esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Time Required to Meet Government Standards  
The time required for gas extraction to meet government standards includes three parts: the time for permeability enhancement, the time for drilling extraction boreholes and the time for extraction, as shown in Table 1.  
In addition, if the gas concentration in the coal roadway exceeds the government standard, then work on the coal roadway should be put off for approximately 8 h. The excavation speed in the high gas concentration area is approximately half of the normal speed. The excavation times for the W2708S and W2708N coal roadways were 5.8 months and 6.1 months, respectively.  
In summary, the time required to meet government standards at W2708N was 4 months shorter than that at the W2708S working face, and the excavation speed at W2708N (158 m/month) was 16% faster than that at W2708S (136 m/month). The novel hydraulic technology mode shortened the time required to meet government standards and improved the coal roadway excavation speed. This mode will ensure that the working faces continue on schedule and help the coal mine reap more economic benefits.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Working Face**} & **Time for Permeability** & **Time for Drilling** & **Time for** & **Total Time** \\  & **Enhancement** & **Extraction Boreholes** & **Extraction** & **(months)** & **(months)** \\  & **(months)** & **(months)** & **(months)** & **(months)** \\ \hline W2708S & 1 & 3 & 10 & 14 \\ W2708N & 1 & 3 & 6 & 10 \\ \hline \hline \end{tabular} The time for permeability enhancement at the W2708S working face included drilling cross-measure fracturing boreholes (3 days), the hydraulic fracturing time (7 days) and the pressure holding time (20 days). The time for permeability enhancement in the W2708N working face included drilling cross-measure fracturing boreholes (2 days), the hydraulic fracturing time (5 days) and the pressure holding time (20 days). During the pressure holding time, HOST was simultaneously performed in the tectonic region.  
\end{table}
Table 1: Details of the time required to meet government standards.  
Figure 8: Gas concentration curves in the return airway.
## 5 Results and Discussions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**Logit Regression**} & \multicolumn{2}{c}{**Poisson Regression with Robust Standard Errors**} \\ \cline{2-5}  & **(a)** & **(b)** & **(c)** & **(d)** & **(e)** & **(f)** \\ \hline \multirow{2}{*}{_clar\_eq\_migr\_WER\_pers\_pal\_rerog_} & \multicolumn{2}{c}{2.495 ***} & \multicolumn{2}{c}{2.433 ***} & \multicolumn{2}{c}{2.381 ***} & \multicolumn{2}{c}{1.858 ***} & \multicolumn{2}{c}{1.798 ***} & \multicolumn{2}{c}{1.717 ***} \\  & (0.152) & (0.157) & (0.162) & (0.110) & (0.116) & (0.120) \\ \hline \multirow{2}{*}{_urban_} & \multicolumn{2}{c}{0.0917} & \multicolumn{2}{c}{0.0753} & \multicolumn{2}{c}{0.0664} & \multicolumn{2}{c}{0.0451} \\  & & (0.190) & (0.194) & (0.129) & (0.131) \\ \hline \multirow{2}{*}{_income\_lot_} & \multicolumn{2}{c}{-0.0196} & \multicolumn{2}{c}{-0.0448} & \multicolumn{2}{c}{-0.012} & \multicolumn{2}{c}{-0.0311} \\  & & (0.042) & (0.044) & (0.027) & (0.028) \\ \hline \multirow{2}{*}{_no\_of\_shl_} & \multicolumn{2}{c}{-0.103} & \multicolumn{2}{c}{-0.131 *} & \multicolumn{2}{c}{-0.067} & \multicolumn{2}{c}{-0.081 *} \\  & & (0.074) & (0.077) & (0.045) & (0.045) \\ \hline \multirow{2}{*}{_both\_p\_gone\_abrad_} & \multicolumn{2}{c}{0.633 ***} & \multicolumn{2}{c}{0.688 ***} & \multicolumn{2}{c}{0.391 ***} & \multicolumn{2}{c}{0.403 ***} \\  & & (0.224) & (0.230) & (0.140) & (0.138) \\ \hline \multirow{2}{*}{_only\_m\_gone\_abrad_} & \multicolumn{2}{c}{0.603 *} & \multicolumn{2}{c}{0.591 *} & \multicolumn{2}{c}{0.363 *} & \multicolumn{2}{c}{0.347*} \\  & & (0.319) & (0.325) & (0.189) & (0.196) \\ \hline \multirow{2}{*}{_only\_d\_gone\_abrad_} & \multicolumn{2}{c}{0.21} & \multicolumn{2}{c}{0.208} & \multicolumn{2}{c}{0.141} & \multicolumn{2}{c}{0.129} \\  & & (0.211) & (0.215) & (0.139) & (0.137) \\ \hline \multirow{2}{*}{_open_} & \multicolumn{2}{c}{0.141} & \multicolumn{2}{c}{0.0905} \\  & & (0.186) & & (0.127) \\ \hline \multirow{2}{*}{_inclinet\_to\_exact\_sci_} & \multicolumn{2}{c}{0.23} & \multicolumn{2}{c}{0.147} \\  & & (0.170) & & (0.107) \\ \hline \multirow{2}{*}{_high\_believe\_in\_Cad_} & \multicolumn{2}{c}{-0.515 ***} & \multicolumn{2}{c}{-0.316 ***} \\  & & (0.182) & & (0.118) \\ \hline \multirow{2}{*}{_secular_} & \multicolumn{2}{c}{-0.308 *} & \multicolumn{2}{c}{-0.208 *} \\  & & (0.181) & & (0.121) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Raw coefficients (Logit vs. Poisson) of the model based on responses from FSEGA, UBB (Cluj-Napoca).
# Analysis of Equivalent Flexural Stiffness of Steel-Concrete Composite Beams in Frame Structures
## 4 Validation
### Comparison with Existing Design Formulas  
First, for conducting a parameter analysis, numerous design cases were selected from practical engineering. As shown in Figure 8, the analysed examples consisted of 180 composite beams with different cross-sections, in which the span length, \(L\), ranges from 6 to 20 m. The effective slab width was calculated using the stiffness amplification coefficient, \(\alpha\), and the slab thickness ranged from 100 to 150 mm. The span-to-steel beam height ratio, \(L/h_{\text{s}}\), ranged from 15 to 25, and the steel beam flange width to height ratio, \(b_{\text{f}}/h_{\text{s}}\), ranged from 0.4 to 1.0. In addition, the steel beam flange thickness, \(t_{\text{f}}\), and the web thickness, \(t_{\text{w}}\), were also designed to satisfy the requirement of a compact section.  
Subsequently, the analysed cases were divided into two groups based on the constraint boundary at the beam ends. In one group, parameter \(K\) ranged from 4 to 10, which represents the composite beams designed with large spans and heavy loads. In the other group, it ranged from 12 to 36, which represents the common composite frame beams arranged with equal spans. The calculated results of the proposed formula in this paper were compared with those obtained from the existing method, including the recommended methods of ANSI/AASC, Eurocode 8 [36], and [PERSON] et al., as shown in Figure 9. Note that the related regulations in ANSI/AASC and Eurocode 8 are similar; thus, the results calculated by both methods are plotted as one. Compared with the theoretical methods of the equivalent flexural stiffness calculation, the formula proposed in this paper had a margin of error of less than 1%. In comparison, the average errors of the methods suggested by ANSI/AASC and [PERSON] et al. were more than 10%, which is because of ignoring the constraint boundary condition at the beam ends.  
Figure 8: Numerical examples of the composite frame beam.
# A Construction Method of Lower Limb Rehabilitation Robot with Remote Control System
## 2 Materials and Methods
### Telerehabilitation Interactive System  
#### 2.2.1 Framework of Telecrabilitation Robot Interaction System  
Rehabilitation robots need to be used by professionally trained doctors. If every patient relies on the doctor's operation and supervision, it will greatly increase the doctor's workload. In this paper, we design a web-based robot remote rehabilitation interactive control system. Using this system, the doctors can take care of multiple rehabilitation robots on the server-side and distribute the rehabilitation training tasks to different patients. Patients or their family members can click to confirm the rehabilitation training tasks on the rehabilitation robot side to complete the training settings. These significantly reduce the doctor's workloads. The content of the rehabilitation training task includes the number of rehabilitation robots, patient information, training range, training speed, and training time. Doctors can then simplify the operation of the rehabilitation robot by remotely setting the rehabilitation training parameters. Through the analysis of the functions and requirements of the lower extremity rehabilitation robot system, we then adopt a network-based client/server (C/S) architecture to form a remote control system. Figure 4 describes the framework of the lower extremity rehabilitation robot remote interaction system. The telecrabilitation interactive control system consists of four components: a doctor server computer, a rehabilitation robot client computer, a lower limb rehabilitation robot, and a computer network.  
The server-side computer of the doctor is convenient for the doctor to guide the patient's rehabilitation training and manage the patient's rehabilitation log. It can distribute rehabilitation training tasks to rehabilitation robots. The doctor's server computer also displays the patient's rehabilitation data in real-time and helps the doctor to understand the patient's rehabilitation training status. The rehabilitation robot is controlled by the client computer at the rehabilitation site. It mainly completes the motion control of the rehabilitation robot and accepts the server-side rehabilitation training tasks, realizes the analysis of the rehabilitation task commands, and sets various parameters of the rehabilitation robot. The network acts as a bridge to transmit the information between the server and the rehabilitation robot client. The rehabilitation robot saves training data in the client's local database. Doctors and patients' family members can also access the local database through the network to view the training data of the patients.
## 4 Performance Analysis
### Complexity Analysis  
For cluster merging procedure, \(E[N_{hop}^{(ch_{i},ch_{j})}]\) rounds of local flooding is applied to achieve an inter-cluster communication between two clusterheads. Note that \(E[N_{hop}^{(ch_{i},ch_{j})}]\) represents the average number of hops between the two clusterheads involved in the merge operation. Then, 2 rounds local flooding are applied within a cluster to unify the cluster ID information in the merged cluster. Hence, the time complexity of is \(\mathcal{O}(E[N_{hop}^{(ch_{i},ch_{j})}]+2)\) rounds. For cluster splitting procedure, since the split cluster may apply the SAMCA to maintain the network operation, the proposed scheme has the same time complexity as described in Section. Based on the energy consumption analysis of cluster reformation above, both the cluster merging process and the splitting process have a communication complexity of \(\mathcal{O}(n)\). Thus, the communication complexity due to cluster reformation is \(\mathcal{O}(n)\).
## 3 Storage Tank Performance Analysis
### Volume Utilization Rate Analysis  
The higher the volume utilization ratio of the tank, the more propellant it can carry. It can prolong the life of the propulsion system and improve the control capability of the altitude and orbit of the satellite [41]. The volume utilization rate refers to the ratio of volume of the inner liquid to the volume of the envelope. The volume utilization ratios of the four storage tanks are denoted as \(v_{\text{C}}\), \(v_{R}\), \(v_{r}\), and \(v_{S}\), and they can be expressed as:  
\[v_{\text{C}}=\iint\limits_{D_{1}}f_{\text{C}}dxdy/xyz \tag{2}\]  
\[v_{R}=(x-2T)(y-2T)(z-2T)(z-2T)/xyz \tag{3}\]  
\[v_{r}=\left\{(x-2T)(y-2T)(z-2T)-d^{2}\big{[}(x-2T)n_{y}n_{z}+(y-2T)n_{x}n_{z}+( z-2T)n_{x}n_{y}\big{]}-2n_{x}n_{y}\big{[}\iint\limits_{D_{2}}f_{\text{C}}dxdy \right\}/xyz \tag{4}\]  
\[v_{S}=\left\{(x-2T)(y-2T)(z-2T)-d^{2}\big{[}(x-2T)n_{y}n_{z}+(y-2T)n_{x}n_{z}+( z-2T)n_{x}n_{y}-2 dn_{x}n_{y}n_{z}\big{]}\right\}/xyz \tag{5}\]  
where \(v_{\text{C}}\) is the volume utilization ratio of the conventional storage tank, \(v_{R}\) is the volume utilization ratio of the improved storage tank with a circular column structure, and \(v_{S}\) is the volume utilization ratio of the improved storage tank with a square column structure. \(f_{\text{C}}\) is the functional expression when the propellant forms an enclosed area \(D_{1}\) in the conventional storage tank. \(f_{r}\) is the functional expression when three cylindrical structures form a closed area \(D_{2}\) at the intersection.  
The calculation results of the propellant volume and volume utilization rates of the four kinds of storage tanks are presented in Table 4. The propellant volume and volume utilization rate of the conventional storage tank are lower than those of other types of tanks; however, there is considerable room for improvement of the conventional storage tank for carrying the propellant.
# Performance Evaluation of TCP BBRv3 in Networks with Multiple Round Trip Times
## 5 Performance Evaluation
### BBR Intended Behavior  
The intended behavior is observed through the transmission of a single stream. To achieve this objective, this study conducted two experiments, with each entailing the establishment of a single connection between node N1 and node N4. In both experiments,  
Figure 4: The dumbbell topology  
access link delays were fixed at 2 ms, and the throughput was set at 40 Mbps, while the bottleneck exhibited a delay of 10 ms and a throughput of 10 Mbps. The buffer size was set to compensate 2*BDP of traffic. The data transmission spanned 200 s.  
For the first experiment, the network conditions remained constant. The throughput trends over time are depicted in Figure 5. All versions of the BBR protocol are able to fully utilize the bottleneck link's capacity. A periodic reduction in throughput, occurring every 10 s, is attributed to the PROBE RTT phase. Versions 2 and 3 demonstrate significantly smaller throughput reductions, thus achieved by halving the window size relative to the current inflight.  
In the subsequent scenario, the bottleneck bandwidth capacity alternated every 40 s, thus fluctuating between increments to 20 Mbps and decrements to 10 Mbps. Figure 6 illustrates the amount of inflight data spanning from the 75 th to the 130 th second, thus capturing the interval of bandwidth reduction from 20 Mbps to 10 Mbps at the 80 th second and the subsequent bandwidth increase at the 120 th second. Each version promptly adjusted its estimations to the altered conditions. The simulation results for BBRv1 mirror the findings outlined in [9].
## 3 Results
### Confusion Matrix  
Based on the results of the performance metrics, the model's performance can be visualized in confusion matrices. In particular, the confusion matrix is a table that displays the aforementioned values in such a way that one can easily view the number of properly classified examples, as well as false positives and false negatives. In this analysis, a multiclass classification, the confusion matrix is of size 6 \(\times\) 6, where six is the number of activities that are predicted, as seen in Table 5.
## 3 Energy Efficient Routing
### Principles
#### 3.1.3 Routing Algorithm for EOLSR  
Our idea to maximize network lifetime, is to minimize the energy consumed by the end-to-end transmission of a flow in selecting the path, build from EMPRs, consuming the minimum energy. For that, we will use the cost given in the Equation 2 to compute routes for flows. Our target is to keep the complexity of Dijkstra (the same complexity as OLSR). However, instead of using the number of hops between source and destination to select the shortest route, as done in \(OLSR\), we will use \(cost(flow)\) as the criterion to choose the best path where: every link \(i\to j\) has a cost equal to \(cost_{transmission}(i)\) computed according to Equation 1. Network nodes are assumed to be in the idle state when the medium is idle, like in IEEE 802.11. In this case, the energy dissipated in receiving + overhearing + interferences takes into account the number of non-sleeping nodes in the 1-hop and 2-hop neighborhood of each sender of the considered flow as follows.  
\[cost(flow)=\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\  
A node forwards once a broadcast message with a non-null time-to-live only if it has received this message for the first time from a node that has selected it as MPR.
## 3 Image Feature-Based Extraction of Damage Indicators
### Indicator Convergence  
In most cases, bridges are not destroyed in a single situation, but often multiple situations coexist, so after extracting the indicators of residual bearing capacity and the number of passing lanes, a coupled analysis is needed to calculate a comprehensive destruction result.  
1. Residual bearing capacity (\(RBC\)). According to the residual bearing capacity index extraction method proposed in Section 3.1, the residual bearing capacity grade of the girder bridge can be obtained, and the value range of the result is [0, 10], and the larger the value is, the lower the residual bearing capacity of the bridge is;
2. Number of lanes (\(OP\)). According to the through-lane detection algorithm proposed in Section 3.2, the number of parallel lanes of the bridge can be obtained as \(OP\). The value is an integer starting from 0, and the maximum value is the downward rounding value of the bridge width divided by the vehicle width result. For bridges of different widths, the \(OP\) value has a different value range, so in order to ensure the consistency of the damage indicators, the number of lanes, \(OP\), is converted to a value in the range of 0-10, and the larger the value, the lower the number of through-lanes; the conversion is as follows: \[OP=10-\frac{OP}{W/W^{\prime}}\times 10\] (10) where \(W\) denotes the width of the bridge deck and \(W\) denotes the width of the vehicle;  
The weighted average method was used for the fusion of the damage indicators, and the weight values of both \(RBC\) and \(OP\) were set to 0.5, and any of the damage indicators were large enough to indicate a high degree of damage in the girder bridges, which resulted in the design of the bridge target of the integrated damage indicator values calculated as follows:  
\[AG=\left\{\begin{array}{cc}RBC\times 0.5+OP\times 0.5&\text{if }(RBC\leq 6 \text{ and }OP\leq 6)\\ \max\{RBC,OP\}&\text{if }(RBC>6\text{ or }OP>6)\end{array}\right., \tag{11}\]  
The girder bridge damage results are designated as three light, medium, and heavy grades, and finally, the girder bridge damage assessment grade evaluation rules are established, as shown in Table 2.  
Figure 6: Simplified diagram of bridge deck.
## 3 Results
### Wind Load Conditions for the Open Courtyard of the Ancestral Hall
#### 3.1.2 Vulnerable Parts under Comprehensive Wind Pressure Inside and Outside the Middle Hall  
At a wind direction angle of \(0^{\circ}\), the central hall is obstructed by the main entrance, resulting in negative pressure on the surface of the internal protective structure, ranging from \(-50\) to \(-100\) Pa, with wind suction on the inner surface of the roof. The majority of the windward surface of the roof's exterior experiences positive pressure ranging from \(0\) to \(150\) Pa, representing wind pressure. Exceptional negative pressure occurs at the windward corner, with a maximum negative pressure of \(-80\) Pa, resulting in downward wind pressure on the windward roof under the combined internal and external pressures. The leeward surface of the roof's exterior experiences negative pressure throughout, forming symmetrical areas of negative pressure in the middle of the roof, ranging from \(-100\) to \(-300\) Pa. These areas on the leeward roof experience upward wind suction under the combined internal and external pressures (Figure 10).  
At a wind direction angle of \(45^{\circ}\), the surface wind pressure on the internal protective structure of the central hall ranges from \(-250\) to \(-300\) Pa. On the exterior surface, the roof experiences uniformly negative wind pressure, with the highest negative pressures concentrated at the windward eaves, windward ridges, leeward eaves, and the center of the leeward roof, reaching \(-730\) Pa. As a result, the roof of the central hall undergoes overall upward wind suction due to the combined internal and external wind pressures.  
At a wind direction angle of \(90^{\circ}\), the surface wind pressure on the internal protective structure of the central hall ranges from \(-350\) to \(-400\) Pa. Externally, the surface wind pressure shows a significant negative distribution, ranging from \(-250\) to \(-700\) Pa, gradually increasing in the direction of the incoming flow, with the maximum negative pressure concentrated at the eaves, ridges, and corners of the roof. Considering the combined wind pressures inside and outside the roof, it is evident that the windward near-end eaves, ridges, and corners experience wind suction, while the windward far-end eaves, ridges, and corners face downward wind pressure.  
Figure 10: The distribution of wind pressure on the building surface: (**a**) \(0^{\circ}\) wind angle; (**b**) \(45^{\circ}\) wind angle; (**c**) \(90^{\circ}\) wind angle.  
From the above observations, it can be concluded that the interior of the central hall building, which includes front and rear open courtyards, experiences negative pressure under various wind direction angles. The internal pressure of the building varies significantly depending on the wind direction angle. Considering the magnitude of wind pressure on the exterior surface of the roof, the central hall building is prone to upward wind suction at the eaves, ridges, and other locations, potentially resulting in roof damage.