esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion  
### Beams' Cracking  
After applying the loads, crack formation and width in all tested TLBs were documented. The data on cracks' opening are summarized in Table 3.  
Figure 2: The testing setup: (**a**) the amplifying equipment scheme: 1—lever arm, 2—upper traverse, 3—upper TLB, 4—lower TLB, 5—lower traverse, 6—LVDTs, 7—jack; (**b**) a view of all 6 tested TLB specimens under loading.  
As it follows from the table, there were no cracks in the top beam loaded by 70% of the ultimate load (Case 1), whereas in the bottom beam, just one crack appeared. It can be explained by the fact that the bottom beam carries, in addition to the applied load, the self-weight of the upper beam as well as that of the steel beam and rods (see Figure 1).  
In Case 2, the specimens were pre-cracked by applying 85% of the ultimate load, unloaded, and loaded again by 70% of the ultimate load. The second loading has not resulted in new cracks appearing, but the old ones have remained.  
In the uncracked specimens subjected to 85% of the ultimate load (Case 3), three cracks appeared in the upper beam and two cracks appeared in the bottom one; however, the cracks' depth in the bottom beam was higher than in the upper one. It should be mentioned that the cracks have not propagated to the SFHSC layer.  
A typical pattern of cracks in the TLB specimens is shown in Figure 3. As it is evident from this figure, no cracks have been observed near the support as well as between the SFHSC and NSC layers. The cracks appeared within the limits of the pure bending zone. No de-bonding was observed between the NSC and SFHSC layers (see Figure 3). Typical cracks appeared in the NSC layer only.
## 3 Results and Discussions
### Characterization
#### 3.1.3 Composition and Chemical State Analysis  
XPS was applied to analyse the composition and chemical state of pure TiO\({}_{2}\) and RE/TiO\({}_{2}\). Figure 4a indicates the survey spectra; there are obvious O and Ti peaks that come from synthesized pure TiO\({}_{2}\) or RE/TiO\({}_{2}\). Figure 4b indicates the spectra of doped La, and the characteristic peaks of La were detected near 853.92 eV and 836.79 eV, which suggests that rare-earth element La was doped successfully. However, the other three elements Ce, Gd and Yb were not detected by XPS. In order to prove the existence of Ce, Gd and Yb in the prepared RE/TiO\({}_{2}\), EDS was used to analyze the content. The result indicates that there was 0.016% of Ce in 0.20%/Ce/TiO\({}_{2}\), 0.20% of Yb in 0.5% Yb/TiO\({}_{2}\), and 0.40% of Gd in 0.70% Gd/TiO\({}_{2}\).  
The peaks of Ti2p and O1s for pure TiO\({}_{2}\) and Gd/TiO\({}_{2}\) were analyzed as shown in Figure 4c-f. Ti peaks of pure TiO\({}_{2}\) are located at 464.45 eV and 458.74 eV while Ti peaks of Gd/TiO\({}_{2}\) are located at 464.48 eV and 458.76 eV. The peak of O1s was fitted into two peaks: crystal lattice oxygen and hydroxyl oxygen [11]. The fitted peak of O1s at 529.83 eV corresponded to the lattice oxygen while the peak at 530.31 eV corresponded to hydroxyl oxygen for pure TiO\({}_{2}\). As seen from Figure 4c-f, after doping with Gd, the binding energy of Ti2p increased while the binding energy of O1s decreased for Gd/TiO\({}_{2}\) compared with pure TiO\({}_{2}\). The other three RE/TiO\({}_{2}\) composites (La/TiO\({}_{2}\), Ce/TiO\({}_{2}\) and Yb/TiO\({}_{2}\)) all present a similar situation, which might be due to the interaction of RE with Ti and O in RE/TiO\({}_{2}\)[34].  
Figure 3: UV-Vis DRS patterns of RE/TiO\({}_{2}\) with different rare-earth doping ratios. (**a**) La/TiO\({}_{2}\); (**b**) Ce/TiO\({}_{2}\); (**c**) Gd/TiO\({}_{2}\); (**d**) Yb/TiO\({}_{2}\).
## 3 Methodology
### Training and Evaluation  
#### 3.4.1 Model Setup  
As shown in Figure 4, the size of the sliding window is 55. An excessive or insufficient number of BiLSTM layers can lead to model overfitting or underfitting. Two BiLSTM layers are selected in this study. The first layer uses the sequence output mode, while the second layer uses the last output mode. Both layers contain 128 cells. A fully connected layer is used to output SOE. The number of epochs and the batch size are set to 150 and 128, respectively. Additionally, set the BiLSTM network with a dropout rate of 0.1, initial learning rate of 0.001, learning rate drop factor of 0.1, and learning rate drop period of 10. The Adam algorithm is selected to optimize weights and biases. The noise covariance of the KF is optimized by the PSO algorithm. The number of particles is set to 30, and the maximum number of iterations is set to 50. The ranges of Qt and Rt are established from 1 \(\times\) 10\({}^{-12}\) to 0.1. The proposed model in this paper runs on a computer equipped with an AMD 7945 HX CPU @ 2.5 GHz (16 cores, 32 threads) and an Nvidia GeForce RTX 4060.
## 3 Materials and Methods
### Features
#### 3.3.3 Average Size of Packets  
**Feature feasibility:** Average size of packets is another good network behavior indicator. Many device level systems with high real time requirements send packets without extra buffering creating distinct average size statistics for these networks. In many networks the average size of packets can be considered to be a good indicator of normal behavior.  
**Our data:** Average size of the packets looks like a feasible feature in the context and time frame of the packet captures. When the trace files where split into 600 s slices and further analyzed, it was clear that there was minimal fluctuation in the Tap1 recording which was not exposed to traffic coming and going to the office side. In the Table 2 we can see that the Tap2 has a wider fluctuation in the average packet size and data rate. Average packet size over the total duration of both packet capture files can be found in Table 1 Should the stability of the average packet size persist for longer periods of time as well during nominal operation of the ICS network, we argue that either increases or decreases in this value could signal an anomalous situation. Average packet sizes are visualized in Figure 2 which clearly shows the difference in the variance of average packet sizes.
# Stability Criterion for Dynamic Gaits of Quadruped Robot
## 5 Results and Discussion
### Simulation 1  
Most existing stability criteria for trotting gaits assume that the robot is in stable motion only when _ZMP_ is located on the support line formed by the current support feet. However, this is inconsistent with the movement of animals in nature. To illustrate that these stability criteria cannot accurately determine the stability state of the robot, the following simulation was designed.  
The projection of CoM in the direction of gravity onto the virtual-support plane is denoted by CoG. In the trot-gait-motion planning used in this simulation, the CoM of the body moves forward at a constant speed and, at each phase-changing point, CoG is located at the center of the quadrilateral formed by the just-disappearing support line and the newly formed line (see Figure 13).  
The simulation animation is in Video S1. The position changes of the support lines, CoG, and _ZMP_ on the support plane (\(z_{i}\)O\(y_{i}\) plane) during the entire simulation process are shown in Figure 14.  
The gray lines in Figure 14 are the support lines and are shown as several groups of line clusters for two reasons. First, the active or passive compliance was not considered in this simulation process, therefore the feet were bouncing for a short period after landing. Second, the feet were slipping during the support phases. The blue line is the actual motion trajectory of CoG on the horizontal plane. The motion trajectory of CoG was not completely consistent with the planned traject  
Figure 14: Positions of the support line and _ZMP_.  
Figure 13: Movement of CoG.  
of the unbalanced moment around the current support line, the legs' movement, and the impact force at the feet. The red line is the trajectory of traditional _ZMP_. It can be clearly seen that, during this simulation, _ZMP_ was not always or mostly located on the current support line.  
As shown in Figure 15, the running direction of the body was slightly skewed (see yaw angle), and the absolute values of the pitch and roll angles were less than 0.11 rad (6\({}^{\circ}\)). The curves of these attitude angles show that the robot moved stably and had no obvious tendency to tumble. This indicates that the existing stability criteria for the trot gait cannot accurately determine the stability state of the robot.
## 4 Adaptive Robust Model Free Control
### Robustness Verification  
To confirm the robustness of the proposed scheme under uncertain disturbances, \(\tau_{d}=7 Nm\) was introduced into the knee joint at the time \(t=2\) s. The angular trajectories in Figure 6a,b show that the disturbance has the greatest influence on the iPD-TDE scheme. The tracking performance of iPD-TDE degrades significantly under the effect of interference. ANFTSMC-TDE and FNN-ANFTSMC-TDE techniques can complete the tracking task with unknown disturbance. It is obvious that the application of FNN makes the system return to a stable state more quickly under interference. Sliding variable  
Figure 5: Simulation results of comparison among iPD-TDE, FTSMC-TDE, ANFTSMC-TDE, and FNN-ANFTSMC-TDE: (**a**) trajectory tracking; (**b**) trajectory error; and (**c**) control torque.  
among ANFTSMC-TDE and FNN-ANFTSMC-TDE has converged on the smaller layer, as shown in Figure 6c.  
Furthermore, we supplied two more complex disturbances \(\tau_{d1}=0.5*u\) and \(\tau_{d2}=8*q+10*\dot{q}^{2}+5*cos(q)\) into the system. The previous disturbance means a 50% loss fault in the actuator at 1.5s and the duration was 0.03 s. The latter disturbance is a time-varying signals at \(t=4\) s. No experiments on iPD-TDE were performed due to failure tracking at any disturbance. To facilitate comparison, based on Figure 7a-c, we can draw the following conclusions: (1) FNN-ANFTSMC-TDE and ANFTSMC-TDE give better performance compared to FTSMC-TDE under all kinds of interference; and (2) FNN-ANFTSMC-TDE has stronger robustness when interference occurs.  
Figure 6: Simulation results of comparison among iPD-TDE, FTSMC-TDE, ANFTSMC-TDE, and FNN-ANFTSMC-TDE when \(\tau_{d}(t=2\) s) occurs: (**a**) trajectory tracking with \(\tau_{d}\); (**b**) trajectory error with \(\tau_{d}\); and (**c**) sliding surface with \(\tau_{d}\).
## 3 Materials and Methods
### Channel and Feature Selection
An exhaustive search is performed in 100 generations of GA in which each consists of 200 chromosomes, known as population, to generate various channel combinations with crossover of 0.8, and mutation of 0.02. The TIFs are then extracted from individual combinations for further resubstitution loss estimation of the KNN model-based fitness function on the training set using the wise-subject 5-fold CV. A repetition of 100 times is deployed for the GA resulting in the number of times that channels are selected. Because the individual channels represent variously specific human brain regions, which generate definitely the emotions. Hence, we remove the channels which are omitted by the GA. As a result, the channels with the numbers of chosen times over zero are collected as the OCH. A similar GA procedure is applied for the feature selection using the above OCH. Here, a string of 42 bits is used to represent a total of 42 input features of the individual OCH. Bits 1 and 0 indicate the presence and absence of features extracted from the OCH. Consequently, the input features are identical for all channels in the OCH at each GA repetition as given in Algorithm 1. A large number of 5376 input features extracted from 4 sub-bands for which each sub-band includes 32 channels makes it difficult to apply for the practical environment. Therefore, it is necessary to set a proper number of selections by the GA to ensure a relevant number of selected features while maintaining relatively high classification performance and small processing time of the proposed algorithm. In this work, we address the SCFs as a set of individual features with the numbers of selected times by the GA, which are larger than 50 known as a half of GA repetitions.
## 4 Device Characterization
### Passing the Reliability Test Criteria  
In the following, reliability tests are performed in accordance with the definitions and requirements in document Telecordia GR-468-CORE for optoelectronic systems, such as high temperature operation (HTOL) test, temperature cycling (TC) test, damp heat (DH), and COB module wire-bond test. All devices under test (DUT) are selected from three consecutively fabricated batches, and they need to pass all these reliability tests to demonstrate consistency and reliability in applying the devices for products.  
Figure 14a shows the results of the HTOL test over a total of 78 DUT from the three consecutively fabricated batches. During testing, the dark currents of the Ge p-i-n PDs built on an evaluation board are initially biased at \(-3\) V bias and \(25\ ^{\circ}\)C for dark current measurements. Then, these DUT are biased at \(-6\) V bias and \(175\ ^{\circ}\)C for 168 h, 500 h, 1000 h, 1500 h, and 2000 h. After each heating duration, the DUT are cooled down back to the room temperature for dark current measurements at \(-3\) V bias. Figure 14a shows the measured dark currents at the six check points. As the failure criterion is set to be a dark current variation exceeding 50% of the initial value, all DUT pass the HTOL test for the heating durations up to 2000 h. The fabricated devices also pass the high temperature storage (HTS) test over a total of 66 DUT from the three consecutively fabricated batches. The HTS test is performed at 135 \({}^{\circ}\)C for a duration of 2000 h, as shown in Figure 14b, in which the variations of QE measured after 2000 h are shown. The failure criterion for the HTS test is set to be a QE variation exceeding 5% of the initial value after 2000 h. The two testing results reveal that the SU8 polymer material is strong enough to withstand thermal stress, implying long product lifetimes. In the TC test, a total of 75 DUT, subjected to 500 cycles of \(-40\ ^{\circ}\)C/80 \({}^{\circ}\)C at \(10\ ^{\circ}\)C change per minute temperature variation rate, all pass the same criterion as in HTOL test. In the DH test, a total of 51 DUT, subjected to \(85\ ^{\circ}\)C temperature and 85% relative humidity for 500 h, all pass the same criterion as in HTOL test. The COB module wire-bond test including ball pull force and shear force, which indicates the wire-bond strength between a device pad and a pad on the evaluation, results in a strength of 9.19 gf and 13.78 gf, respectively, both conforming to the success criteria. Finally, the electrostatic discharge (ESD) test, which evaluates the electrical pulses that may occur during probing, wire-bonding, pick-and-place, and so on, is performed. The most common ESD test is the Human Body Mode (HBM) test, and results from the ESD test under such a mode show that our devices could withstand electrical pulses at a forward bias of 2000 V and a reverse bias of 250 V. These excellent reliability test results showcase that our devices can be used in commercial products.
## 2 Related Work
### Detailed Structure of SE-GNNRM
#### 3.2.3 Session Interaction Layer  
The target embedding and global embedding of the session actually only pay attention to the current session features but ignore the similar but different features between sessions, so this paper designs a session interaction layer. The GAT [40] aggregates neighbor node features through an attention mechanism, and the FGNN [14] uses GAT with weight information to learn node features. Inspired by the GAT and FGNN, this model calculates the co-occurrence degree of the same interactive items in existing sessions as the co-occurrence parameter \(\phi_{ij}\) and integrates the graph attention network for feature propagation so that sessions with high similarity can learn from each other features that have an important impact on recommendation results. The final embedding of a session depends on the combination of the session itself and its neighborhood sessions.  
In the session interaction layer, first, a global session graph is constructed, in which each node represents a session, and the co-occurrence parameter \(\phi_{ij}\) of the items in the session is used to determine the connections of each node in the global session graph. Then, the current session embedding \(h_{c}\) output by the session embedding layer is used asthe initial input of the feature transfer function, and the similarity coefficient \(e_{ij}\) between nodes and neighboring nodes is obtained by calculating the inner product of nodes in the global session graph and their co-occurrence parameters. Finally, the softmax function is used to normalize \(e_{ij}\) to obtain the inter-session attention coefficient \(\beta_{ij}\), which is described as follows.  
\[e_{ij}=LeakyRelu\big{(}\left[h_{i}\odot h_{j}\|\phi_{ij}\right]W_{7}\big{)}W_{8} \tag{13}\]  
\[\beta_{ij}=\frac{\exp\big{(}e_{ij}\big{)}}{\sum_{h_{k}\in N_{h_{i}}}\exp(e_{ik})} \tag{14}\]  
where, \(LeakyRelu(~{})\) is a nonlinear function. Its negative slope is set to 0.2, \(W_{7}\in R^{(d+1)\times d}\), \(W_{8}\in R^{d}\) is a learnable weight matrix, \(\odot\) is element-wise multiplication, \(N_{h_{i}}\) denotes the set of \(h_{i}\) neighborhood sessions. \(h_{i},~{}h_{j},~{}h_{k}\) are the outputs of the session embedding layer. The attention-aware neighborhood session embedding \(h_{i}^{s}\) is given as follows.  
\[h_{i}^{s}=\sum_{h_{k}\in N_{h_{i}}}\beta_{ij}h_{j} \tag{15}\]  
The final step of the session interaction layer will rely on the attention-aware neighborhood session embedding \(h_{i}^{s}\) to be aggregated with the current session embedding \(h_{i}\). The neighborhood aggregation formula adopts a gating mechanism to autonomously select more meaningful session features, which is described as follows.  
\[\gamma=\sigma(W_{9}h_{i}+W_{10}h_{i}^{s}) \tag{16}\]  
\[h_{f}=\gamma h_{i}+(1-\gamma)h_{i}^{s} \tag{17}\]  
where \(W_{9},W_{10}\in R^{d\times d}\) is a learnable weight matrix. \(\gamma\) is a gate obtained by a nonlinear activation function after calculating the linear transformation of \(h_{i}\) and \(h_{i}^{s}\), \(h_{f}\) is the global embedding session.
## 3 Results
### Comparison between the Bone Mineral Density Parameters among Regions
#### 3.2.2 _Trabecular Number_ (Tb.n)
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Region**} & \multicolumn{3}{c}{**Right**} & \multicolumn{3}{c}{**Left**} & \multicolumn{3}{c}{**Difference**} & \multirow{2}{*}{\(p\)**-Value**} \\ \cline{3-3} \cline{5-9}  & **Median** & **IQR** & **Median** & **IQR** & **Median** & **IQR** & \\ \hline \multirow{4}{*}{Mx} & Tb.th & 3.79 & 1.11 & 3.71 & 0.79 & 0.10 & 0.94 & 0.568 \\  & Tb.n & 0.14 & 0.04 & 0.14 & 0.03 & 0.00 & 0.03 & 0.298 \\  & Tb.sp & 3.57 & 0.82 & 3.52 & 0.85 & \(-\)0.02 & 0.81 & 0.640 \\  & BV/TV & 54.52 & 10.23 & 53.10 & 11.81 & 0.49 & 9.34 & 0.502 \\  & HU & 1441.41 & 332.27 & 1505.36 & 268.68 & \(-\)74.48 & 331.90 & 0.268 \\ \hline \multirow{4}{*}{Mn} & Tb.th & 3.45 & 0.67 & 3.42 & 0.68 & 0.04 & 0.69 & 0.497 \\  & Tb.n & 0.14 & 0.06 & 0.14 & 0.07 & 0.02 & 0.10 & 0.572 \\  & Tb.sp & 3.62 & 1.17 & 3.71 & 0.91 & \(-\)0.08 & 0.98 & 0.354 \\  & BV/TV & 49.86 & 13.25 & 48.03 & 11.85 & 1.79 & 11.21 & 0.374 \\  & HU & 1401.38 & 304.89 & 1416.11 & 283.95 & \(-\)13.08 & 290.25 & 0.248 \\ \hline \multirow{4}{*}{Co} & Tb.th & 3.17 & 0.51 & 3.24 & 0.43 & \(-\)0.06 & 0.58 & 0.349 \\  & Tb.n & 0.14 & 0.04 & 0.13 & 0.03 & 0.01 & 0.03 & 0.130 \\  & Tb.sp & 4.46 & 3.96 & 4.55 & 3.75 & \(-\)0.31 & 4.73 & 0.811 \\  & BV/TV & 48.07 & 21.83 & 44.58 & 23.50 & \(-\)0.75 & 33.20 & 0.868 \\  & HU & 653.02 & 315.91 & 715.49 & 378.01 & \(-\)49.77 & 383.40 & 0.402 \\ \hline \multirow{4}{*}{Cv} & Tb.th & 3.22 & 0.60 & 3.35 & 0.47 & \(-\)0.14 & 0.65 & 0.201 \\  & Tb.n & 0.13 & 0.09 & 0.14 & 0.05 & \(-\)0.01 & 0.07 & 0.366 \\ \cline{1-1}  & Tb.sp & 4.50 & 3.79 & 3.96 & 2.48 & 0.79 & 2.89 & 0.659 \\ \cline{1-1}  & BV/TV & 43.86 & 28.81 & 49.26 & 20.35 & \(-\)6.51 & 25.54 & 0.383 \\ \cline{1-1}  & HU & 511.09 & 236.07 & 575.71 & 279.92 & \(-\)45.18 & 235.75 & 0.186 \\ \hline \hline \end{tabular} IQR: Interquartile range; Tb.th: Trabecular thickness; Tb.n: Trabecular number; Tb.sp: Trabecular separation; BV/TV: Bone volume fraction; HU: Hounsfield unit; Mx: Arterial base of the maxilla; Mn: mandibular body; Co: mandibular cordyle; Cr: First cervical vertebra.  
\end{table} 
Table 3: Bone mineral density measurement in the right and left side and the differences between males and females.
## 3 Results and Discussion
### Influence of the Cooling Water Temperature  
Figure 11 presents the performance of the PCTCS under varying T\({}_{\text{win}}\). As depicted in Figure 11a, when T\({}_{\text{win}}\) increased from 15 \({}^{\circ}\)C to 60 \({}^{\circ}\)C, the average temperature of the two CPUs rose from 61.46 \({}^{\circ}\)C to 89.03 \({}^{\circ}\)C. Simultaneously, the Tdt of the PCTCS decreased from 56.74 \({}^{\circ}\)C to 29.63 \({}^{\circ}\)C, suggesting that a higher T\({}_{\text{win}}\) can enhance the PCTCS's temperature performance. In Figure 11b, as T\({}_{\text{win}}\) escalated from 5 \({}^{\circ}\)C to 60 \({}^{\circ}\)C, Ppump in the PCTCS fell from 32.5 W to 23.9 W, while EERp improved from 98 to 134. This trend signifies that a greater T\({}_{\text{win}}\) value improves the PCTCS's energy efficiency. As shown in Figure 11c, as  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**Condenser Position**} & \multicolumn{3}{c}{**Condenser Position**} \\  & **Same Height** & **1 m Higher** & **1 m Lower** & **Parameter** & **Same Height** & **1 m Higher** & **1 m Lower** \\ \hline Pdt (kPa) & 83.21 & 78.14 & 92.53 & Tdt (\({}^{\circ}\)C) & 34.50 & 34.97 & 34.05 \\ Ppump (W) & 27.9 & 26.27 & 30.86 & \(\overline{\text{T}}_{\text{cpu1}}\) (\({}^{\circ}\)C) & 78.72 & 79.16 & 78.28 \\ EERp & 115 & 122 & 104 & \(\overline{\text{T}}_{\text{cpu2}}\) (\({}^{\circ}\)C) & 79.50 & 79.97 & 79.05 \\ \(\text{l}_{\text{con}}\) & 0.513 & 0.518 & 0.479 & \(\overline{\text{T}}_{\text{cpu}}\) (\({}^{\circ}\)C) & 0.78 & 0.81 & 0.77 \\ \(\text{d}\text{T}_{\text{sub}}\) of the first & 14.51 & 13.17 & 17.06 & \(S_{\text{cpu1}}\)\({}^{2}\) & 0.08 & 0.09 & 0.07 \\ \(\text{d}\text{T}_{\text{sub}}\) of the condenser & 0.78 & - & 4.59 & \(S_{\text{cpu2}}\)\({}^{2}\) & 0.34 & 0.37 & 0.31 \\ outlet (\({}^{\circ}\)C) & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation results of the PCTCS with different condenser heights.  
the \(\mathrm{T_{win}}\) increased, the PCTCS's Pdt and \(\mathrm{dT_{sub}}\) diminished, and a significant reduction in \(\mathrm{dT_{sub}}\) at the first mini-channel evaporator inlet was observed, dropping from 37.72 \({}^{\circ}\)C at a \(\mathrm{T_{win}}\) of 5 \({}^{\circ}\)C to 8.86 \({}^{\circ}\)C at 60 \({}^{\circ}\)C. In summary, an increase in \(\mathrm{T_{win}}\) significantly enhances both the temperature performance and energy efficiency of the PCTCS. Therefore, it is recommended to maximize the \(\mathrm{T_{win}}\) during the operation of the PCTCS, provided the \(\mathrm{T_{cpu}}\) does not surpass the threshold. This recommendation is in alignment with the conclusions drawn from several notable studies, including those conducted by [PERSON] [48] and [PERSON] [49]. Importantly, a higher \(\mathrm{T_{win}}\) is favorable for natural cooling and waste heat recovery.