esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Physicochemical Analysis
Sorption phenomenon is associated with wood species, porosity, and the toasting level [37], and may also explain the decrease in methanol and 2+3 metyl-1-butanol contents in aged samples, in comparison to the control sample. The PCA (standardized biplot of loadings and scores) made with the physicochemical data confirmed the above-mentioned differentiation between three and six months of ageing (Figure 2). The medium and medium plus toasting level were only separated by the PC2, which only explains 20% of the total variation. The two principal components explain 64.1% of the total variance, but the third component which only explains 15.8% of the variance (plot not shown) still separates the same aforementioned groups. The samples aged for three months, and samples aged with light toasting levels are placed at the positive side of component 1, which is related to high values of pH, acetaldehyde and 2+3-methylbutanan-1-ol. Samples aged for six months and samples with MPT and MT toasting levels are at the negative side of the PC1, which had a positive correlation with acidity, methanol content and dry extract. These results confirm and support the abovementioned observations. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Code**} & **butan-1-ol** & **propan-1-ol** & **2-methylpropan-1-ol** & **butan-2-ol** & **2+3-methylbutanan-1-ol** \\  & **(mg/L)** & **(mg/L)** & **(mg/L)** & **(mg/L)** & **(mg/L)** \\ \hline C & 1.79 \(\pm\) 0.02 & 84.88 \(\pm\) 0.05 & 200.57 \(\pm\) 0.34 & 1.66 \(\pm\) 0.11 & 839.77 \(\pm\) 1.34 * \\ LT3 & 1.85 \(\pm\) 0.31 a & 83.04 \(\pm\) 1.57 a & 197.53 \(\pm\) 1.56 a & 1.82 \(\pm\) 0.21 a & 825.07 \(\pm\) 4.98 b \\ MT3 & 1.96 \(\pm\) 0.06 a & 82.87 \(\pm\) 0.49 a & 197.18 \(\pm\) 1.66 a & 1.71 \(\pm\) 0.27 a & 824.44 \(\pm\) 6.22 b \\ MPT3 & 1.69 \(\pm\) 0.30 a & 82.35 \(\pm\) 12.2 a & 194.46 \(\pm\) 2.77 a & 1.86 \(\pm\) 0.06 a & 808.29 \(\pm\) 12.35 a \\ LT6 & 1.90 \(\pm\) 0.14 a & 83.39 \(\pm\) 0.28 a & 196.00 \(\pm\) 1.21 a & 2.17 \(\pm\) 0.02 a & 819.12 \(\pm\) 1.79 b \\ MT6 & 1.87 \(\pm\) 0.11 a & 83.59 \(\pm\) 0.87 a & 196.45 \(\pm\) 1.53 a & 2.08 \(\pm\) 0.21 a & 822.22 \(\pm\) 8.05 b \\ MPT6 & 1.86 \(\pm\) 0.21 a & 83.61 \(\pm\) 0.59 a & 197.79 \(\pm\) 2.21 a & 1.70 \(\pm\) 0.31 a & 810.18 \(\pm\) 7.94 a \\ \hline \hline \end{tabular}  
* Control denotes significant differences from the other modalities. Means within the same column followed by different letters are significantly different (\(p\) < 0.05).  
\end{table} 
Table 3: Mean and Standard deviation of higher alcohol of the AUS aged with oak wood. Figure 2: Principal component analysis of the physicochemical determination, and significant volatile compounds of _Arbutus unado_ spirits aged with oak, with three different toasting levels (LT-AUS aged in light toasting oak wood; MT-AUS aged in medium toasting oak wood; MPT-aged with medium plus toast oak wood) during three and six months. M—Methanol (g/mL); Ac—Acetaldehyde (mg/L); 23M1B—2+3-methylbutanan-1-ol (mg/L); DS—dry extract (g/L); VA—Volatile acidity; TA–total acidity; FA–fixed acidity.
# An Energy Balanced and Lifetime Extended Routing Protocol for Underwater Sensor Networks
## 4 Energy Balanced and Lifetime Extended Protocol (EBLE)
### Detailed Design of EBLE
#### 4.2.2 Data Transmission Phase  
In data transmission phase, the sensor nodes mainly have two operations: forwarding data packets and updating residual energy levels. The data packets in a node are either sensed by the node's sensor or received from other nodes. The unique ID of the chosen forwarding node is included in data packet header and the receivers can check if it is eligible to forward the data packet. When a node is responsible for sending a data packet, it first checks its neighbor list and the sink node's location. There are three cases in data forwarding process: (1) the EL of the sender is smaller than or equal to at least one of its neighbors' EL; (2) the EL of the sender node is larger than that of all its neighbors and the sink node is within its maximum transmission range; (3) the EL of the sender is larger than that of all its neighbors and the sink node is located outside its maximum transmission range. In the first case, a subset of neighbors with larger or equal EL is constructed first. Then the neighborhood node with the minimal \(Q\) value in this subset is chosen as the next hop forwarder. In the second case, the data packet is just sent to the sink node directly. In the third case, a subset of neighbors with the largest EL is chosen first and then the node with the minimal \(Q\) in this subset is chosen as the next hop forwarder. After choosing the next hop node, the data packet header is refreshed with the updated node ID and data packet is sent to the chosen node. The detailed process of data forwarding is shown in Algorithm 2. The relative parameters are also defined in Table 1. After a period of time, the residual energy level may change and nodes need to inform their neighbors of their EL changes. Each time a node detects an EL change, it broadcasts an EL notice packet with its ID and EL. Then the receivers can update their neighbors' EL according to the new received notice packet.  
```
1:procedureSendData
2:\(Node_{opt}.EL\gets 0\);
3:\(Node_{opt}.Q\leftarrow+\infty\);
4:\(Node_{opt}.ID\gets Null\);
5:for\(m\gets 1\)to\(Num_{nb}\)do
6:if\(Node_{opt}.EL<EL(i)\)then
7:if\(Node_{opt}.EL<NbList(m).EL\)then
8:\(Node_{opt}\gets NbList(m)\);
9:elseif\(Node_{opt}.EL=NbList(m).EL\) and \(NbList(m).Q<Node_{opt}.Q\)then
10:\(Node_{opt}\gets NbList(m)\);
11:endif
12:elseif\(NbList(m).EL\geq EL(i)\) and \(NbList(m).Q<Node_{opt}.Q\)then
13:\(Node_{opt}\gets NbList(m)\);
14:endif
15:endfor
16:if\(Node_{opt}.EL<EL(i)\) and \(TR\geq d(i,sink)\)then
17:\(Node_{opt}.ID\gets SinkID\);
18:endif
19:if\(Node_{opt}.ID=Null\)then
20: drop the packet;
21:else
22: Update data packet header with \(Node_{opt}.ID\);
23: Broadcast the packet;
24:endif
25:endprocedure
```  
**Algorithm 2** Data Forwarding Process
## 3 Results and Discussion
### Adsorption Behavior of GO-MIPs for 4-MEC
#### 3.2.3 Adsorption Selectivity  
The experiment on the selective adsorption of the GO-MIPs towards 4-MEC was conducted by comparing it with the selective adsorption of GO-MIPs towards 4-MMC,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{3}{c}{**Langmuir**} & \multicolumn{3}{c}{**Freundlich**} \\ \cline{2-7}  & \(Q_{m}\) **(mg g\({}^{-1}\))** & \(K_{L}\) **(L mg\({}^{-1}\))** & \(\mathbf{R}^{2}\) & \(K_{F}\) & \(n\) & \(\mathbf{R}^{2}\) \\ \hline GO-MIPs & 28.27 & 0.07989 & 0.9772 & 3.5323 & 2.0306 & 0.9842 \\ \hline GO-NIPs & 11.02 & 0.03797 & 0.9768 & 0.6024 & 1.5356 & 0.9489 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Langmuir and Freundlich model adsorption constants of GO-MIPs and GO-NIPs for the adsorption of 4-MEC.  
Figure 5: Adsorption isotherms (**a**), Langmuir (**b**), and Freundlich (**c**) isotherm models of GO-MIPs and GO-NIPs.  
4-MDMC, THF-F, and MO. Among the four potential interferents, 4-MMC and 4-MDMC, which are both synthetic cathinones, are similar to 4-MEC in chemical structure to a certain extent. As shown in Figure 6a, the results prove that the selectivity performance of GO-MIPs towards 4-MEC is better than that of GO-MIPS towards the other four compounds, with a higher adsorption capacity. On the other hand, the adsorption capacity of GO-NIPs towards the five compounds is very small and shows no difference between the compounds, indicating that GO-NIPs have no selectivity. According to the above result, the template 4-MEC could been recognized by the specific cavities with molecular shape memory on the GO-MIPs, suggesting a high adsorption selectivity.
## 3 Results
### Responses of the SIF-IWUE Relationship to the Environmental Factors
#### 3.3.3 Effects of Air Temperature on IWUE-SIF Correlations  
Too high air temperature reduces the activation of RuBISCO and thus photosynthesis rates. Under heat stress, plants tend to release more absorbed energy by NPQ, which may weaken SIF emission and photosynthesis activities [32]. Also, water loss through transpiration may increase to maintain the energy balance in plants when the water supply is not limiting [40]. Both of the two mechanisms may deteriorate the relationship between SIF emission and IWUE. To analyze the influences of \(T_{\text{air}}\) on the SIF-IWUE relationship, we calculated the \(R^{2}\) between SIF emission and IWUE for three \(T_{\text{air}}\) groups. Hourly \(T_{\text{air}}\) between 7 am and 5 pm. local time were separated into three groups: (1) low: \(T_{\text{air}}\leq\) 14 \({}^{\circ}\)C, (2) medium: 14 \({}^{\circ}\)C \(<\)\(T_{\text{air}}\) \(<\) 22 \({}^{\circ}\)C, and (3) high: \(T_{\text{air}}\geq\) 22 \({}^{\circ}\)C.  
When LR models are used, the predictive power of SIF is negatively related to \(T_{\text{air}}\). For example, SIF\({}_{761}\) can explain about 40% of the variance in IWUE in the low \(T_{\text{air}}\) group, but its performance decreases to 27% for high \(T_{\text{air}}\) (Figure 5a). The combination of SIF\({}_{687}\), SIF\({}_{720}\), and SIF\({}_{761}\) also benefits from low \(T_{\text{air}}\) with an \(R^{2}\) of 0.48 and 0.32 for the low and high \(T_{\text{air}}\), respectively (Figure 5a). However, we find that the SIF-IWUE correlation tends to increase with an increasing \(T_{\text{air}}\) when GPR models are used (Figure 5b). For instance, SIF\({}_{761}\) show \(R^{2}\) = 0.41 and 0.47 in the low and high \(T_{\text{air}}\), respectively (Figure 5b). Further, the SIF-IWUE correlations demonstrate more increases with \(T_{\text{air}}\) when the band combinations are used, as the combination of SIF\({}_{687}\), SIF\({}_{720}\), and SIF\({}_{761}\) has \(R^{2}\) = 0.55, 0.62 and 0.57 with increasing in \(T_{\text{air}}\) (Figure 5b). The different responses of IWUE to \(T_{\text{air}}\) yielded by LR and GPR show that SIF-IWUE relationships become more nonlinear with increasing \(T_{\text{air}}\).  
Figure 5: The effect of air temperature (\(T_{\text{air}}\), \({}^{\circ}\)C) on the coefficients of determination (\(R^{2}\)) between IWUE and the selected single SIF bands and their combinations by using **(a)** linear regression analysis and **(b)** Gaussian processes regression, respectively. The blue, green and yellow bars represent \(R^{2}\) from data with \(T_{\text{air}}\leq\) 14 \({}^{\circ}\)C, 14 \({}^{\circ}\)C \(<\)\(T_{\text{air}}\) \(<\)\(22\)\({}^{\circ}\)C, and \(T_{\text{air}}\geq\) 22 \({}^{\circ}\)C, respectively. All data have an hourly time resolution and belong to the period between 7 a.m. and 5 p.m. local time.
# Multi-Line Prefetch Covert Channel with Huge Pages
## 4 The Design of a Multi-Line Prefetch Covert Channel with Huge Pages
### Baseline Performance Comparison with Prefetching and Huge Pages  
To establish a baseline for evaluating the performance impact of huge pages and software prefetching, we measure the average memory access latency over a 32 KB region (comprising 512 cache lines, each 64 bytes in size). As shown in Algorithm 1, we test four configurations: with and without huge pages and with and without software prefetching. The resulting latency measurements are summarized in Table 1.  
```
1:for all lines in memory buffer do
2:if prefetching enabled then
3: Prefetch(line)
4:endif
5: read_start_time \(\leftarrow\) ReadTime()
6: Read(line)
7: read_end_time \(\leftarrow\) ReadTime()
8: elapsed_time \(\leftarrow\) elapsed_time + read_end_time - read_start_time \(\triangleright\) Accumulate(elapsed_time)
9:endfor
```  
**Algorithm 1** Timing measurement per cache line  
* Prefetching Only: Applying software prefetching without huge pages results in a 12% latency reduction. The prefetch instruction (__builtin_prefetch()) helps bring the cache lines closer to the processor ahead of access, thereby reducing stall Huge Pages Only: Enabling huge pages alone reduces the average access latency by approximately 14% compared to that at the baseline. This is primarily due to the reduced TLB pressure and improved memory translation efficiency provided by 2 MB page mappings.
* Combined Optimization: The combination of huge pages and prefetching yields the lowest average latency (3.07 cycles). This configuration effectively leverages both a reduced TLB pressure from huge pages and improved cache readiness from prefetching, making it the most efficient strategy for minimizing the access latency in our setup.
# FL-SDUAN: A Fuzzy Logic-Based Routing Scheme for Software-Defined Underwater Acoustic Networks
## 2 Related Work
### The Fuzzy Logic-Based Clustering Techniques in ULANs  
[PERSON] et al. [18] designed a fuzzy logic-based clustering scheme for hydroacoustic networks. There was a significant difference between detecting malicious nodes and delivering packets. [PERSON] et al. [19] proposed a fuzzy inference-based clustering mechanism that could reduce and prolong the survival time of UANs. [PERSON] et al. [20] introduced a fuzzy logic-based clustering algorithm to select cluster heads (CHs) in hydroacoustic networks. [PERSON] et al. [21] proposed a fuzzy logic clustering method to identify the best CH in hydroacoustic networks. [PERSON] et al. [22] proposed a fuzzy clustering algorithm for hydroacoustic networks. The experimental results showed that this protocol minimized energy consumption and latency while increasing throughput and transmission rate. [PERSON] et al. [23] implemented a fuzzy clustering algorithm based on the analytic hierarchy process (AHP) in a hydroacoustic network. [PERSON] et al. [24] used a fuzzy algorithm for CH selection to extend the stability and depletion rate of UANs. The experimental results showed that their method outperformed other methods.
## 3 Results
### Reactive Strength Index, Average Power Output, and Vertical Jump Height  
Repeated-measures ANOVA revealed significant main effects and interaction effects of both time and velocity on all three variables (\(p<0.05\)). These findings indicate that both high-speed and medium-speed flywheel intensities successfully induced PAP, while low-speed intensity demonstrated limited efficacy (see Table 4 and Figure 2A-F).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Intensity** & **Time** & **RSI (m/s)** & **d vs. Baseline** & **APO (W/kg)** & **d vs. Baseline** & **VJH (m)** & **d vs. Baseline** \\ \hline Baseline & & 1.02 \(\pm\) 0.31 & - & 34.48 \(\pm\) 4.64 & - & 0.46 \(\pm\) 0.03 & - \\  & 30 s & 0.92 \(\pm\) 0.25 & 0.35 [\(-\)0.09, 0.79] & 31.93 \(\pm\) 4.81 * & 0.56 [0.12, 1.00] & 0.41 \(\pm\) 0.04 * & 1.38 [0.94, 1.82] \\ Fast speed & 4 min & 1.18 \(\pm\) 0.32 * & 0.53 [0.09, 0.97] & 37.65 \(\pm\) 4.46 * & 0.69 [0.25, 1.13] & 0.54 \(\pm\) 0.04 * & 2.41 [1.97, 2.85] \\  & 8 min & 1.17 \(\pm\) 0.3 * & 0.49 [0.05, 0.93] & 37.57 \(\pm\) 4.37 * & 0.67 [0.23, 1.11] & 0.54 \(\pm\) 0.03 * & 2.78 [2.34, 3.22] \\  & 12 min & 1.17 \(\pm\) 0.31 * & 0.49 [0.05, 0.93] & 37.44 \(\pm\) 4.46 * & 0.65 [0.21, 1.09] & 0.54 \(\pm\) 0.03 * & 2.73 [2.29, 3.17] \\  & 30 s & 0.9 \(\pm\) 0.28 * & 0.42 [\(-\)0.02, 0.86] & 32.05 \(\pm\) 4.28 * & 0.52 [0.08, 0.96] & 0.41 \(\pm\) 0.03 * & 1.53 [1.09, 1.97] \\  & 4 min & 1.1 \(\pm\) 0.34 * & 0.25 [\(-\)0.19, 0.69] & 36.2 \(\pm\) 4.18 * & 0.37 [\(-\)0.07, 0.81] & 0.51 \(\pm\) 0.04 * & 1.32 [0.88, 1.76] \\ Medium speed & 8 min & 1.1 \(\pm\) 0.32 * & 0.25 [\(-\)0.19, 0.69] & 36.27 \(\pm\) 4.22 * & 0.38 [\(-\)0.06, 0.82] & 0.50 \(\pm\) 0.03 * & 1.21 [0.77, 1.65] \\  & 12 min & 1.09 \(\pm\) 0.34 * & 0.23 [\(-\)0.21, 0.67] & 36.06 \(\pm\) 4.49 * & 0.35 [\(-\)0.09, 0.79] & 0.50 \(\pm\) 0.04 * & 1.11 [0.67, 1.55] \\  & 30 s & 0.86 \(\pm\) 0.28 * & 0.52 [0.08, 0.96] & 32.38 \(\pm\) 4.56 * & 0.45 [0.01, 0.89] & 0.39 \(\pm\) 0.04 * & 1.89 [1.45, 2.33] \\  & 4 min & 1.0 \(\pm\) 0.29 * & 0.07 [\(-\)0.37, 0.50] & 34.33 \(\pm\) 4.26 * & 0.09 [\(-\)0.35, 0.53] & 0.45 \(\pm\) 0.03 * & 0.01 [\(-\)0.43, 0.45] \\ Slow speed & 8 min & 1.0 \(\pm\) 0.33 * & 0.06 [\(-\)0.38, 0.50] & 34.21 \(\pm\) 4.4 * & 0.07 [\(-\)0.37, 0.51] & 0.45 \(\pm\) 0.04 * & 0.01 [\(-\)0.43, 0.45] \\  & 12 min & 1.01 \(\pm\) 0.45 & 0.04 [\(-\)0.40, 0.48] & 34.18 \(\pm\) 4.52 * & 0.06 [\(-\)0.38, 0.50] & 0.45 \(\pm\) 0.03 * & 0.01 [\(-\)0.43, 0.45] \\ \hline \end{tabular} Note: * indicates a significant difference compared to the baseline value; * indicates a significant difference compared to the 30 s mark; RSI: reactive strength index, APO = average power output, VJH: vertical jump height, Effect sizes (Cohen’s d) are reported with 95% confidence intervals (95% CI). The statistical power (1 \(-\)\(\beta\)) for main effects ranged from 0.55 to 0.88.  
\end{table}
Table 4: Average power output, reactive strength index, and vertical jump height across time points and velocity conditions.  
Figure 2: _Cont._  
Figure 2: _Cont_.
# The Effect of Different Deep Network Architectures upon CNN-Based Gaze Tracking
###### Abstract  
In this paper, we explore the effect of using different convolutional layers, batch normalization and the global average pooling layer upon a convolutional neural network (CNN) based gaze tracking system. A novel method is proposed to label the participant's face images as gaze points retrieved from eye tracker while watching videos for building a training dataset that is closer to human visual behavior. The participants can swing their head freely; therefore, the most real and natural images can be obtained without too many restrictions. The labeled data are classified according to the coordinate of gaze and area of interest on the screen. Therefore, varied network architectures are applied to estimate and compare the effects including the number of convolutional layers, batch normalization (BN) and the global average pooling (GAP) layer instead of the fully connected layer. Three schemes, including the single eye image, double eyes image and facial image, with data augmentation are used to feed into neural network to train and evaluate the efficiency. The input image of the eye or face for an eye tracking system is mostly a small-sized image with relatively few features. The results show that BN and GAP are helpful in overcoming the problem to train models and in reducing the amount of network parameters. It is shown that the accuracy is significantly improved when using GAP and BN at the mean time. Overall, the face scheme has a highest accuracy of 0.883 when BN and GAP are used at the mean time. Additionally, comparing to the fully connected layer set to 512 cases, the number of parameters is reduced by less than 50% and the accuracy is improved by about 2%. A detection accuracy comparison of our model with the existing George and Routray methods shows that our proposed method achieves better prediction accuracy of more than 6%.  
Keywords: gaze tracking; convolution neural network; batch normalization; global average pooling layer +
Footnote †: journal:
# The Influence of Immersive Virtual Reality Systems on Online Social Application
## 5 Result
### Online Social Experience  
In addition to questions about user satisfaction, the questionnaire also included five questions about functional effects and user experience, in order to obtain subjective feedback from volunteers on social functions of the system. The five questions are  
**Question 6:**: Is expression of intent limited? (high score means more freedom)
**Question 7:**: Is expression of intent accurate? (high score means more accurate)
**Question 8:**: Are there various ways of expressing intentions? (high score means higher variability)
**Question 9:**: Is social process natural? (high score means more natural)
**Question 10:**: How similar is it to a real face-to-face social situation? (high score means more similar)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **RVM-IVRSA** & **WeChat** & \(p\)**-Value** \\  & **Mean/SD** & **Mean/SD** & \(p\)**-Value** \\ \hline
**Comfort** & 3.66/0.59 & 4.60/0.66 & 0.02 \\
**Stability** & 4.40/0.32 & 4.63/0.24 & 0.39 \\
**Efficiency** & 4.08/0.59 & 3.77/0.54 & 0.04 \\
**Popularization** & 3.31/0.65 & 3.72/0.72 & 0.04 \\
**Improvement** & 2.45/0.54 & 3.79/0.31 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 1: User satisfaction score.  
In terms of user intent expression, we assumed that RVM-IVRSA should be superior to traditional social software across the board, and differences between the two should be significant, indicating that RVM-IVRSA's social approach is subversive. The results of the above five questions are given in Table 2.  
In this part of the experiment, we assumed that the feedback results of RVM-IVRSA were better than WeChat in all aspects, and that there was a big gap. The experimental results basically confirmed our hypothesis.  
Table 2 shows that the system had obvious functional advantages. Using voice and body language can express users' intention more freely and accurately. Throughout the process, volunteers behaved naturally and communicated efficiently. It was worth mentioning that some volunteers put forward some shortcomings of virtual space scene interaction and modeling quality.  
Compared with user feedback from WeChat, RVM-IVRSA features a comprehensive advantage in user's social experience. The significant differences among five sets of data reflected the subversive impact of face-to-face social simulation on the online social field.
_Applied_  
_sciences_  
_Article_  
Application of a Portable Colorimeter for Reading a Radiochromic Film for On-Site Dosimetry  
[PERSON] 1,*\({}^{\star}\) and [PERSON] 2  
1 Department of Radiation Biophysics, Research Institute for Radiation Biology and Medicine, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8553, Japan  
2 School of Medicine, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8553, Japan  
* Correspondence: [EMAIL_ADDRESS]  
Received: 16 March 2023  
Received: 7 April 2023  
Accepted: 7 April 2023  
Published: 10 April 2023  
**Key words:** Radiochromic films have widely been used for quality assurance (QA) in radiation therapy and have many advantageous features such as self-developing visible coloration, wide dose range and easiness to handle. These features have a good potential for application to other fields associated with high-dose radiation exposure, e.g., verification of various radiation sources used in industry and research, occupational radiation monitoring as a preparedness for radiological emergencies. One of the issues in such applications is the elaborate process of acquisition and analyses of the color image using a flatbed scanner and image processing software, which is desirably to be improved for achieving a practical on-site dosimetry. In the present study, a simple method for reading a radiochromic film by using a portable colorimeter (mix pro 2; abbreviated here \"Nix\") was proposed and its feasibility for diagnostic X-rays was tested with a commercial radiochromic film (Gafchromic EBT-XD). It was found that the color intensities of red and green components of EBT-XD were successfully measured by Nix over a wide dose range up to 40 Gy. Though some angle dependence was observed, this error could be well averted by careful attention to the film direction in a reading process.  
According to these findings, it is expected that the proposed on-site dosimetry method of combining a radiochromic film and a portable colorimeter will be practically utilized in various occasions.  
**Keywords:** radiochromic; gafchromic; film; EBT-XD; portable colorimeter; mix; X-rays