esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Motivation and Contribution  
The factors that influence sound perception on the balconies are the morphology of the balcony, the materials, the height of the balcony's location on the facade, and the surrounding urban configurations and use [9]. In this direction, the height of the floor where a balcony is located is an essential parameter that allows for different types of atmospheres, as it can affect the visual and sensory experience. For example, if a balcony is located on a high floor, the view can be expansive, providing a sense of openness and freedom. The height can make the balcony feel more secluded and private, as it is removed from street-level activity. Additionally, being at a greater height can create a sense of thrill or excitement, as one feels a sense of being suspended in the air. On the other hand, if a balcony is located on a lower floor, the view may be obstructed by buildings or trees, limiting the sense of openness and freedom. The balcony may feel more exposed to street-level activity, reducing the sense of privacy. Being at a lower height can create a more grounded feeling connected to the surrounding environment. In this sense, the height of a floor where a balcony is located can greatly impact the visual and sensory experience of being on the balcony, and can create different types of atmospheres.  
In this work, our goal is to determine whether sound perception in the form of sound fragments can be used to automatically determine the floor on which a balcony is located. If this identification can be done efficiently, new components could be included to the design of balconies, as designers could better suggest the exposure, morphology, and arrangement tailored to balconies with regard to the impression of a sound environment connected with the height of its floor.  
In this work, the problem of floor identification of a balcony is tackled by analyzing its sound environment as an audio classification problem using the associated soundtrack to characterize this environment. This task involves learning to classify sounds and predicting the class (in this case, the floor height) of a new sound.  
Although machine learning has made significant advancements in the field of audio categorization recently, this issue remains open and challenging, as ambient everyday noises includes a great deal of information [10]. Recent research suggests using machine learning for sound and acoustic analysis [11; 12; 13]. To the best of our knowledge, no research has been done on the application of learning strategies for taking advantage of balcony soundscapes. In this paper, a novel machine learning-based approach is proposed to automatically classify recordings with soundtracks from balconies into the respective floors.
# A Comparative Study of Coverage Hole Detection Techniques in Wireless Sensor Networks +
## 3 Experimental Setup  
The resulting experimental example has a 40 m range, since it is anticipated that the transmitter and receiver distance will be twice as great as what the sensors can detect. Each node is given a set of instructions on how to find the sensor array of its failed neighbor to learn more about that node's options from its one-hop neighbors.
## 2 Frequency Regulation Strategy of WTG Based on VSG Control  
### VSG Control  
Due to the converter control, the rotor speed of the WTG is completely decoupled from the frequency of the power system, so the WTG lacks frequency support capacity. In practice, the control technology of the WTG can be improved by simulating the characteristics of a synchronous generator so that the WTG can provide inertia and damping for the power grid. Figure 1 is the VSG control block diagram.  
The VSG control strategy mainly includes active power-frequency control and reactive power-voltage control. The active power-frequency control equation of VSG is shown in Equation (1) [6]:  
\[J_{\text{vsg}}\frac{d(\omega_{\text{vsg}}-\omega_{0})}{dt}=P_{ref}+K_{\text{ vsg}}(\omega_{0}-\omega_{\text{vsg}})-P_{\text{W}}-D_{\text{vsg}}(\omega_{\text{ vsg}}-\omega_{0}) \tag{1}\]  
The reactive power-voltage governing equation of VSG is shown as follows [6]:  
\[E=K_{\text{q}}(Q_{\text{ref}}-Q)+U_{\text{ref}} \tag{2}\]  
The VSG control is applied to the frequency regulation of the WTG to improve the frequency stability of the power system.  
Figure 1: VSG control block diagram.
## 3 Results and Discussion
### Physicochemical Parameters Variation during Desalting Process
#### 3.2.3 Soluble Protein Content Variation  
Figure 2C shows the soluble protein results obtained during cod desalting. The results showed that the soluble protein content decreased (\(p<0.05\)) during desalting from a value of 138.8 \(\pm\) 10.4 mg albumin/g IS (salted cod) to 71.3 \(\pm\) 0.5 mg albumin/g IS (20 \({}^{\circ}\)C) and 70.3 \(\pm\) 8.5 mg albumin/g IS (4 \({}^{\circ}\)C) after 4 h of cod desalting, maintaining (\(p>0.05\)) similar values during the remaining desalting time. Since a similar behaviour was observed at both temperatures, it may be concluded that soluble protein variation was not affected by the desalting temperature. This pronounced decrease in soluble protein content in cod muscle may be explained by its dissolution in the desalting water ([PERSON], 2010) [7].  
The percentage of soluble protein loss by the cod muscle was 65 and 58% at 4 and 20 \({}^{\circ}\)C, respectively, compared to the initial value. [PERSON] et al. (2002) [27] monitored the total protein content (insoluble and soluble) in cod muscle and observed a protein loss of 32% after 110 h of desalting at 3 \({}^{\circ}\)C. This value is lower than that observed in this study; however, it is important to note that this value corresponds to the total protein content (soluble and insoluble).
## 1 Introduction
### Related Work  
The joint problem of VNF placement, SFC chaining, and routing in wired networks, mostly targeting cloud environments in the network core, has been widely studied with tools such as Dynamic Programming [11], knapsack algorithms [12], Monte Carlo Tree Search [13], and Benders decomposition [14]. The above works, including the recent ones in [7; 15; 16], formulate NP-hard Mixed Integer Linear (MILP) and Nonlinear Programs, for which heuristic algorithms are proposed and numerically evaluated. Hence, their essential differences lie in the considered constraints and objectives (i.e., minimum total network power consumption in [15], link utilization, overhead, and server power consumption in [16], and monetary profit in [7]). Models with a distinct MEC/cellular flavor appear in [17; 18] (see also survey in [19]); however, [17] does not consider power consumption, while [18] models the link constraints more abstractly than in our paper and uses a different objective (i.e., minimize maximum link utilization).  
Regarding Network Slicing, SDN, and NFV/MEC orchestration, many platforms, both commercial and open-source, provide functionalities that target the management and control of technology-specific resources. The Open Source Management and Orchestration (MANO) framework proposed by the European Telecommunications Standards Institute (ETSI), considered the most mature standardized solution for Network Service Lifecycle Management (LCM) [20], also supports Network Slicing but is not currently targeting the management of radio elements. On the other hand, O-RAN, being the most successful of the open Radio Access Network (RAN) initiatives, targets specifically the virtualization and management of radio functions [21]. During Horizon 2020 5G Infrastructure Public Private Partnership (5G-PPP) phase 2, some research initiatives focused on prototyping Network Slicing solutions considering (mostly) computational resources [22] and integrating, in some cases, mechanisms for joint management of the transport [23] and radio network segments [24].
# Robotics Applications in the Hospital Domain:
## 3 Robotics in Healthcare: An Overview
### Applications Across Medical Fields  
Hospitals can implement various types of robotic systems to improve efficiency and reduce the risk of human error [1]. Robotic systems are integrated assemblies that include one or more robots together with the control systems, software, and infrastructure required for their operation. These systems are designed to perform complex tasks that require precision and repetitiveness, often replacing or complementing human labor in areas such as surgery, rehabilitation, and hospital logistics [10,11]. Robotics has a wide range of applications in the medical field:  
1. Telepresence and Remote Monitoring: robots can be employed to facilitate remote medical consultations, patient monitoring, and remote assistance in cases of limited accessibility or emergencies [3];
2. Transportation: robotic systems are used for the autonomous delivery of medicines, biological samples, and medical supplies within hospitals, thus optimizing logistics and reducing human error [5];
3. Medical Training and Simulation: Robotic simulators are employed to educate medical trainees and healthcare professionals in surgical and diagnostic procedures, providing practical experience in a controlled environment [12].  
The examples of robot applications in hospitals show that robots are increasingly being adopted in this context and that there is also a growth in both robotic and emerging technology that can enhance care efficiency and quality [13]. Although most of theinvestment in robotic research, development, and the production of robots focuses on surgery and rehabilitation, it is necessary to emphasize less explored areas such as cleaning and disinfection in hospitals [1], medical training and simulation, medical diagnosis and imaging, operating theatre assistance, and more. In addition, it is necessary to improve the adaptability of robots in various hospitals contexts, allowing them to operate efficiently in different settings. Improving HRI by developing dynamic algorithms and models that enable evaluation in real-world settings, such as crowded hospital corridors, is crucial [14; 15]. Furthermore, it is necessary to expand the capabilities of robotic systems, such as increasing the accommodation capacity, specifically in rehabilitation systems, and making them more adapted to the needs of patients [15]. This development highlights the need for innovative and robust solutions that can support different medical contexts [1; 14; 16].
## 2 Literature Review
### Deterministic and Stochastic Programming in Multiperiod SSOA  
Various strategies have been explored to manage the risks within the framework of the multiperiod SSOA problem. One prevalent approach involves the development of mathematical models, often formulated as linear programming models, where risk parameters are expressed as deterministic values, such as rates or percentages. In several notable studies, including those by [21; 22], risk has been quantified in terms of deterministic rates and percentages. For instance, ref. [21] assessed supplier quality risk by quantifying the expected defect rate of purchased components, while ref. [22] measured supplier delivery delays and quality risks as percentages of products delivered on time and rejection rates of products, respectively. An alternative approach demonstrated in the investigation by [23] involves representing supplier disruption risk based on the availability of suppliers per period. This approach recognizes the impact of supplier availability on the overall risk profile and decision-making process within the SSOA problem. Conversely, ref. [24] adopted a comprehensive perspective by concurrently considering multiple risks alongside quality risks. They employed a failure mode and effect analysis (FMEA) to compute a single-risk metric summarizing the identified risks. Similarly, refs. [25; 26] integrated various risk factors into a single metric using different methodologies, facilitating a holistic risk assessment within the SSOA framework. Lastly, ref. [27] introduced a unified risk metric to model supplier cost risk in their approach. By consolidating diverse risk factors into a single metric, they simplified the decision-making process and enhanced the efficiency of their optimization model.  
While the deterministic models adopted in these studies provide valuable insights, they must be considered limited by the assumption that the exact future value of risks is known in advance. As this assumption is not always valid, researchers have thus started to design stochastic formulations of the multiperiod SSOA problem to capture the inherent uncertainty associated with risk factors. In these formulations, different risk scenarios are formulated, and a specific probability of occurrence is usually associated with each scenario. For example, ref. [28] presented a formulation considering supplier local disruption risk, while ref. [29] extended this approach to consider stochastic supplier local disruption and capacity risks alongside deterministic quality risks. Lastly, ref. [30] incorporated stochastic supplier costs and demand risks into their formulation.
## 3 Performance Analysis of NOMA-Enabled HSTRNs  
In this section, we provide details of the step-by-step derivations of closed-form expressions of outage probabilities and ergodic capacities. Prior to that it is important to understand the different parameters of the wireless channel. The details of the channel models are given below:
## 4 Results and Analysis of Spot Area Detection
### Characteristics of the Spot Area in Relation to Gain and Observation Distance Variations
Similarly to the process in Equations (3)-(6), the correction of the actual gain for the spot size to the reference gain can be obtained, as shown in Equation (10). \[S_{0}=Se^{0.0265(g_{0}-g)} \tag{10}\]  
In the above equation, \(g\) is the actual gain, \(S\) is the spot area obtained from the actual observation, \(g_{0}\) is the reference gain, which is taken as 100% in this paper, and \(S_{0}\) is the corrected spot area. The formula to calibrate the spot size obtained at any detection distance and gain for a 5 mm conductor to the spot size at a gain of 100% and a detection distance of 2 m is as follows:  
\[S_{0}=S\cdot e^{0.0265(100-g)}\cdot e^{0.2504(L-2)}=S\cdot e^{2.1492-0.0265g+ 0.2504L} \tag{11}\]  
By combining Equations (8) and (11), the corresponding spot size at the reference detection distance and the reference gain for any detection distance and gain of the ultraviolet imaging device can be obtained, along with the corresponding voltage on the conductor. This allows for the evaluation of the discharge level of the conductor. To validate the correctness of this approach, using a UV imager, the UV photon count and spot area of a conductor with a diameter of 5 mm were measured at a detection distance of 7 m and a gain of 100. The voltage was then varied, and the measured UV photon counts and spot areas were obtained. These values were then corrected to obtain the reference photon counts and spot areas. The fitted voltage was determined by combining the reference photon counts and spot area with a voltage fitting function. From Table 7, it can be seen that with the correction method and voltage fitting function proposed in this paper, accurate observations of corona discharge can be obtained. The error of the fitted voltage and the actual voltage was within 5%. \begin{table}
\begin{tabular}{c c c} \hline
**Voltage (kV)** & **Fitted Function Expression** & **Fit Coefficient** \\ \hline
51.5 & \(S(g)=147.35e^{0.0279}\) & 0.9571 \\
52.5 & \(S(g)=327.88e^{0.0263}\)g & 0.9353 \\
53.5 & \(S(g)=458.77e^{0.0250}\)g & 0.9737 \\
54.5 & \(S(g)=813.59e^{0.0267}g\) & 0.9631 \\ \hline \end{tabular}
\end{table} 
Table 6: Fitting relationship expression of discharge area with gain at different voltages. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Discharge** & **Actual Photon** & **Corrected** & **Actual Spot** & **Corrected Spot** & **Actual Voltage** & **Fitted Voltage** \\
**Area** & **Count** & **Photon Count** & **Area** & **Area** & **(kV)** & **(kV)** \\ \hline
1 & 4903 & 19,882 & 3732 & 13,052 & 52.45 & 55.00 \\
2 & 3193 & 12,948 & 2559 & 8949 & 51.65 & 54.10 \\
3 & 1962 & 7956 & 1773 & 6201 & 51.84 & 53.30 \\ \hline \end{tabular}
\end{table} 
Table 7: UV imaging detection parameters and correction parameters under different voltages.
## 4 Experimental Datasets and Results
### Computational Cost  
Here, we first enlist the computational time for an MLR-LORSAL classifier for each Query Function used in this work. One can observe that the computational time is gradually increasing as the number of training samples increases as shown in Figure 1, however, the trend is quite different in accuracy which increases exponentially. Prior to the experiments, we performed the necessary normalization between [0, 1] and all the experiments were carried out on cluster using MATLAB (2017a) on Intel Core (TM) i7-7700K CPU 2.40 GHz, 1962 MHz, Ubuntu 16.01.5 LTS, CUDA completion tools, release 7.5, V7.5.17 with 65 GB RAM.
# Health State Assessment of Road Tunnel Based on Improved Extension Cloud Model
## 2 Construction of Indicator System
### Calculation of Indicator Values  
Basic indicators for evaluating the health status of road tunnels cannot be directly determined, such as changes in tunnel crack length, width, and depth trends. A significant waste of maintenance resources would occur due to the requirement of a substantial amount of manpower, material resources, and financial resources for long-term monitoring of each tunnel structure [31]. From another perspective, the health status of the tunnel can be reflected by the number of new cracks. Unstable stress and deformation of the tunnel surrounding rock and lining structure will cause the cracks to expand in depth or length and lead to the emergence of new cracks [32]. Therefore, pavement technical condition indicators including pavement condition index (PCI), riding quality index (RQI), and skid resistance index (SRI), are also evaluated to determine the static indicator values based on the historical inspection data of road tunnels.  
Figure 1: Framework for the construction of the tunnel health status evaluation indicator system.  
Figure 2: Tunnel health status evaluation indicator system.  
The dynamic indicator values are obtained by fitting the scattered detection points into a smooth function curve and derivation. From a derivative perspective, the differences in health change patterns between different tunnels are taken into consideration. Common curve fitting methods include the Lagrange interpolation method, least squares fitting, Bezier curve fitting method, etc. The above methods are unsuitable for fitting the characteristics of tunnel health conditions as the process of fitting the health trend of a tunnel is complex and takes into account multiple factors. For example, the overall trend can be reflected by the least squares fitting method, but the changing trends of a specific control point (detection time) cannot be revealed. As the lifespan of tunnels increases, there will be more control points. As the number of control points increases, it becomes challenging to control the Lagrange interpolation method and Bezier curve fitting method, leading to a considerable deviation. However, the advantages of the Bezier curve fitting method are inherited by the Cubic b-spline while solving its limitations. Moving a control point does not alter the overall shape of the curve. Instead, it only affects the shape of the two near curves. Therefore, Cubic b-spline fitting is suitable for the characteristics of tunnel health condition fitting. Not only can the overall trend of the data be reflected by Cubic b-spline fitting, but the local curve can also be controlled relatively flexibly. Based on this, this paper uses the Cubic b-spline smoothing method to fit the function (Equation (1)).  
\[B(t)=\sum_{i=0}^{n}P_{i}N_{i,k}(t),0\leq t\leq 1 \tag{1}\]  
where \(P_{i}\) is the position vector of each vertex, and \(N_{i,k}(t)\) is the Bernstein basis function, also known as the Cubic b-spline basis function which the order is \(k\) and the degree is \(k-1\).
## 3 Experimental Work
### Sample with Copper Spiral  
When measuring the sample with PCM, a copper spiral was added to the sample. The copper spiral had a mass of 21 g. The heat capacity of the copper spiral in these measurements was not considered. The melting of pure PCM with the copper spiral is graphically presented in Figure 14. The thermocouple was placed in the core of the sample. In the first part of the graph, the curve of sensible heat (State A) is presented, which lasts for 1430 s, and the second part of the graph starts at a temperature of 48.2 \({}^{\circ}\)C with the phase change transition (melting of PCM in the core) or the acceptance of energy in the form of latent heat (image record State B). The end of the phase change occurs after the core melts at 52 \({}^{\circ}\)C at a time of 1630 s. This means that the whole phase change process took 200 s. The latent energy received was 10,800 J.  
The Figure 15 shows the curve of latent heat transfer to the heat transfer medium over time. The solidification process of the PCM starts at a time of 365 s and a temperature of 53.5 \({}^{\circ}\)C. The temperature of the heat transfer medium at this point is 31.0 \({}^{\circ}\)C. The phase  
Figure 14: Charging PCM–copper spiral sample.  
change ends at a temperature of 51.00 \({}^{\circ}\)C at a time of 2660 s. The heat transfer medium was heated to a temperature of 35.1 \({}^{\circ}\)C. In total, the phase change lasted 2295 s. The latent heat portion lasts 1555 s and the mixed-phase portion lasts 740 s, which means that the latent heat portion accounts for 67.75% of the total phase change time. During this section, the PCM heated the heat transfer medium from 31.0 \({}^{\circ}\)C to 34.7 \({}^{\circ}\)C, i.e., \(\Delta\)t = 3.7 \({}^{\circ}\)C. This can be calculated as follows:  
\[Q\ =\ m\cdot c\cdot\Delta\mathrm{t}\ =\ 0.4\cdot 4180\cdot 3.7\ =\ 6186.4\ \mathrm{J} \tag{25}\]  
The PCM supplied 6186.4 J to the heat transfer medium during the phase change in the latent heat part, 6186.4 J. During the mixed phase, the sample heated the heat transfer medium to a final temperature of 35.1 \({}^{\circ}\)C. In total, the two phases together heated the medium by 4.1 \({}^{\circ}\)C:  
\[Q\ =\ m\cdot c\cdot\Delta\mathrm{t}\ =\ 0.4\cdot 4180\cdot 4.1\ =\ 6885.2\ \mathrm{J} \tag{26}\]  
The total energy transferred was 6855.2 J.  
Two thermocouples were used for measurements in the experimental setup--one for measuring the temperature of the heat transfer medium and the other for measuring the phase change of the sample located in the core. Therefore, the measured state may be different from the real state due to the uneven solidification of the PCM both on the surface and in the core.