esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 5 Results and Discussions
### Mining Design Applications  
The spider diagram in Figure 15 illustrates the distinct strengths and adaptability of the ant, firefly, and honeybee swarm models in various mining approaches. The ant model, characterized by its task-allocation feature and decentralized control, may be highly reliable in operations where precise targeting is unnecessary, such as placer mining, dredging, and block caring. It may optimize task distribution in hydraulic mining and may be effectively applied in space exploration, including asteroid, planetary, and lunar mining, by enhancing mining efficiency through independent task execution. The firefly model excels in scalability and decentralized control and may be suitable for surface mining methods like open pit and strip mining, where accurate ore positioning is crucial. In underground mining methods such as room-and-pillar, longwall, and stoping, the firefly model's ore-detection feature may allow for highly targeted mining with high confidence. It may also be effective in space mining, where the model's ability to operate in harsh conditions with communication challenges becomes advantageous. Lastly, the honeybee model, with its centralized control strategies, may be highly efficient in targeting high-grade ores in both surface and underground mining methods, such as room-and-pillar, longwall, shrinkage stoping, and sublevel stoping. Its advanced communication system may facilitate efficient coordination in selective mining and may be particularly beneficial for specialized techniques like in situ leaching and solution mining, where focusing on high-grade ore areas is critical for maximizing efficiency and profitability. The detailed applications of these models across various mining methods are summarized in Table 4.
## 3 Results
### Principal Component Analysis (PCA)
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Compounds (mg/L)**} & \multicolumn{4}{c}{**Treatments (Mean \(\pm\) SD)**} & \multirow{2}{*}{**Sig.**} \\ \cline{2-2} \cline{4-4}  & **Control** & & & **Elderflower** & **Rosemary** & **Thyme** \\ \hline Hydroxycinnamic acids & & & & & \\ Caffeic acid & 2.46 \(\pm\) 0.07 \({}^{\rm d}\) & 25.22 \(\pm\) 0.41 \({}^{\rm a}\) & 11.13 \(\pm\) 0.16 \({}^{\rm c}\) & 13.79 \(\pm\) 0.13 \({}^{\rm b}\) & *** \\ Chlorogenic acid & n.d. & 219.10 \(\pm\) 3.90 \({}^{\rm a}\) & 0.36 \(\pm\) 0.04 \({}^{\rm b}\) & 0.26 \(\pm\) 0.03 \({}^{\rm b}\) & *** \\ Ferulic acid & 0.47 \(\pm\) 0.01 \({}^{\rm d}\) & 1.13 \(\pm\) 0.03 \({}^{\rm a}\) & 0.52 \(\pm\) 0.02 \({}^{\rm a}\) & 0.60 \(\pm\) 0.001 \({}^{\rm b}\) & *** \\ Isoferulic acid & 0.18 \(\pm\) 0.01 & 0.25 \(\pm\) 0.03 & 0.19 \(\pm\) 0.04 & 0.20 \(\pm\) 0.02 & n.s. \\ Neochlorogenic acid & 0.17 \(\pm\) 0.00 \({}^{\rm c}\) & 24.40 \(\pm\) 0.28 \({}^{\rm a}\) & 0.50 \(\pm\) 0.01 \({}^{\rm b}\) & 0.18 \(\pm\) 0.00 \({}^{\rm c}\) & *** \\ \(p\)-Coumaric acid & 0.35 \(\pm\) 0.07 \({}^{\rm d}\) & 1.02 \(\pm\) 0.04 \({}^{\rm a}\) & 0.79 \(\pm\) 0.03 \({}^{\rm b}\) & 0.64 \(\pm\) 0.03 \({}^{\rm c}\) & *** \\ Sinapic acid & 0.04 \(\pm\) 0.00 \({}^{\rm c}\) & 0.12 \(\pm\) 0.00 \({}^{\rm a}\) & 0.06 \(\pm\) 0.01 \({}^{\rm b}\) & 0.05 \(\pm\) 0.00 \({}^{\rm c}\) & *** \\ Verbascide & 0.01 \(\pm\) 0.01 \({}^{\rm b}\) & 0.02 \(\pm\) 0.01 \({}^{\rm b}\) & 0.08 \(\pm\) 0.04 \({}^{\rm a}\) & 0.02 \(\pm\) 0.01 \({}^{\rm b}\) & *** \\ \(\Sigma\) hydroxycinnamic acids & 3.69 \(\pm\) 0.12 \({}^{\rm c}\) & 271.258 \(\pm\) 4.13 \({}^{\rm a}\) & 13.63 \(\pm\) 0.22 \({}^{\rm b}\) & 15.74 \(\pm\) 0.11 \({}^{\rm b}\) & *** \\ \hline Hydroxyphenylpropanoic acids & & & & & \\ Dihydro-\(p\)-comamic acid & 0.39 \(\pm\) 0.15 & 0.40 \(\pm\) 0.08 & 0.45 \(\pm\) 0.20 & 0.39 \(\pm\) 0.16 & n.s. \\ \hline Other & & & & & \\ Polydatin & 0.04 \(\pm\) 0.02 & 0.02 \(\pm\) 0.01 & 0.03 \(\pm\) 0.01 & 0.03 \(\pm\) 0.00 & n.s. \\ Vanillin & 0.04 \(\pm\) 0.01 \({}^{\rm c}\) & 0.16 \(\pm\) 0.02 \({}^{\rm a}\) & 0.08 \(\pm\) 0.01 \({}^{\rm b}\) & 0.08 \(\pm\) 0.00 \({}^{\rm b}\) & *** \\ Hydroxyproxyl & 0.15 \(\pm\) 0.00 \({}^{\rm c}\) & 0.23 \(\pm\) 0.00 \({}^{\rm a}\) & 0.18 \(\pm\) 0.01 \({}^{\rm b}\) & 0.19 \(\pm\) 0.00 \({}^{\rm b}\) & *** \\ \(\Sigma\) other & 0.23 \({}^{\rm c}\) & 0.40 \({}^{\rm a}\) & 0.29 \({}^{\rm b}\) \({}^{\rm b}\) & 0.30 \({}^{\rm b}\) & *** \\ \hline \hline \end{tabular}  
* Means \(\pm\) SD with different superscript letters (a, b, c, d) for each compound in the same row differ significantly (\(p\leq\) 0.05); n.s. non-significant; **. \(p\leq\) 0.01; ***. \(p\leq\) 0.001; n.d. not detected.  
\end{table} 
Table 2: _Cont._
## 3 Results and Discussions
### Wettability Behavior
The synergic effect of the micro/nano-structure and the symmetric and regular distribution of the peaks and valleys on the A_F surface (Sku \(\approx\) 0) induced the formation of a uniform silane monolayer, resulting in a very high contact angle (180\({}^{\circ}\)) and great water repellency (SA \(\approx\) 0\({}^{\circ}\)).
## 3 Results and Discussion
### Discussion
#### 3.4.2 The Influence of Crack Dip Angle on Grouting Reinforcement Body  
This study also analyzes the influence of different crack dip angles on the performance of sandstone grouting reinforcement. The results show that when the crack dip angle is \(0^{\circ}\), \(30^{\circ}\), or \(45^{\circ}\), the grouting reinforcement has obvious plastic deformation and ductility after the peak strength. When the fracture dip angle is \(60^{\circ}\), the stress-strain relationship curve has no plastic deformation stage regardless of the confining pressure, and it decreases instantaneously after the axial stress reaches the peak value. The main reason is that the stress reaches the shear strength of the weak surface of the rock-slurry consolidation body, resulting in shear slip failure of the rock along the fracture surface. With the increase of crack dip angle, the failure mode of the grouting reinforcement body gradually changes from axial splitting failure to axial splitting-shear mixed failure, and finally to shear failure. This is relatively consistent with the research results of [PERSON] et al. [22]. The change in this failure mode further illustrates the importance of crack dip angle to the stability of the grouting reinforcement body.
# Stereo Digital Image Correlation in MATLAB
## 2 Framework Theory
### Correlation
#### 2.3.5 Optimization Equation  
The optimization equation derived from Equation (17), derivation detailed in [53], is given as  
\[\Delta\mathbf{P}=-\mathbf{H}^{-1}\sum_{i=1}^{l}\biggl{(}\
abla f_{i}\frac{\partial W_{i} }{\partial\mathbf{P}}\biggr{)}^{T}\Biggl{[}f_{i}-\overline{f}-\frac{\widetilde{f}} {\widetilde{g}}(G(\mathbf{x}^{\rho}+\mathbf{W}(\Delta\mathbf{x}_{i},\mathbf{P}))-\overline{g}) \Biggr{]}. \tag{18}\]  
Here \(\mathbf{H}\) is the Hessian, defined in Equation (19), and the remaining terms (within the summation) form the Jacobian, \(\mathbf{J}\). \(\mathbf{H}\), being independent of \(\mathbf{P}\), is precomputed prior to beginning iterations.  
\[\mathbf{H}=\sum_{i=1}^{l}\Biggl{[}\biggl{(}\
abla f_{i}\frac{\partial\mathbf{W}_{i}}{ \partial\mathbf{P}}\biggr{)}^{T}\biggl{(}\
abla f_{i}\frac{\partial\mathbf{W}_{i}}{ \partial\mathbf{P}}\biggr{)}\Biggr{]}. \tag{19}\]  
\(\
abla f_{i}=\left[\begin{array}{cc}\frac{\partial f_{i}}{\partial\mathbf{x}}& \frac{\partial f_{i}}{\partial\mathbf{y}}\\ \end{array}\right]\) is the light intensity gradient of \(\mathbf{f}\) at pixel \(i\). \(\frac{\partial\mathbf{W}_{i}}{\partial\mathbf{P}}\) is the Jacobian of the SF for pixel \(i\) and based on the SF order is given as  
\[\frac{\partial\mathbf{W}_{i}^{SF1}}{\partial\mathbf{P}^{SF1}}=\left[\begin{array}{ cccc}1&0\\ 0&1\\ \end{array}\right], \tag{20}\]  
\(\frac{\partial\mathbf{W}_{i}^{SF2}}{\partial\mathbf{P}^{SF2}}=\left[\begin{array}{ cccc}1&\Delta x_{i}&\Delta y_{i}&0&0&0\\ 0&0&0&1&\Delta x_{i}&\Delta y_{i}\\ \end{array}\right]\)  
\(\frac{\partial\mathbf{W}_{i}^{SF2}}{\partial\mathbf{P}^{SF2}}=\left[\begin{array}{ cccc}1&\Delta x_{i}&\Delta y_{i}&\frac{\Delta x_{i}^{2}}{2}&\Delta x_{i} \Delta y_{i}&\frac{\Delta y_{i}^{2}}{2}&0&0&0&0&0\\ 0&0&0&0&0&1&\Delta x_{i}&\Delta y_{i}&\frac{\Delta x_{i}^{2}}{2}&\Delta x_{i} \Delta y_{i}&\frac{\Delta y_{i}^{2}}{2}\\ \end{array}\right]\).  
Although, each iteration of Equation (18) attempts to determine how \(\mathbf{f}\) should displace and deform (according to \(\Delta\mathbf{P}\)) such that it matches \(\mathbf{g},\mathbf{f}\) is not displaced or deformed in practice. Instead \(\Delta\mathbf{P}\) is used to determine the updated SFPs, \(\mathbf{P}_{update}\), which serves as the current estimate in the next iteration. The stopping criterion deems when further iterations are redundant.
# Energy-Efficient Hybrid Routing Protocol for IoT Communication Systems in 5G and Beyond
## 3 Problem Formulation and Existing Challenges
### System Model and Assumptions  
We assume a wireless network of \(N\) number of IoT smart sensors which can be distributed evenly over \(M\times M\) square region to periodically observe the environment. Let \(s_{i}\) is the \(i^{th}\) sensor then the corresponding set of sensor nodes is given by \(S=\{s_{1},s_{2},...,s_{n}\}\,|\,\begin{subarray}{c}|S|=N\\ n=1\end{subarray}\).  
Our proposed system model is based on the following eight assumptions.  
1. All wireless sensor nodes support MIMO.
2. Each wireless sensor node sends only small volume of data. However, the Cluster Head can send big volume of data.
3. All sensor nodes have unequal capabilities (communication, processing, and battery)
4. Energy is limited (no continuous power supply).
5. All sensor nodes are not provided with a GPS-capable device (i.e., they are assumed as location-unaware).
6. The nodes can control their transmission power level depending on the distance to the receiver.
7. Nodes are assumed to be stationary.
8. Each IoT device has various options on communication technologies for data transfer symbolized as \(NAI_{i}\) (see Section 4.4).  
A similar wireless transmission/reception model is adopted as described in [12]. In this case, the wireless system diffuses energy to render the transmitter and amplifier of the transmitter. In addition, the wireless system emits energy for operating of the receiving circuit. Also, the power loss (\(d^{2}\)) under the free space model and the power loss (\(d^{4}\)) model under the multi-path fading have been used, where the energy loss is dependent on the distance between the transmitting end and the receiving end to power the transmitter. The power consumed to convey an \(l\)-bit message for a distance \(d\) is calculated in this radio model as:  
Transmitter side:  
\(E_{TX}\)(1, d) = \(E_{TX-{\it{elec}}}\)(1) + \(E_{TX-{\it{amp}}}\)(1, d)  
\[=\begin{cases}|I_{{\it{elec}}}\ +\ E_{{\it{efs}}}d^{2},{\sf d}\ <\ d_{0}\\ |I_{{\it{elec}}}\ +\ E_{{\it{emp}}}d^{4},d\geq\ d_{0}\end{cases} \tag{1}\]  
Receiver side:  
\[E_{RX}\ =\ E_{RX-{\it{elec}}}\)(1) = \(lE_{{\it{elec}}} \tag{2}\]
# Respiration Symptoms Monitoring in Body Area Networks
## 5 Overview of the Proposed Method  
The system architecture designed to monitor the breathing of patient suffering from hyperhyreosis, sleep apnea and SIDS diseases consist of two main components as shown in Figure 3. Identification of breathing pattern using a wearable digital respiratory sensor, and breathing pattern monitoring using S-Band sensing technique leveraging wireless channel information. From raw WCI data, the amplitude information is extracted which is examined against the time history using an individual frequency. The variances of amplitude information are then passed through a filtering process, using wavelet filter. The variances of amplitude information are then analyzed and compared with the data collected with a wearable sensor to see if they can be attributed to the normal breathing or abnormal breathing.  
Figure 2: The raw wireless channel information (WCI) amplitude information received for breathing pattern detection.
## 3 Results and Discussion
### Effect of Coexisting Anions on Fluoride Removal  
Discharged wastewater contains competing anions, including PO4\({}^{3-}\), CO3\({}^{2-}\), SO4\({}^{2-}\), NO3\({}^{-}\), and Cl\({}^{-}\), which are known to interfere with fluoride adsorption [43]. Figure 3c illustrates the impact of these coexisting anions on WA-based fluoride removal at 1 and 10 mM. An increase in the concentration of PO4\({}^{3-}\), a trivalent ion, from 0 to 10 mM resulted in a significant (41.0%) decrease (\(p\) < 0.05) in fluoride adsorption capacity, from 118.59 mg/g to 70.00 mg/g. Conversely, the monovalent ions NO3\({}^{-}\) and Cl\({}^{-}\) had the least inhibitory effect on fluoride adsorption. An increase in Cl\({}^{-}\) concentration from 0 to 10 mM resulted in only a negligible (1.9%) decrease in adsorption capacity, from 118.59 mg/g to 116.37 mg/g. The coexisting anion-induced inhibition of fluoride removal by WA was found to be in the order PO4\({}^{3-}\) > CO3\({}^{2-}\) > SO4\({}^{2-}\) > NO3\({}^{-}\)\(\approx\) Cl\({}^{-}\). Trivalent ions have a greater inhibitory effect on fluoride adsorption than do monovalent ions, as observed in other studies [12; 43]. The pronounced inhibitory effect of PO4\({}^{3-}\) on fluoride adsorption arises from its occupancy of more active sites than those occupied by fluoride and its ability to form stronger bonds due to the formation of an inner-sphere complex [3]. Meanwhile, WA can absorb PO4\({}^{3-}\) by reacting with calcium to form Ca5(PO4)3 OH [31], suggesting a potential strategy for treating both fluoride and phosphate simultaneously in real wastewater systems. CO3\({}^{2-}\) was found to have a pH > 11.5 after the adsorption reaction at concentrations of both 1 and 10 mM. Under high pH conditions, carbonate exists in the form of CO3\({}^{2-}\) in the fluoride solution, and the electrostatic repulsion between OH\({}^{-}\) and CO3\({}^{2-}\) notably affects fluoride removal [53]. SO4\({}^{2-}\) is an anionic ligand, and because it forms both an inner- and outer-sphere complex at the surface active site, it suppresses fluoride adsorption onto WA [54]. NO3\({}^{-}\) and Cl\({}^{-}\) do not interfere with fluoride occupying the active adsorption sites on WA because they are low-affinity ligands that produce weak outer-sphere surface complexes [37]. Therefore, NO3\({}^{-}\) and Cl\({}^{-}\) have a less significant influence (\(p\) < 0.05) on WA-based fluoride removal.
# Green Hydrogen Production--Fidelity in Simulation Models for Technical-Economic Analysis
## 2 System Component Modeling
### Electrolyzer Model  
Electrolyzers can be modeled using various approaches. In this case, the chemical-electrical approach is adopted. The electrolyzer block represents the electrical load of the electrolyzer, which consists of an anode and a cathode separated by an electrolyte. This block calculates the hydrogen production based on the electrical energy input and the water temperature in the tank [30]. The electrolyzer block calculates the electrical power using Equation (4):  
\[P_{E}=\frac{\
u^{2}}{R_{T}}\to P_{t}^{ETZ} \tag{4}\]  
where \(\
u\) is the voltage and \(R_{T}\) is the total resistance given by Equation (5). The \(R_{T}\), is defined by  
\[\frac{1}{R_{T}}=\frac{1}{R_{el}}+\frac{1}{R} \tag{5}\]  
\[R_{el}=\frac{\rho(pH)\times x}{A}\]  
where \(R\) is the external resistance, \(A\) is a resistive term obtained from the cross-sectional transport area, \(x\) is the anode-cathode distance, and \(\rho(pH)\) is the electrical resistivity of the solution. The energy per mole stored over the electrical load facilitates water electrolysis and can be computed using Equation (6):  
\[G_{E}=\frac{P_{E}}{\frac{1\bmod}{\mathrm{s}}} \tag{6}\]  
In performing electrolysis, the electrolyzer needs a minimum level of energy according to Equation (7):  
\[G_{min}=\Delta H-\frac{T}{T_{o}}T_{\Delta s} \tag{7}\]  
where \(\Delta H\) is the water enthalpy, \(T_{\Delta s}\) is the reaction entropy, \(T\) is the water temperature, and \(T_{o}\) is the reference temperature.  
If there is not enough electrical energy to split the water molecules, the electrolyzer will not generate hydrogen. Consequently, the electrolyzer block determines Equations (8) and (9), the rates of electron, mole, and hydrogen production, respectively.  
\[mol_{e}=\left\{\begin{array}{ll}\frac{\mu(G_{E}-G_{min})}{\
u\
u N_{A}}&G_{e }>G_{min}\\ 0&G_{e}\leq G_{min}\end{array}\right. \tag{8}\]  
\[mol_{H_{2}}=\left\{\begin{array}{ll}\frac{N_{e}mole}{2}&G_{e}>G_{min}\\ 0&G_{e}\leq G_{min}\end{array}\right. \tag{9}\]  
Figure 2: PV system model: (**a**) PV power, (**b**) I-V characteristics.  
where \(N_{A}\) is the Avogadro constant, \(e_{V}\) is the energy per electron, \(N_{c}\) is the number of cells, and \(\mu\) is the temperature-dependent efficiency of the electrolysis.  
The current passing through the electrolyzer tank, as given by Equation (10), is determined using the following expression, where \(F\) represents the Faraday constant (96,485.3321 s\(\cdot\)A/mol):  
\[i=mol_{e}\times F \tag{10}\]  
The electrolyzer block computes the mass rates of water consumption and hydrogen production using Equations (11) and (12), respectively:  
\[\dot{m}_{H_{2}O}=mol_{H_{2}}M_{H_{2}O} \tag{11}\]  
\[\dot{m}_{H_{2}}=mol_{H_{2}}M_{H_{2}} \tag{12}\]  
where \(M_{H_{2}O}\) is the molar mass of water, and \(M_{H_{2}}\) is the molar mass of hydrogen.
## 4 Discussion  
This study may be the first case report on changes in body composition following a short-term vegan diet of a professional vegan powerlifter. The results suggest that the phenomenon known as \"body recomposition\" could have occurred in an advanced athlete as it was previously reported by [PERSON] and colleagues [14]. A plausible explanation to this phenomenon could have been the improvement in meal planning compared to what the athlete ate before the intervention. On the other hand, a potential limitation to explain this would be the use of the measurement techniques. Further advantages provided by this nutritional plan were the following: high satiety despite a low energy intake and the combination of whole foods and supplements to improve the profile of the specific intake of amino acids [3]. On the contrary, several disadvantages may be also highlighted. These were the high amounts of soy protein supplement, which may reduce the variability of the nutritional plan and compromise adherence, and an insufficient intake of some micronutrients such as calcium, iron, zinc, and magnesium due to a low energy budget [15]. There is a certain relationship between the weight lost and performance via the improvement of the power-to-weight ratio, which is particularly desired in weight-class sports such as powerlifting. A decrease in BM could lead to lean body mass (LBM) or FFM loss. However, it has also been suggested that BM reductions throughout the season should be achieved in a slow fashion when the main goal is to preserve skeletal muscle mass [9]. It has been suggested that losing 0.7% of BM per week might be better than 1-1.4% of BM for athletes who would like to preserve or gain LBM and strength [16]. Nonetheless, it was also recently reported that a gradual weight loss is associated with greater loss in FM and body fat percentage, but it was not associated with changes in FFM in overweight and obese adults and older individuals [17]. Our results show that the athlete's mean weekly weight loss during the first four weeks of the intervention (0.47 kg/week) was consistent with results reported by some authors [9], while others recommended minor weekly weight losses in physique athletes (<0.5% of BM per week) [18]. It should be mentioned that these nutritional recommendations were obtained for bodybuilders, whose body fat percentage tends to be lower during the season. Moreover, the magnitude of the energy deficit [19], the rate of BM loss per week [20], nutritional considerations such as timing, protein intake, etc., and the body composition assessment methods employed could help explain FFM variations along the weight loss phase. Athletes involved in weight-class sports are advised to optimize protein intake since the preservation of FFM and relative strength is likely advantageous to performance [13]. There is a relationship between dietary protein intake, energy balance status, and the preservation of LBM. It should be acknowledged that the stimulation of muscle protein synthesis (MPS) is thought to be one of the most relevant physiological variables responsible for the maintenance of, or gains in, skeletal
## 2 Methods
### General Observation Models and OSB Concept
Because this bias is recombined into other parameters, the server algorithms for satellite clock offsets and the client PPP algorithms are strictly self-consistent. Hence, the carrier-phase time-variant OSB values at the basic frequency signals can be set to zero. The carrier-phase time-variant OSB values at the basic and the third frequency signals can be written as follows. \[\left\{ \begin{array}{l}\delta_{\phi_{i}}^{s}(t)=0\\ \delta_{\phi_{i}}^{s}(t)=0\\ \delta_{\phi_{k}}^{s}(t)=\alpha_{ij}\cdot\left(1-\mu_{k}/\mu_{j}\right)\cdot B _{i}^{s}(t)-\beta_{ij}\cdot(\mu_{k}-1)\cdot B_{j}^{s}(t)-B_{k}^{s}(t)\\ \end{array} \right. \tag{6}\]  
Equation (6) indicates that the carrier-phase time-variant OSB at different frequencies, and \(\mu_{i}\) and \(\mu_{k}\) denote the frequency-dependent multiplier factor.
## 5 Experiment Design
### Experimental Procedures  
The main test was conducted in the evening. The flowchart of operational procedures is presented in Figure 5. After an in-wall pipe was found to have severe leakage, the BARMS system tester needed to first shut down the water supply by positioning its valve either to or from the cooling tower and then shut down the corresponding power switch to the tower and controlling pump.  
This test comprises six related tasks:  
1. Retrieve leaking pipe-related information: the construction date and previous maintenance schedule were retrieved by taking advantage of existing BIM information;
2. Trace the corresponding location of the pipe on the other side of wall: tracking has to be performed among 20 possible pipes in order to identify the correct one. Not all of the 20 in-wall pipes are shown in the vertical shaft, i.e., some of the pipes are sealed in the walls. The tested pipe could be seen in the shaft relatively easier than the others. This constitutes one of the most difficult parts of the test, since the indoors and outdoors is separated by a wall, without any visual reference to the pipe outside. Counting the sequential layout of pipes is also impractical because some pipes are not visible in the shaft;
3. Previous research has identified a possible solution to the pipe identification problem by projecting the outline of an entity inside of a wall to the surface in order to show the correct location when observed from the outside [11]. Our study extended this concept to show the pipe on the opposite side of a space that is not directly connected. In addition, the entity-pinpointing task had to be performed for other system components, such as the power switch of an electrical system;
4. Locate the correct cooling tower to which the pipe is connected: there are two types of pipes--the cooling water supply pipe and the return pipe--which increases the complexity of accurate pipe identification;
5. Shut down the power to the cooling tower and the pump connected to the return pipe: power must be shut down before the pipe or the valve. Since the system was designed using the same switch for the tower and its related pump, both components will be powered off at the same time; and
6. Correctly shut down the valve connected to the return pipe.  
Figure 5: Sequence of operational procedures.