines indicate the steel plate; and the red lines indicate the rebars.)
# SMS-Net: Bridging the Gap Between High Accuracy and Low Computational Cost in Pose Estimation
## 1 Introduction
Moreover, through various experiments using the widely used MPII Human Pose [6] and COCO [7] datasets, the effectiveness of the lightweight techniques applied in this study was demonstrated, while the balance between the computational efficiency and performance of the proposed system compared to existing models was improved. The first contribution of this study is the SMS-Net model, which applies various lightweight techniques to minimize floating-point operations and memory access costs, using fewer weights for pose feature extraction. Additionally, the performance of the proposed model was validated using representative public datasets used for pose estimation, and its effectiveness and inference efficiency were demonstrated through comparisons with existing research results. The remainder of the paper is structured as follows: Section 2 introduces the latest deep learning-based pose estimation algorithms, and Section 3 provides a detailed description of the structure of the proposed SMS-Net and the applied lightweight techniques. Section 4 presents the implementation and training of the model, along with the experimental results using the MPII and COCO datasets. Finally, Section 5 summarizes the conclusions of the study.
## 2 Materials and Methods
### Data Analysis  
CW-NIRS signals and torque/angle data were synchronized with an external device (PortaSync, Artinis Medical Systems, Elst, The Netherlands) and transferred to MATLAB (The Mathworks, Inc., Natick, MA, USA, version R2021a) for further analysis. Out of the three submaximal trials performed at each contraction condition, only the trial with the lowest standard deviation from the biofeedback torque target curve (30% MVC) was chosen for further analysis. The torque and ankle joint signals were filtered using a fourth-order 5 Hz low-pass filter.  
Figure 1: Experimental Setup. (1) Pressure cuff. (2) CW near-infrared spectroscopy device + adhesive tape. (3) Footrest including safety belts.  
For statistical analysis, the mean torque value for each trial was calculated over 25 s. For normalization of the submaximal torque values, we used the highest peak torque value from the MVCs.  
The estimation of the oxygen consumption was analyzed using five different approaches. The slope was calculated from:  
1. TSI signal;
2. Difference between O\({}_{2}\)Hb and HHb (Hbdiff);
3. Mean of the slopes from O\({}_{2}\)Hb and HHb (Hbmean);
4. HHb signal;
5. O\({}_{2}\)Hb signal.  
The signals were smoothed using local regression smoothing (Loess smoothing, span 10%). Using simple linear regression modelling, the linear part of the initial slope from the different approaches was defined (r\({}^{2}\) > 0.99), representing the estimated oxygen consumption per second (Figure 2).  
mVO\({}_{2}\) was normalized using the respective delta value. The delta value was defined as the difference between the value at the onset of the contraction and the minimum (TSI, Hbdiff, HHb) or maximum (O\({}_{2}\)Hb) value that occurred during the contraction. In addition, we analyzed the tHb level to detect possible shifts in the blood volume during the analysis window. The analysis window represents the time between the start and end of the linear slope. This time window was split in two sections of equal length (t1, t2). For both sections, a mean value was calculated.  
Figure 2: Exemplar data showing the five different approaches to estimate muscle oxygen consumption out of the initial slope using simple linear regression modelling (dashed lines, r\({}^{2}\) > 0.99). (1) Slope of oxygenated hemoglobin (**A**: negative line). (2) Slope of deoxygenated hemoglobin (**A**: positive line). (3) Mean out of slope from oxygenated and deoxygenated hemoglobin (**A**). (4) Estimation of the slope out of the subtraction of oxygenated and deoxygenated hemoglobin (**B**). (5) Slope of the tissue saturation index (**C**).
# Ensemble Clustering in GPS Velocities: A Case Study of Turkey
## 2 Data and Methodology
### Gap Statistic Algorithm  
The Gap is one of several methods determining the number of clusters \(k\) that best represents a given data set. The Gap algorithm is a data mining statistic developed by [PERSON] et al. [24] to measure the significance of the clustering for each \(k\). This procedure determines the validity or the goodness of fit of the clustering evaluations. Moreover, it is a popular internal indicator such as Silhouette and/or Davies-Bouldin in terms of test data processing of building the clustering algorithm. The advantage among other methods that have different computational models for determining the optimal number of clusters is that it is designed  
Figure 1: GPS velocity field with respect to Eurasian tectonic plate from [15], gray thin lines show active faults [23].  
Figure 2: The flowchart of this study.  
to be applied to any clustering technique and distance measure. For any \(x_{ij}=1,2,\ldots,n\) observation data with p features, this method compares the total intra-cluster variation in \(k\) different number of clusters with the expected values of the data under a distribution of reference null hypothesis (i.e., no significant clustering). It can be achieved by generating a random distribution of points (Monte Carlo sampling method) that occupy the same spatial extent in velocity space as does the data to be tested [12; 24]. A measure of the quality of the \(r\)th cluster of given data set is calculated using Equation (1).  
\[D_{r}=\sum_{i,i^{{}^{\prime}}=1}^{n_{r}}d_{ii^{{}^{\prime}}} \tag{1}\]  
In Equation (1), the sum for both \(i\) and \(i^{{}^{\prime}}\) is overall \(n_{r}\) points within the \(r\)th cluster and \(d_{(ii^{{}^{\prime}})}\) is the squared Euclidean distance in velocity space between the observations \(i\) and \(i^{{}^{\prime}}\) in each class. After that, the sum over all \(k\) clusters \((W_{k})\) according to the mean of each cluster is defined as:  
\[W_{k}=\sum_{r=1}^{k}(\frac{1}{2n_{r}}D_{r}) \tag{2}\]  
where \(n_{r}\) is the number of points in cluster \(r\). At this point, for different values of \(k\), the total within-cluster variation between the observation and the reference data is calculated as in Equation (3) by standardizing \(log(W_{k})\) compared to the expected distribution of the reference data in the zero-mean distribution:  
\[GAP_{n}(k)=E_{n}^{*}log(W_{k}^{ref})-log(W_{k}^{obs}) \tag{3}\]  
where \(E_{n}^{*}\) is expressed the expected value of \(n\) samples in the reference data distribution. While the superscript \"ref\" refers to a random, null, and reference data set, the superscript \"obs\" represents the observed data set being tested. The choice of the optimal number of clusters with the Gap statistic \((GAP_{n}(k))\) is the value of \(k\) at which \(log(W_{k})\) has the longest decline below the reference value.