esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
In WSNs, during communication, energy consumption is a highly prominent issue that seeks attention. Energy efficiency has a greater impact on the entire performance of the network and acts as a significant function in the lifespan of a WSN [4]. The metrics that are essential while routing and estimating the cost function (CF) in a WSN are total energy, energy consumed and residual energy. Energy is one of the most significant aspects of the efficient working of wireless sensor networks. The longevity of the network is totally dependent upon the optimum use of the available energy; therefore, optimization is much needed for the efficient utilization of energy. Routing protocol, considered as an important factor, has to be selected carefully to route the data packets safely to the destination with less overhead because of the resource constraints of SNs, namely, their limited power and shorter range for communication. Numerous efforts have been made to bring out the best solutions for WSNs, yet extraordinary works are required [5]. The benefits of the IoT are as follows. An urban IoT has many facilities that may bring a number of benefits such as the management and optimization of traditional public services, including transport and parking facilities, lighting, the surveillance and maintenance of public areas, preservation of cultural heritage, garbage collection, public health care, i.e., hospitals, and schools. Therefore, the availability of different types of data that will store in the cloud or be collected by a data warehouse in the urban IoT should increase the transparency and promote the actions of the local government or municipality towards the citizens, which can increase the awareness of people about the status of their city and their life style. Therefore, the application of the IoT paradigm to the smart city is attractive and expansive for local government and administrations; however, it take times to adopt IoT technology in a wide manner. This research is organized as follows: A detailed review of the literature related to this research is described in Section 2. The design, algorithms and functionality of the proposed protocol are elaborated on in Section 3. The simulation results of the proposed protocol are presented in Section 4, which is followed by the conclusion in Section 5.
# Ammonia Gas Sensing Characteristic of P3 HT-rGO-MWCNT Composite Films
## 2 Experimental
### Characterization Techniques  
The surface morphology, crystal structure, and optical absorption of the samples was characterized using a \"Hitachi S-4800\" field emission scanning electron microscope (FE-SEM), D5000 with CuK\(\alpha\)1 radiation (\(\lambda\) = 1.5406 A) and a UV-Vis spectrophotometer (Jasco, V-670), respectively. The technique reported by [PERSON] et al. [31] to monitor NH\({}_{3}\) gas as described below was used in this study. The sensors were placed in a testing chamber with a volume of 10 dm\({}^{3}\). By using the \"EPA-2 TH\" promoter (USA), the chamber airflow can be precisely controlled (from 1 to 100 sccm) corresponding to an NH\({}_{3}\) concentration ranging from 10 ppm to 90 ppm. The sensor's gas sensitivity was assessed by the following steps: the sensor was placed in a test chamber at a temperature, pressure, and humidity of 300 K, 1 atm, and 10%, respectively; The 2400-Keithley power meter was used to determine the resistance between the two electrodes of the sensor. The sensor's gas sensitivity was assessed through two adsorption and desorption processes. During adsorption, a gas stream (or vapor) consisting of the Ar carrier and the measured vapor was introduced into the test chamber for a period of time, and a change in the sensor resistance was recorded. During desorption, a stream of dry Ar gas was introduced into the chamber to restore the original resistance of the sensors. The sensitivity and desorption of the sensor can be assessed by the time-dependent change in resistance.
# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
## 2 Related Work
### Human-Computer Interaction
#### 2.2.3 F-Shape and Horizontal Left Patterns  
A study in human-computer interaction using eye-tracking technology has also been used to better understand human on-screen behaviour, out of which two clear patterns have emerged-the \"F-shape\" [36] and \"horizontal-left\" [37]. The terms come from gaze pattern analysis which has shown that the human eye naturally reads material in an F-shape, reading horizontally from left to right along the upper content of the page, then travelling vertically down the left-hand side and traversing horizontally across the next section of information again from left to right, but not continuing along as far to the right-hand side as the first section, before finally continuing vertically along the left-hand side before finishing. This pattern forms an \"F\" shape on a heatmap produced by the gaze pattern [36].  
In terms of the \"horizontal left\" pattern, this refers to the user's eye-tracking patterns appearing significantly more on the left-hand side of the page than on the right. A study performed in 2010 found that 80% of fixations fell on the left-hand side of the page, with only 20% of fixations on the right-hand side [37]. Taking the previous information regarding the typical \"F\" shape reading pattern, it is understandable that these figures are so disproportionately high as the scrolling vertically on the left-hand size would lean to a higher amount of left-side fixations. With this information, graphic designers are able to adjust the layout and content on websites accordingly, placing the most important information in the top few lines and along the left-hand side of the page where viewers are most likely to fixate on it [36].
# Numerical Simulation of Airflow Distribution in a Pregnant
## 6 Conclusions  
A CFD model of a centralized ventilation pregnant sow piggery with a ceiling air intake and a central air exhaust was developed, and the following conclusions were obtained:  
1. The CFD model was used to simulate the temperature and humidity distribution in the piggery. The maximum difference between the simulated and the experimental temperature values was less than 1.54 \({}^{\circ}\)C, and the relative error ranged from 0.28% to 5.99%, with an average relative error of 3.06%. The maximum difference between the simulated and the tested relative humidity values was less than 10% RH, and the relative error ranged from 0.06% to 13.14%, with an average relative error of 7.68%. Therefore, good agreement between the simulation and test results was demonstrated.
2. The temperature and humidity fields and the uniformity coefficient of the two air outlet locations were compared. When the temperature field is given priority, positioning the air outlet in the middle of the unit will help to increase the uniformity of the temperature field but will reduce the uniformity of the humidity field.
3. With an increase in the air speed in the inlet, the temperature showed a downward trend while the relative humidity showed an upward trend; however, the uniformity of the two both increased first and then decreased. Considering the temperature and the humidity, along with the corresponding non-uniformity coefficient, the ventilation speed of 0.5 m/s was selected as the optimal inlet speed for the pregnant sow piggery.
4. With an increase in the velocity at the air inlet, the uniformity of the temperature distribution on the body surface of pregnant pigs decreases, while the uniformity of the relative humidity distribution increases.  
In this study, the coupling simulation of airflow in the pregnant sow piggery was carried out to obtain the characteristics of temperature and humidity distribution and air organization in the piggery, which has certain reference significance for the optimization of airflow in piggeries with centralized ventilation.  
Conceptualization, X.W. and Z.Z.; methodology, H.L.; software, X.W.; validation, X.W., B.L. and E.L.; formal analysis, J.G.; investigation, Y.J.; resources, B.L.; data curation, X.W.; writing--original draft preparation, X.W.; writing--review and editing, Z.Z. and E.L.; visualization, J.G.; supervision, H.L.; project administration, H.L.; funding acquisition, B.L. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
The Project of Collaborative Innovation Center of GDAAS-XTXM202201; the Innovation Fund Project of Guangdong Academy of Agricultural Sciences (202202); the Youth Tutorial Program of Guangdong Academy of Agricultural Sciences (R2021 QD-023); Independent Research Project of Maoming Laboratory (2021 ZZ003); Key Research and Development Program of Guangdong Province (2019B020225001); the Key Laboratory of Modern Agricultural Intelligent Equipment in South China, Ministry of Agriculture and Rural Affairs, P.R. China (HNZJ202209).  
Not applicable.  
The authors declare no conflict of interest.
# Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration
## 3 Methodology
### Appliance Identification Based on Multi-Feature Integrated Classification (MFIC)
#### 3.6.2 Appliance Recognition Based on Linear Discriminant Classifier Group  
This section aims to label each appliance candidate model based on similarity indices. In order to synthetically consider the effects of various features in appliance identification, a linear discriminant classifier is designed for each appliance based on the similarity of all features. All the classifiers constitute a linear discriminant classifier group. The similarity is calculated by the sum of weighted similarity of different features. Because the feature weights of different appliances are inconsistent, the particular weight vector needs to be set for each linear discriminant classifier separately. It is firstly estimated by observing the difference of different appliances' features. For instance, a refrigerator has specific ON-duration and OFF-duration, so the two features will be emphasized, while they are not important for light. Generally, the intrinsic features are more important than statistical features since statistical features are easily influenced by the external environment. After the predefinition of weight vectors, it is necessary to adjust their values to exploit the test data in different times and environments, so as to ensure the identification accuracy.  
The detailed process of labeling appliance candidate models is described below. At first, the intrinsic and statistical features of each model are extracted. Then each unlabeled model will be classified by the linear discriminant classifier group in this particular house. The classification result of the \(j\)th classifier is calculated as:  
\[d(\mathbf{S}_{j})=\boldsymbol{\omega}_{j}{}^{T}\mathbf{S}_{j}-\delta_{j} \tag{16}\]  
where, \(\omega_{j}\) stands for the weight vector of the \(j\)th classifier, \(\mathbf{S}_{j}\) includes feature similarity indices between the unlabeled model and the \(j\)th classifier, and \(\delta_{j}\) represents the judgment threshold of the \(j\)th classifier. If \(d(\mathbf{S}_{j})\)\(>\) = 0, the unlabeled model is determined as the appliance corresponding to the \(j\)th classifier; otherwise not.
# Real-Time Contrail Monitoring and Mitigation Using CubeSat Constellations
## 4 Distributed Systems with CubeSats
### Preliminary Constellation Simulations
The longer contact times, depicted by darker blue regions, are concentrated at mid-to-high latitudes, suggesting that the constellation can maintain extended communication periods in these areas. This extended duration is advantageous for continuous monitoring missions, providing ample time for data acquisition and reducing the likelihood of data loss. Simulation results from comparable scenarios suggest that while circular orbits with numerous satellites distributed across multiple planes can achieve over 90% availability, the operational and financial burden of deploying and maintaining such a large constellation makes this approach less feasible, with over 150 such satellites required to achieve the minimum viable coverage required for initial operations. By contrast, a constellation with fewer satellites in highly eccentric orbits offers a viable alternative, with fewer than ten satellites potentially sufficient to satisfy mission requirements. An essential aspect of this design is the optimization of handover timing between satellites to minimize data loss and ensure seamless coverage. Such a highly eccentric constellation is uncommon, with almost all nanosatellites launched to date orbiting in near-circular orbits below 1000 km in altitude [57]. As such, the configuration proposed above will occupy a rarely used region of near-Earth space to increase both the performance and operational efficiency of the constellation. Figure 5: Minimum **(a)**, average **(b)**, and maximum **(c)** blackout duration over the North Atlantic Corridor region for various highly elliptic orbit configurations. Subfigure **(a)** shows the shortest blackout durations, **(b)** depicts the average blackout durations, and **(c)** highlights the longest blackout durations. The color bar represents the blackout duration in minutes.
# Decentralized Storage with Access Control and Data Persistence for e-Book Stores
## 3 Existing Decentralized Storage Systems
### Filecoin  
Filecoin [21] serves as an incentive layer of an IPFS, introduced in 2014 with its Mainnet launched in 2020, addressing the critical issue of data persistence. To store data in Filecoin, users first engage in contracts with storage providers, referred to as miners. These contracts define the data's storage volume, duration, and the corresponding price for the storage service. Upon entering into an agreement, users reward miners with native tokens (FIL), and the transaction history of FIL is recorded on the Filecoin blockchain. Filecoin implements two incentive mechanisms, namely proof of replication (PoRep) and proof of spacetime (PoSt), to bolster data persistence within the IPFS. PoRep operates as a challenge-response protocol that verifies the presence of a file in the designated storage. Once the network successfully verifies the proof, the result is recorded on the blockchain. PoSt, on the other hand, enforces miners to periodically submit proof and validate the correctness of their stored data. Miners failing to submit or verify their PoSt will incur penalties, leading to a partial loss of their deposit. Additionally, Filecoin leverages a combination of erasure coding and replication strategies to heighten data availability.  
Access control for stored data in Filecoin can be implemented in the same manner as in al-IPFS. However, Filecoin is not suitable for our e-book repository, which requires indefinite data retention. This is because the content storage contract in Filecoin is established by specifying a completion time for the contract. The requirement to set a contract completion time is rooted in the design concept of paying for data storage with native tokens and the inherent challenge of accurately predicting the long-term price fluctuations of the cryptocurrency used for rewards. As a result, distributed storage systems such as Storj [22] and Sia [23], which offer contract-based persistence, are also unsuitable for our intended purpose.
## 3 Materials, Methods, and Equipment
### Classical Bearing Capacity Methods
#### 3.6.8 [PERSON] and [PERSON] (1981) Method  
The method of [PERSON] and [PERSON] (1981) calculates circular pile bearing capacity in sand through empirical design charts, relating bearing capacity factor (\(N_{q}\)*) to relative depth (\(L\)/\(d\)) and soil friction angle (\(\varphi\)). Their approach plots relative depth against the bearing capacity factor on a logarithmic scale, with curves illustrating variations across friction angles from 30\({}^{\circ}\) to 40\({}^{\circ}\)[42]. A distinctive feature is the recognition that bearing capacity factors reach limiting values at greater depths, as shown by curves becoming vertical. This reflects the critical depth concept in pile foundations [43]. The method's empirical foundation in pile load test data enhances its practical utility, though its primary database of driven piles may require modifications for other pile types. The charts demonstrate that higher friction angles produce larger bearing capacity factors, particularly at lower relative depths where curve separation is more pronounced. End-bearing and shaft resistance calculations follow [PERSON]'s equations, assuming full mobilization. Figure 7 depicts a sequential research methodology used for analyzing hybrid composite pile systems, integrating multiple characterization techniques and analytical methods to evaluate the ultimate bearing capacity and load transfer mechanisms in steel-concrete and timber-concrete composite foundations.  
Figure 7: Comprehensive methodology framework for investigating hybrid composite pile foundations.
# 5G/B5G mmWave Cellular Networks with MEC Prefetching Based on User Context Information +
## 3 Prefetching Algorithm
### Process for Data Prefetching  
First, we consider which small-cell BSs should be selected to prefetch the UE data. It needs to know the UE context information, such as the destination, traffic demand, and mobility. Figure 5 presents the flowchart of how to collect and use such context information in the pre-association process, while Figure 6 visualizes this process. First, the UE destination is assumed to be predicted by specific applications, e.g., the UE's private calendar/agenda. This application will also predict the type and amount of traffic that the UE will demand at the predicted destination.  
In the next step, based on the information about the UE destination, the 5G operator will predict its received Signal-to-Interference-plus-Noise Ratio (SINR) using a power map measured in advance (e.g., at the time of deployment of small-cell BSs) and pre-associate the UE to the small-cell BS, which yields the highest SINR to the UE for facilitating the prefetching process as explained in the next step. When UE is close to the destination, the 5G operator's mobility management function will track the UE movement and predict the time \(t_{u,t}\) that the UE will arrive at the destination and request the \(n\)-th traffic. If \(t_{u,n}\) is smaller than \(T_{\text{p}}+t\), which is a design parameter called the prefetching window in this paper, the future demand traffic will be prefetched. Since there will be different traffic requests from many UE within this prefetching window, a prefetching algorithm for proper scheduling is required, which will be explained in the following section. This paper assumes that context information can be predicted perfectly.
## 5 Control Strategy for Strand Tension Equilibrium
### The Design of The Discrete Sliding Mode Controller  
The design process of the sliding mode controller is shown in Figure 10[19; 20]. Based on the system models of each numbered strand, the state space equations are constructed, which are represented by Equation (14):  
\[\left\{\begin{array}{l}x(k+1)=Ax(k)+Bu(k)\\ y(k+1)=Cx(k)+Du(k)\end{array}\right. \tag{14}\]  
where \(A\), \(B\), \(C\), and \(D\) correspond to the state matrix, input matrix, output matrix, and direct action matrix of the system, respectively, with \(x(k)\) representing the initial state of the state space equation, \(y(k)\) representing the current output of the system, and \(u(k)\) representing the system control variable. The design of the discrete sliding mode surface is:  
\[s(k)=Px(k) \tag{15}\]  
\(P=P[p_{1},p_{2}\ldots p_{n}]\) represents the coefficients of the designed sliding surface. Based on the discrete index convergence rate, the sliding mode controller is designed as in Equation (18):  
\[s(k+1)=s(k)+T(\mathrm{esgn}(s(k))-qs(k)) \tag{16}\]  
where T is the system sampling time, \(\varepsilon>0\), \(q>0\), and \(1-qT>0\). The discrete sliding mode control law based on the exponential convergence rate can be obtained as follows:  
\[u(k)=-(PB)^{-1}[CAx(k)-(1-qT)s(k)+\varepsilon Tsat(s)] \tag{17}\]  
where sat (s) is the saturation function.  
Figure 10: Design of discrete sliding mode controller.
# Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset
## 2 Recording of Visual Data  
Electric power lines are very specific objects similar to other industrial infrastructure facilities, usually covering a wide area. This requires performing periodic inspections for proper maintenance. Usually, periodical flights are made by airplanes or unmanned aerial vehicles [7], during which visual material is recorded depicting the state of individual elements of overhead high voltage lines. The continuous development of imaging methods allows image-recording at increasing resolutions with an increasingly larger number of frames per second. Obtaining desired information from the acquired visual material requires application of appropriate processing and the development of new analysis methods for the detection of specific irregularities affecting the proper functioning of the inspected object. The visual data (video, series of frames) recorded during a controlled flight, due to the vastness of electric power infrastructure and the consecutive development of imaging techniques, are currently characterized by very large volume, whereas analysis conducted by humans is expensive, inefficient in terms of time, burdened with many errors, and dependent on the perception and experience of the person making the inference based on visual material.  
At the same time, human analysis is characterized by high flexibility in decision-making in unusual situations that require complex multi-criteria analysis and the need to consider many aspects from different fields. The development of artificial intelligence, in the field of robotic process automation (RPA), among others, allows for effective algorithm application in the area of visual material analysis. In terms of inspection of the state of technical infrastructure, the challenges for these types of algorithms are as follows:  
* The need to use advanced methods of image processing (deep learning);
* The need to build appropriate datasets which enable the construction of models describing the essential elements of technical infrastructure;
* A high degree of complication in making decisions concerning the assessment of the state of the examined object.