esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Control Strategy
### Active Power Control of a DFIG  
Initially, a Pcmd is sent to the DFIG from WF control unit. Regarding WTG rotor speed feedback and the available wind velocity, Pcmd is corrected and applied to the electrical part model as mentioned in Section 2. If Pcmd is not appropriately allocated to each individual WTG, stability problems may occur.  
Considering all possible situations in this procedure, three modes of operation can be defined as follows:  
* Medium wind condition  
In this mode of operation, when wind velocity fluctuates, the moment of inertia in WTG acts to absorb or release energy. As rotor speed varies between allowable operating limits, DFIG traces the Pcmd properly and keeps the WTG output constant. Changes in the value of Pcmd and \(\tau_{\mathrm{em}}\) will result in a new state of \(\omega_{\mathrm{r}}\).  
* High wind condition  
There are situations in which Pcmd is not properly set and this makes the DFIG accelerate and rotor speed may run out of its upper limit. If preventive action is not taken, stability problems may occur [23]. When this situation takes place, wind acquisition must be limited by initializing pitch angle controller. When \(\omega_{\mathrm{r}}\) is near its upper limit (\(\omega^{\mathrm{up.lim}}\)), the total wind power acquisition must be reduced by proper adjustment of turbine's blade pitch angle. The schematic of the proposed PAC is depicted in Figure 3. The difference between \(\omega_{\mathrm{r}}\) and its upper reference (\(\omega_{\mathrm{r}}\)max) is applied to a proportional controller and then to a servomotor to adjust the pitch angle. A proper speed margin between \(\omega_{\mathrm{r}}\)max and \(\omega^{\mathrm{up.lim}}\) must be considered to ensure proper operation of pitch angle control unit. When \(\omega_{\mathrm{r}}\) decreases, WTG operation switches back to the mode of medium wind condition.  
* Low wind condition  
There are situations in which the available wind power is lower than the Pcmd set point value determined by WF control unit, which results in \(\omega_{\mathrm{r}}\) reduction. In order to maintain stable performance of DFIG, Pcmd must be corrected in this case applying Equation (18) [4].  
\[\mathrm{P_{cmd}}^{(\mathrm{new})}=\mathrm{Pcmd}\;+\;\mathrm{P_{slope}}( \omega_{\mathrm{r}}-\omega_{0}), \tag{18}\]  
\(\mathrm{P_{cmd}}^{(\mathrm{new})}\) is the corrected value of Pcmd. \(\omega_{0}\) is the rotor speed corresponding to the value of Pcmd. \(\mathrm{P_{slope}}\) is calculated according to Equation (19). \(\mathrm{P_{start}}\) and \(\omega_{\mathrm{start}}\) are cut-in power and rotor speed, respectively.  
\[\mathrm{P_{slope}}\;=\;\frac{\mathrm{P_{0}}-\mathrm{P_{start}}}{\omega_{0}- \omega_{\mathrm{start}}}, \tag{19}\]
# A Scheduling Scheme for Improving the Performance and Security of MU-MIMO Systems
## 3 Instantaneous Signal-to-Noise Ratio  
From (9), the variance of the \(k\)-th element of \(\mathbf{z}\), that is, \(z_{k}\), conditioned on the instantaneous elements of \(\hat{\mathbf{H}}\), can be found as follows  
\[\begin{split}\text{Var}[z_{k}|\hat{\mathbf{H}}]&= \text{E}\Big{[}(\hat{\mathbf{H}}^{\intercal}+\mathbf{n})(\hat{\mathbf{H}}^{ \intercal}+\mathbf{n})^{H}\mid\hat{\mathbf{H}}\Big{]}_{k,k}\\ &=\Big{[}\hat{\mathbf{H}}^{\intercal}\text{E}[\mathbf{n}\mathbf{ n}^{H}](\hat{\mathbf{H}}^{\intercal})^{H}\Big{]}_{k,k}\\ &=\sigma_{n}^{2}\Big{[}\hat{\mathbf{H}}^{\intercal}(\hat{ \mathbf{H}}^{\intercal})^{H}\Big{]}_{k,k}\\ &=\sigma_{n}^{2}\Big{[}(\hat{\mathbf{H}}^{\intercal}\hat{ \mathbf{H}})^{-1}\Big{]}_{k,k},\end{split} \tag{10}\]  
where the usage is that \((\mathbf{A}\mathbf{B})^{H}=\mathbf{B}^{H}\mathbf{A}^{H}\), that \(\text{E}[\mathbf{n}\mathbf{n}^{H}]=\sigma_{n}^{2}\mathbf{I}_{A}\), and where \(\mathbf{I}_{A}\) is a \(A\times A\) identity matrix. Finally, in the last step, the identity \(\hat{\mathbf{H}}^{\intercal}(\hat{\mathbf{H}}^{\intercal})^{H}=(\hat{ \mathbf{H}}^{H}\hat{\mathbf{H}})^{-1}\) is employed [46].  
From (10), the instantaneous SNR at the output of the ZF detector for the \(k\)-th user can be written as  
\[\gamma_{k}=\frac{s_{k}^{2}}{2\text{Var}[z_{k}|\hat{\mathbf{H}}]}= \frac{s_{k}^{2}}{2\sigma_{n}^{2}}\zeta_{k}=\frac{E_{b}}{N_{0}}\zeta_{k}\log_{2 }M, \tag{11}\]  
where \(\zeta_{k}\) is a random variable that can be written as  
\[\zeta_{k}=\frac{1}{[\mathbf{V}^{-1}]_{k,k}}, \tag{12}\]  
where the \(U\times U\) matrix \(\mathbf{V}\) is obtained as \(\mathbf{V}=\hat{\mathbf{H}}^{H}\hat{\mathbf{H}}\). In addition, it was considered that the energy received per symbol is \(E_{s}=s_{k}^{2}T_{s}/2\) and that the energy received per bit is equal to \(E_{b}=E_{s}/\log_{2}(M)\), where \(M\) is the modulation order. Consequently, \(\log_{2}M\) is the number of bits transmitted in each symbol.
## 3 Methodology
### Data Acquisition

Table 2: The number of images collected depending on the flight altitude and the overlap percentage. Figure 4: Flight plans for data collection: (**a**) the 60 m flight with 80% front overlap and 60% side overlap, (**b**) the 60 m flight with 90% and 70% overlap, (**c**) the 80m flight with 80% and 60% overlap, (**d**) the 80 m flight with 90% and 70% overlap, (**e**) the 100 m flight with 80% and 60% overlap, and (**f**) the 100 m flight with 90% and 70% overlap. In addition, images were collected using the panorama mode from Litchi [67], with which horizontal, vertical, and spherical panoramas were captured. A total of three panoramas were captured at a flight height of 60 m, 80 m, and 100 m, and the images were arranged in eight columns per 45\({}^{\circ}\) and five rows per 24\({}^{\circ}\), with each panorama consisting of 43 images. Restrictions were observed during the data collection process, one of which was the sun's angle as the orientation of the study area was north, which resulted in certain parts not being adequately lit. Another factor that influenced and intensified this specific phenomenon was the gorge's intense geometry, resulting in strongly shaded parts of the geosite which created negative slopes.
## 4 Applications of Multimodal Learning for Fault Detection Diagnosis
### Applications in Renewable Energy Systems
#### 4.2.3 Integrating Multiple Data Sources for Better Fault Detection  
A key advantage of modern diagnostic systems is their ability to integrate data from multiple sensor types. In wind turbines, combining vibration, temperature, and acoustic data provides a comprehensive assessment of mechanical health. Similarly, in PV systems, integrating current, voltage, and temperature readings enables the more reliable detection of underperformance and hardware faults.  
However, multimodal integration presents challenges. Sensors may collect data at different frequencies or formats, and some data streams may be missing or noisy. To address these challenges, advanced preprocessing techniques such as smoothing, filtering, or interpolation are employed to create a consistent, clean dataset suitable for reliable fault detection.
## 2 Experiment Setup  
The experiment setup is shown in Figure 1. The pump sources consisted of two CW linearly polarized fiber lasers, a low-power 1064 nm SF fiber laser and a high-power 1018 nm fiber laser, with a maximum power of 340 mW and 56 W, respectively. They were connected together using a polarization-maintaining 1018/1080 nm wavelength division multiplexer (WDM). As shown in Figure 1, port 1 and 2 of the WDM were fused with the output fiber of two pump sources. Port 3 was the output port and was connected with a fiber collimator, and port 4 was a monitoring port used to monitor the working status of the two fiber sources. The OPO cavity configuration is similar to the setup described previously [13], and the nonlinear crystal used in this experiment was a bulk of 50 mm \(\times\) 5 mm \(\times\) 1 mm 5% MgO-doped PPLN with a grating period of 29.8 \(\upmu\)m. After the cavity was two dichroic mirrors, M5 and M6, which were used to separate the residual pump, signal and idler beams. In this experiment, an oven was used to control the temperature of the PPLN crystal for studying the frequency down-conversion of the 1064 nm SF pump beam under different phase-matching conditions.
# Experimental Investigations on Laser Ablation of Aluminum in Sub-Picosecond Regimes
## 3 Experimental Setup  
The micromachining of aluminum samples was conducted with two different lasers, a sub-picosecond Yb:KYW (Lab-designed at the Institute of Physical Chemistry PAS, Warsaw, Poland, 2010) and a femtosecond Ti:S laser (Spectra-Physics, Santa Clara, CA, USA, 2012). The parameters are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline Wavelength (nm) & 1030 & 800 & 532 & 345 & 266 \\ \hline Ablation threshold (J/cm\({}^{2}\)) & 1.36 & 1.06 & 0.70 & 0.46 & 0.35 \\ \hline \end{tabular}
\end{table}
Table 1: Calculated values of the aluminum ablation threshold, for different wavelengths.  
For the first laser, 1030 nm and 345 nm wavelengths were made available for the micromachining processes. For the second one, three different wavelengths from the UV, VIS and IR range were picked, as well as being used for the calculations in Table 1.  
Figure 1 shows that, as the laser beam passes through the optical collimator and focusing system, consisting of mirrors and lenses adequate for the applied laser wavelength, it is then transferred to the high-precision positioning system, based on XY linear motors. [14]
## 5 Experimental Results  
The following section presents experimental results of a network topology used for biomedical applications. We realized a performance analysis using a star topology that only considered one of the routers in the global topology, which had three end devices connected to it.
# Target Image Mask Correction Based on Skeleton Divergence
## 1 Introduction
However, owing to the number of point clouds, the calculation demands of the system are high, leading to low system efficiency. [PERSON] et al. [PERSON] et al. (2018) discussed in detail the use of a transformation matrix obtained by key point matching to improve the stitching precision of a scene, although the selection of key points was affected by dynamic participants in the scene, leading to low robustness in practical applications. [PERSON] et al. [PERSON] et al. (2018) used the dotted-line feature of a 2D image to perform closed-loop detection and achieve high-precision scene stitching reconstruction through probability conversion of the octree structure. However, the point-line feature constraint of the system was complicated, not robust, and invalid in terms of the original scene. [PERSON] (2018) proposed a method for image filtering 3D objects using 2D manifolds, although the calculation requirements of their system were high, and system efficiency was low. In this study, a target image mask correction method based on skeleton divergence is proposed. The skeleton and discrete pixel region edge constraints are used to avoid excessive shrinkage and expansion during pixel region recovery, which ensures high precision of discrete pixel shaping. The edge-guided strategy is used to merge the local pixel regions, and the sparse point-cloud regularization constraints and TOPSIS (technique for order of preference by similarity to ideal solution) [PERSON] (2018) decision-making are used to reduce the error accumulation caused by the combination cost error and improve the robustness of the system. The main contributions of this paper are as follows. Firstly, the idea of skeleton divergence is applied to mask correction, and the idea based on the skeleton is a good way to reduce the problem of excessive contraction and excessive expansion. Secondly, based on the work of predecessors, a complete high-precision mask correction system has been presented. We compare our algorithm with some existing algorithms. Experimental results prove our algorithm can achieve higher mask correction accuracy and efficiency. The remainder of this paper is organized as follows. Section 2 introduces each part of the system. Section 3 outlines the experiment conducted and analyzes the results obtained. Section 4 presents our concluding remarks.
# NOMA or OMA in Delay-QoS Limited Satellite Communications: Effective Capacity Analysis
## 3 Effective Capacity
### Closed-Form Expressions of Users' Effective Capacity  
Then, to evaluate the closed-form expression of effective capacity for User \(c\) with the NOMA scheme, we first transform and simplify \({}_{1}F_{1}\left(m_{j};1;\frac{\delta_{j}x}{\alpha\rho}\right)\) by expanding it into generalized hypergeometric series via ([31], Equation (9.14.1)) as  
\[{}_{1}F_{1}\left(m_{j},1,\frac{\delta_{j}x}{\alpha\rho}\right)=\sum_{k=0}^{ \infty}\frac{\delta_{j}^{k}\left(m_{j}\right)_{k}}{\alpha^{k}\rho^{k}k!}x^{k}, \tag{17}\]  
and expressing \(\left(1+\frac{\alpha-1}{\alpha\rho|g_{c}|^{2}+1}\right)^{-\varphi_{c}/\ln 2}\) in terms of the Binominals representations with ([12], Equation (1.11)) as  
\[\left(1+\frac{\alpha-1}{\alpha\rho|g_{c}|^{2}+1}\right)^{\frac{-\varphi_{c}}{ \ln 2}}=\sum_{k=0}^{\infty}\binom{\frac{-\varphi_{c}}{\ln 2}}{k}\left(\frac{ \alpha-1}{\alpha\rho|g_{c}|^{2}+1}\right)^{k}, \tag{18}\]where \(\binom{a}{b}=\frac{(a)_{b}}{b!}\) with \((\cdot)_{b}\) being the Pochhammer symbol ([31], Equation (3.10.1)). Then, inserting (5), (6), (17), and (18) into (11) along with ([32], Equation (13.2.5)), we can obtain the desired result for the expression of \(C_{c}^{\rm N}(\theta_{c})\) as,  
\[E_{c}^{N}=-\frac{1}{\varphi_{t}}\ln\frac{\alpha_{t}\frac{\beta_{c}}{\ln 2}-1}{ \rho}\sum_{k=0}^{\infty}\binom{-\varphi_{k}}{\ln 2}(\alpha-1)^{k}\sum_{m=0}^{ \infty}\frac{\delta_{t}^{k}(m_{c})_{\pi}}{\alpha^{k}\rho^{k}n!}\,U\!\left(n+1, 2+n-k,\frac{\beta_{c}}{\alpha\rho}\right), \tag{19}\]  
where  
\[U(a,b,c)=\frac{1}{\Gamma(a)}\int_{0}^{\infty}e^{-cx}x^{a-1}(1+x)^{b-a-1}dx, \tag{20}\]  
is the confluent hypergeometric function of the second kind [32]. By substituting (5) and (6) into (12) and following with the similar steps as those in the derivation of (19), the effective capacity for User \(t\) with NOMA scheme can be obtained as  
\[E_{t}^{N}=-\frac{1}{\varphi_{t}}\ln\frac{\alpha_{t}}{\alpha\rho}\sum_{k=0}^{ \infty}\frac{\delta_{t}^{k}(m_{1})_{k}}{\alpha^{k}\rho^{k}k!\Gamma(k+1)}U\! \left(k+1,k+2-\frac{\varphi_{t}}{\ln 2},\frac{\beta_{t}}{\alpha\rho}\right). \tag{21}\]  
Similarly, by following similar steps, the effective capacities achieved with the TDMA scheme of Users \(c\) and \(t\) can be, respectively, as  
\[E_{c}^{T}=-\frac{1}{\varphi_{c}}\ln\frac{\alpha_{c}}{\rho}\sum_{k=0}^{\infty} \frac{\delta_{c}^{k}(m_{1})_{k}}{\rho^{k}k!\Gamma(k+1)}U\!\left(k+1,k+2-\frac{ \varphi_{c}}{2\ln 2},\frac{\beta_{c}}{\rho}\right), \tag{22}\]  
and  
\[E_{t}^{T}=-\frac{1}{\varphi_{t}}\ln\frac{\alpha_{t}}{\rho}\sum_{k=0}^{\infty} \frac{\delta_{t}^{k}(m_{1})_{k}}{\rho^{k}k!\Gamma(k+1)}U\!\left(k+1,k+2-\frac{ \varphi_{t}}{2\ln 2},\frac{\beta_{t}}{\rho}\right). \tag{23}\]
# Using Graph Partitioning for Scalable Distributed Quantum Molecular Dynamics
## Appendix B Further Details on Experimental Results  
Raw data for the experiments described in Section 4.3 can be found in Table A1. The six partitioning schemes we use are listed in column \"methods\". The performance of those methods is measured in four different ways: As a measure of the total matrix multiplication cost of a step of the SP2 algorithm, we report the sum of cubes criterion (3) in column 3 (\"sum\"). The sum of cubes criterion is also a measure of the computational effort of SP2 since matrix multiplications consume most of the computation time in SP2. As a measure of the variability of block sizes created by our algorithm, we report the smallest and largest block size of any CH-partition (columns 4 (\"min\") and 5 (\"max\")). In the best-case scenario, the sizes of all blocks should be roughly equal since otherwise, the nodes or processors will have very unequal computational loads in our parallel implementation of the SP2 algorithm. This is undesirable in practice. The average computation time (in seconds) for each partitioning algorithm is shown in the last column (\"time\"). As pointed out by a referee, it would be interesting to investigate closer how the discrepancy of the block sizes from the mean influences the performance of SP2: this is left as future research.  
\begin{table}
\begin{tabular}{l l r r r r} \hline \hline
**Test System** & **Method** & **Sum** & **Min** & **Max** & **Time [s]** \\ \hline polyethylene dense crystal & METIS & 57,982,058,496 & 1536 & 1536 & 0.267175 \\ n = 18,432 & METIS + SA & 51,856,752,364 & 976 & 1536 & 0.347209 \\ m = 4,112,189 & HMETIS & 7,126,357,377,024 & 3840 & 9984 & 141.426 \\ p = 16 & HMETIS + SA & 1,362,943,612,944 & 2520 & 5814 & 141.79 \\  & KaHIP & 32,614,907,904 & 768 & 1536 & 0.7 \\  & KaHIP + SA & 32,614,907,904 & 768 & 1536 & 0.73 \\ \hline polyethylene sparse crystal & METIS & 24,461,180,928 & 1152 & 1152 & 0.024942 \\ n = 18,432 & METIS + SA & 24,461,180,928 & 1152 & 1152 & 0.030508 \\ m = 812,343 & HMETIS & 195,689,447,424 & 2304 & 354 & 55.9726 \\ p = 16 & HMETIS + SA & 170,056,587,295 & 2013 & 2299 & 55.9943 \\  & KaHIP & 24,461,180,928 & 1152 & 1152 & 0.07 \\  & KaHIP + SA & 24,461,180,928 & 1152 & 1152 & 0.08 \\ \hlinephenyl dendrimer & METIS & 336,049,081 & 150 & 409 & 0.13482 \\ n = 730 & METIS + SA & 146,550,740 & 0 & 382 & 0.14877 \\ m = 31,147 & HMETIS & 177,436,462 & 135 & 358 & 1.578 \\ p = 16 & HMETIS + SA & 118,409,940 & 0 & 358 & 1.59436 \\  & KaHIP & 231,550,645 & 55 & 381 & 1.72 \\  & KaHIP + SA & 116,248,715 & 0 & 324 & 1.74 \\ \hline \hline \end{tabular}
\end{table}
Table A1: Various test systems (first column) evaluated by different partition schemes (second column; number of vertices \(n\), edges \(m\), and blocks \(p\) are given). Results measured with the sum of cubes (3) criterion (sum), the sum of size and halo for the smallest CH-partition (min) as well as the biggest CH-partition (max), and the overall runtime (in seconds) are given in columns 3–6.
## 1 Introduction
Another thing is that iron (or nitride) nanocrystallites undergo phase transformations differently--in the order from the largest to the smallest with increasing (or decreasing) gas phase nitriding potential [58]. The phenomenon of hysteresis in chemical reactions was also observed [59; 82]. To sum up, the conducted research [85] showed that phase transformations in the discussed systems occur from nanocrystallites of the biggest active surfaces to those with the smallest, when the actual potential of the adsorbate is much higher from the equilibrium potential of a nanocrystallite of the biggest active surface. If these potentials are similar, the order in which the reaction occurs is reversed. Furthermore, \(\alpha\)-Fe(N) nanocrystallites can exist in equilibrium together with \(\gamma^{\prime}\)-Fe\({}_{4}\)N nanocrystallites (under constant gas phase nitriding potential) [86], which is understandable when we take into account the nanocrystallite size as an additional parameter in the Gibbs phase rule [58; 82]. Using the Crank equations [87], it was stated [88] that the chemical process rate can be limited by diffusion through the top layer of iron atoms on the nanocrystalline surface only. Values of nitrogen diffusion coefficient through the boundary layer of iron atoms was also estimated. The promoter-doped iron catalyst was also studied using the magnetic resonance method [89]. The study was carried out for samples with different sizes of nanocrystallites. This allowed the influence of medium-sized nanocrystallites on the magnetic properties of this material to be determined. Recently, an innovative method for the characterization of an iron catalyst under the conditions of a chemical reaction has been developed [90], consisting of the measurement of kinetic quantities with simultaneous measurement of magnetic permeability. The tests were carried out with the use of pressed samples of an industrial iron catalyst at a temperature of 350 \({}^{\circ}\)C.  
The goal of the present work is to study and explain the phenomenon observed in the nitriding process of the industrial iron ammonia synthesis catalyst under the reaction conditions, simultaneously characterizing this catalyst both in terms of the kinetics of the nitriding process and determining changes in its properties taking place in its volume by measuring changes in magnetic permeability. The result of the work will be to broaden the knowledge about changes in the volume of iron nanocrystals in the nitriding process based on the understanding of the relationship between kinetic and thermodynamic parameters and changes in the transformation process in the volume of nanocrystallites.
## 3 Results and Discussions
### Order Picking Performance
#### 3.2.5 The Effect of Order Quantity on the Cost Efficiency Level
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Warehouse 1**} & \multicolumn{4}{c}{**Warehouse 2**} & \multicolumn{4}{c}{**Warehouse 3**} & \multicolumn{4}{c}{**Warehouse 4**} & \multicolumn{1}{c}{**Total**} \\ \hline  & & **M** & **R** & **T** & **M** & **R** & **T** & **M** & **R** & **T** & **M** & **R** & **T** \\ \hline  & BPH & 0.80 & 0.73 & 0.76 & & & & & & & & 0.76 \\
200 & RFID & 0.80 & 0.73 & 0.76 & & & & & & & 0.76 \\  & VP & 1.00 & 0.90 & 0.93 & & & & & & & 0.94 \\ \hline  & & 0.87 & 0.79 & 0.81 & & & & & & & 0.82 \\ \hline  & & BPH & 0.83 & 0.78 & 0.80 & & & & & & & 0.80 \\
300 & RFID & 0.83 & 0.77 & 0.79 & & & & & & & 0.80 \\  & VP & 1.00 & 0.89 & 0.92 & & & & & & 0.94 \\ \hline  & & 0.89 & 0.82 & 0.84 & & & & & & & 0.85 \\ \hline  & & BPH & 0.89 & 0.87 & 0.87 & & & & & & & 0.87 \\
400 & RFID & 0.88 & 0.86 & 0.86 & & & & & & & 0.87 \\  & VP & 1.00 & 0.91 & 0.93 & & & & & & & 0.95 \\ \hline  & & 0.92 & 0.88 & 0.89 & & & & & & & & 0.90 \\ \hline  & & BPH & 0.99 & 0.99 & 0.80 & 0.76 & 0.82 & 0.79 & 0.76 & 0.83 & & & 0.86 \\
500 & RFID & 0.98 & 0.98 & 0.98 & 0.79 & 0.76 & 0.82 & 0.79 & 0.75 & 0.82 & & & 0.85 \\  & VP & 1.00 & 0.94 & 0.95 & 0.97 & 0.90 & 1.00 & 0.97 & 0.90 & 1.00 & & & 0.96 \\ \hline  & & 0.99 & 0.97 & 0.97 & 0.85 & 0.80 & 0.88 & 0.85 & 0.80 & 0.88 & & & 0.89 \\ \hline  & & BPH & & & 0.85 & 0.82 & 0.86 & 0.83 & 0.81 & 0.86 & 0.87 & 0.78 & 0.89 & 0.84 \\
600 & RFID & & & 0.84 & 0.82 & 0.86 & 0.83 & 0.81 & 0.85 & 0.86 & 0.77 & 0.89 & 0.84 \\  & VP & & & 0.96 & 0.90 & 1.00 & 0.97 & 0.89 & 1.00 & 0.97 & 0.87 & 1.00 & 0.95 \\ \hline  & & & & 0.88 & 0.85 & 0.91 & 0.88 & 0.84 & 0.90 & 0.90 & 0.81 & 0.93 & 0.88 \\ \hline  & & BPH & & & 0.92 & 0.92 & 0.92 & 0.90 & 0.91 & 0.86 & 0.80 & 0.88 & 0.89 \\
700 & RFID & & & 0.91 & 0.91 & 0.89 & 0.89 & 0.90 & 0.85 & 0.79 & 0.87 & 0.88 \\  & VP & & & 0.97 & 0.91 & 1.00 & 0.97 & 0.91 & 1.00 & 0.97 & 0.87 & 1.00 & 0.95 \\ \hline  & & & & 0.93 & 0.91 & 0.94 & 0.92 & 0.90 & 0.94 & 0.89 & 0.82 & 0.92 & 0.91 \\ \hline  & & BPH & & 1.00 & 1.00 & 1.00 & 0.99 & 0.99 & 0.99 & 0.90 & 0.89 & 0.91 & 0.96 \\
800 & RFID & & & 0.99 & 0.99 & 0.98 & 0.98 & 0.98 & 0.89 & 0.88 & 0.90 & 0.95 \\  & VP & & & 0.98 & 0.94 & 1.00 & 0.98 & 0.94 & 1.00 & 0.96 & 0.87 & 1.00 & 0.96 \\ \hline  & & & & 0.99 & 0.98 & 0.99 & 0.98 & 0.97 & 0.99 & 0.92 & 0.88 &Beyond that level, the warehouse managers will have to sacrifice cost efficiency in order to attain a 100% service level. However, due to a budget constraint and operation capacity, a 100% service level is not attainable. Therefore, the joint performance of routing policies and solution technologies also depends on the warehouse activity level.