esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Analysis
### Experimental Setup
#### 5.1.2 Case 2: Observational Study with Bias  
This case simulates a common scenario, in which data suffer from selection bias. For the generation of the dataset, we created two groups of patients, each one comprising 500 individuals. The difference compared to the unbiased scenario is that 80% of the patients that belong to the high-intensity exercise group (i.e., treatment group) have been prescribed calcium supplementation. In contrast, only 20% of the patients who belong to the low-intensity exercise group (i.e., control group) receive calcium supplementation. In general, clinicians and medical researchers suggest that a reasonable proportion of patients who should receive calcium supplementation is typically between 50% and 80% [67], especially in populations at higher risk of bone density loss, such as older adults or postmenopausal women. The clinical experts from the REBECCA project suggested that in the generated synthetic data, 80% of the patients that belong to the high-intensity exercise group (i.e., treatment group) should be receiving calcium supplementation, as this aligns with current guidelines and practices aimed at maximizing bone health outcomes in these vulnerable populations [68].  
In this case, the direct calculation of the ATE is 0.87, which is an overestimation of the ATE compared to the real 0.58 value; hence, \(|\epsilon_{\text{ATE}}|=0.29\). This result is reasonable, since in this case the ATE measures the combined effect of a higher concentration of calcium in the blood along with high-intensity physical activity, leading to a higher reduction in BMD loss.  
Finally, the PEHE is 2.74 and 2.72 for the train and the test set for both non-imputed and imputed datasets, respectively.  
For the selection of the variables used to conduct the experiments based on Cases 1 and 2, we use the DAG in Figure 2, developed for the use case of osteopenia and osteoporosis. In detail, our decision rests on the condition that the selected variables should satisfy the backdoor criterion [23].  
The subset of the selected variables are as follows: \"Exercise\", \"PTH\", \"Age\", \"Calcium in blood\", \"C-Telopeptides\", \"Alcohol consumption\", \"Calcium supplement\", \"Calcitonin\", \"Nutrition rich in calcium\", \"Vit.D in Blood\" as well as \"Cigarettes per day\".  
Finally, the target variable is the Bone Turnover, which directly leads to BMD loss.
## 3 Governing Equations
### Mathematical Model
\begin{split}\vec{t}=\frac{tU_{Lid}}{L},\vec{p}=\frac{P}{\rho U_ {Lid}^{2}},\vec{y}=\frac{y}{L},\vec{x}=\frac{x}{L},\\ \vec{v}=\frac{v}{U_{Lid}},\vec{u}=\frac{u}{U_{Lid}},\theta=\frac{ T-T_{0}}{\
abla\,T},\
abla\,T=T_{1}-T_{0},\\ \overline{\varphi}=\frac{\varphi-\varphi_{0}}{\
abla\varphi}, \
abla\varphi=\varphi_{1}-\varphi_{0},\vec{q}=\frac{q}{q_{0}},\vec{E}=\frac{ E}{E_{0}}\end{split}  \tag{14}\]  
In order to reach clear formulation, over bar will be deleted in next equations. So, the governing equations can be considered as follows:  
\[ \begin{split}\left[\
abla(\vec{\overset{\rightarrow}{V}})=0 \right.\\ \left(\left[\frac{\partial\vec{\overset{\rightarrow}{V}}}{ \partial t}+\left(\vec{\overset{\rightarrow}{V}}.\
abla\right)\vec{\overset{ \rightarrow}{V}}\right]=-\
abla p+\frac{\rho_{n_{f}}/\rho_{f}}{\mu_{n_{f}}/\mu _{f}}\frac{1}{\text{Re}}\
abla^{2}\vec{\overset{\rightarrow}{V}}+\frac{S_{ E}}{\rho_{n_{f}}/\rho_{f}}q\vec{E}\\ \left(\frac{\partial\theta}{\partial t}+\left(\vec{\overset{ \rightarrow}{V}}.\
abla\right)\theta\right)=\frac{k_{n_{f}}/k_{f}}{\left( \rho C_{p}\right)_{n_{f}}/\left(\rho C_{p}\right)_{f}}\frac{1}{\text{RePr}} \
abla^{2}\theta+\frac{S_{E}Ec}{\left(\rho C_{p}\right)_{n_{f}}/\left(\rho C_{ p}\right)_{f}}\left(\vec{\overset{\rightarrow}{J}}.\vec{E}\right)\\ \
abla.\vec{\overset{\rightarrow}{J}}+\frac{\partial q}{ \partial t}=0\\ \
abla\varepsilon\vec{E}=q\\ \overset{\rightarrow}{E}=-\
abla\varphi\end{split}  \tag{15}\]  
The formulas of vorticity and stream function are:  
\[ \begin{split}\omega=\frac{\partial v}{\partial x}-\frac{\partial u }{\partial y},\\ v=-\frac{\partial\psi}{\partial x},u=\frac{\partial\psi}{ \partial y},\\ \Omega=\frac{\omega L}{U_{Lid}},\Psi=\frac{\psi}{LU_{Lid}}\end{split}  \tag{16}\]  
Continuity equation has been satisfied by the stream function. By eliminating pressure between x-momentum and y-momentum, the vorticity equation can be obtained. \(Nu_{loc}\) and \(Nu_{ave}\) along the lid wall can be obtained as:  
\[Nu_{loc}=\left(\frac{k_{n_{f}}}{k_{f}}\right)\frac{\partial\Theta}{\partial Y} \tag{17}\]_Appl. Sci._**2015**, \(5\)  
\[Nu_{av}=\frac{1}{L}\int_{0}^{L}Nu_{loc}\;dX \tag{18}\]
# Transient Responses of Repeated Transverse Impacts on Beams
## 3 Solution of Transient Impact Responses by Eigenfunction Expansion Method
### Equation of Motion of the Beam  
As shown in Figure 1, a beam is simply supported at two ends and a dynamic force \(F(t)\) is applied at the middle of the beam, where \(L\) is the length, \(b\) is the width, \(h\) is the height, \(E_{b}\) is the Young's modulus, \(I\) is the cross sectional moment of inertia and \(\rho_{b}\) is the mass density. The beam is assumed as a Bernoulli-Euler beam, which has the following equation of motion,  
\[\rho_{b}\Lambda\frac{\partial^{2}w_{b}(x,t)}{\partial t^{2}}+E_{b}I\frac{ \partial^{4}w_{b}(x,t)}{\partial x^{4}}-F(t)\delta(x-L/2)\ =\ 0 \tag{4}\]  
where \(w_{b}(x,t)\) is the deflection of the beam, \(\delta(x)\) is the Dirac delta function. For the simply supported beam, the boundary conditions are:  
\[w_{b}(0,t)\ =\ 0,\ w_{b}(L,t)\ =\ 0\]  
\[\frac{\partial^{2}w_{b}(0,t)}{\partial x^{2}}\ =\ 0,\ \frac{\partial^{2}w_{b}(L,t)}{ \partial x^{2}}\ =\ 0\]
## 3 A Machine Learning Approach for Predicting Dementia
### The Feature Selection Approach
#### 3.3.3 The Proposed Method (NCA-F)  
This research proposes a new approach referred to as NCA-F (Neighborhood Component Analysis and correlation-based Filtration method). It uses a combination of filtering, [PERSON]'s correlation coefficient, and a wrapper method in the feature selection process. Firstly, irrelevant features are excluded based on [PERSON]'s correlation coefficient having an absolute value greater than 0.9. After the filtration process, the selected features are further processed using the NCA method. NCA aims to assign weights to each feature based on its classification performance. NCA-F has used the nearest neighbor classifier for checking the performance of different combinations of features and then assigning weights to each feature. These weightedfeatures are then sorted in descending order and used in the model for the prediction of dementia. This research has separated neuroimaging measures (neuroimaging features) and cognitive test scores (cognitive features) and combined both of these features (combined features) to analyze the impact of both features on dementia detection together and separately. These features are shown in Tables 1 and 2 after applying the feature filtration method with their weights. Although in a typical ML model, age is treated as a demographic, in AD prediction, age is an important factor and is treated as a cognitive feature by ADNIMERGE. \"Combined features\" are the combination of these two tables.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S. No.** & **Feature** & **Weight** \\ \hline
1 & ICV & 4.981 \\ \hline
2 & Ventricles & 4.1487 \\ \hline
3 & WholeBrain & 3.6149 \\ \hline
4 & mPACCdigit & 2.986 \\ \hline
5 & Hippocampus & 2.9641 \\ \hline
6 & MidTemp & 2.952 \\ \hline
7 & Fusiform & 2.662 \\ \hline
8 & Entorhinal & 1.8558 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The list of filtered neuroimaging features after NCA-F.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S. No** & **Feature** & **Weight** \\ \hline
1 & AGE & 15.288 \\ \hline
2 & EcogSPPlan & 3.0912 \\ \hline
3 & PTGENDER & 2.9801 \\ \hline
4 & CDRSB & 2.6719 \\ \hline
5 & EcogSPOrgan & 2.345 \\ \hline
6 & EcogSPLang & 2.18 \\ \hline
7 & ADAS11 & 2.0056 \\ \hline
8 & RAVLTimmediate & 1.9877 \\ \hline
9 & ADASQ4 & 1.7356 \\ \hline
10 & MMSE & 1.565 \\ \hline
11 & EcogPtOrgan & 1.5628 \\ \hline
12 & EcogSPVisspat & 1.5223 \\ \hline
13 & LDELTOTAL & 1.1009 \\ \hline
14 & EcogPtLang & 1.0243 \\ \hline
15 & FAQ & 0.91021 \\ \hline
16 & MOCA & 0.79935 \\ \hline
17 & EcogPtTotal & 0.75844 \\ \hline
18 & EcogPtDivatt & 0.72765 \\ \hline
19 & EcogSPMem & 0.33403 \\ \hline
20 & EcogPtMem & 0.14011 \\ \hline
21 & TRABSCOR & 0.024946 \\ \hline
22 & EcogSPDivatt & 0.0033972 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The list of filtered cognitive features after NCA-F.
## 3 Error Model and Cross-Identification Algorithm
### Geometric Parameter Error Model
#### 3.1.4. TM-MDH Error Model  
In Equation (19), \(C_{u},C_{v}^{\prime}\) and \(D_{w_{i}}^{\prime}\) follow the form of Equation (5). Similarly, \(\delta_{m}^{t}T\) also follows the same form, specifically:  
\[\delta_{m}^{t}T=\left[\begin{array}{cccc}0&-{}_{m}^{t}\delta_{x}&{}_{m}^{t }\delta_{y}&{}_{m}^{t}d_{x}\\ {}_{m}^{t}\delta_{x}&0&-{}_{m}^{t}\delta_{z}&{}_{m}^{t}d_{y}\\ -{}_{m}^{t}\delta_{y}&{}_{m}^{t}\delta_{z}&0&{}_{m}^{t}d_{z}\\ 0&0&0&0\end{array}\right] \tag{22}\]  
where \({}_{m}^{t}d\) is equal to \(\left[{}_{m}^{t}d_{x^{\prime}}{}_{m}^{t}d_{y^{\prime}}{}_{m}^{t}d_{y}{}_{m}^{t }d_{y}\right]^{T}\),which represents the differential translation of the TCF relative to the MCF. \({}_{m}^{t}\delta\) is equal to \(\left[{}_{m}^{t}\delta_{z^{\prime}}{}_{m}^{t}\delta_{y^{\prime}}{}_{m}^{t} \delta_{x}\right]^{T}\), which represents the differential rotation of the TCF relative to the MCF.  
Set the errors of the link parameters as \(\Delta\theta=\left[\Delta\theta_{1}\;\;\;\cdots\;\;\;\Delta\theta_{n}\right]^{T}\), \(\Delta d=\left[d_{1}\;\;\;\cdots\;\;\;d_{n}\right]^{T}\), \(\Delta a=\left[a_{1}\;\;\;\cdots\;\;\;a_{n}\right]^{T}\), \(\Delta\alpha=\left[a_{1}\;\;\;\cdots\;\;\;a_{n}\right]^{T}\), and \(\Delta\beta=\left[\beta_{1}\;\;\;\cdots\;\;\;\beta_{n}\right]^{T}\). Based on Equation (19), the geometric parameters error model (TM-MDH) can be established as follows:  
\[\left[\begin{array}{cccc}{}_{m}^{t}d\\ {}_{m}^{t}\delta\end{array}\right]=\left[\begin{array}{cccc}M_{\theta}&M_{d}& M_{a}&M_{\theta}&E_{u}&F_{v}\\ M_{\theta}^{\prime}&M_{d}^{\prime}&M_{a}^{\prime}&M_{\theta}^{\prime}&M_{\beta}^ {\prime}&E_{u}^{\prime}&F_{v}^{\prime}\\ \end{array}\right]\left[\begin{array}{c}\Delta\theta\\ \Delta d\\ \Delta a\\ \Delta\beta\\ \Delta u\\ \Delta v\end{array}\right] \tag{23}\]\[\begin{array}{l}M_{w}=\left[D^{\prime}_{w_{1}}(t),\cdots,D^{\prime}_{w_{n}}(t) \right]\\ M^{\prime}_{w}=\left[D^{\prime}_{w_{1}}(r),\cdots,D^{\prime}_{w_{n}}(r)\right] \\ E_{u}=\left[C_{x}(t),C_{y}(t),C_{z}(t),C_{\alpha}(t),C_{\beta}(t),C_{\gamma}(t) \right]\\ E^{\prime}_{u}=\left[C_{x}(r),C_{y}(r),C_{z}(r),C_{\alpha}(r),C_{\beta}(r),C_{ \gamma}(r)\right]\\ F_{v}=\left[C^{\prime}_{x^{\prime}}(t),C^{\prime}_{y^{\prime}}(t),C^{\prime}_{ z^{\prime}}(t),C^{\prime}_{y^{\prime}}(t),C^{\prime}_{y^{\prime}}(t)\right]\\ F^{\prime}_{v}=\left[C^{\prime}_{x^{\prime}}(r),C^{\prime}_{y^{\prime}}(r),C^{ \prime}_{z^{\prime}}(r),C^{\prime}_{x^{\prime}}(r),C^{\prime}_{\beta^{\prime }}(r),C^{\prime}_{\gamma^{\prime}}(r)\right]\end{array} \tag{24}\]  
where \(w\in\{\theta,d,a,\alpha,\beta\}\). \(t\) is the column vector composed of the elements of the corresponding matrix at (1, 4), (2, 4), and (3, 4). \(r\) is the column vector composed of the elements of the corresponding matrix at (1, 2), (1, 3), and (2, 3). \(M_{w}\) and \(M^{\prime}_{w}\) are 3 \(\times\) n matrices. \(E_{u}\), \(E^{\prime}_{u}\), \(F_{v}\), and \(F^{\prime}_{v}\) are 3 \(\times\) 6 matrices.
# A Realistic Hand Image Composition Method for Palmprint ROI Embedding Attack
## 3 Methodology  
### Threat Model for Hand Attacks  
Let \(\mathcal{P}(\cdot)\) represent the preprocessing procedure of the PPR system, where \(H\) denotes the full hand image. The ROI is obtained through \(\mathcal{P}(H)\) and is subsequently utilized for palmprint recognition.  
Suppose \(R_{Atk}\) and \(C\) denote an attacking ROI and an arbitrary carrier hand image, respectively, where \(R_{Atk}\) is assumed to be an ROI image designed to match the target ROI in the feature domain. The \(R_{Atk}\) can be sourced from a compromised dataset or synthesized using the methods detailed in Section 2.2. We assume that an adversary possesses knowledge of the \(\mathcal{P}(\cdot)\) of the target system, implying awareness of the precise position \(P\) of ROI within \(C\); this is referred to as the embedding position.  
Designating \(\mathcal{E}(\cdot,\cdot,\cdot)\) as our proposed ROI embedding technique, a highly realistic composited hand image \(H_{Atk}\) can be generated through \(\mathcal{E}(R_{Atk},C,P)\) in an end-to-end and training-free manner. By inputting \(H_{Atk}\) into the target PPR system, \(R_{Atk}\) can be accurately extracted through \(\mathcal{P}(\cdot)\), achieving the objective of the attack.
## 2 Review of the Analytical Solutions for the Vertical Response of FREIs
### Problem Setting  
The vertical stiffness of U-FREIs under combined axial and shear loads can be written as:  
\[K_{v}^{\delta_{H}}=\frac{F_{v}}{\delta_{v}^{t}}=\frac{F_{v}}{\delta_{v}^{c}+ \delta_{v}^{v}} \tag{9}\]  
where the notation on the total vertical displacement introduced in Section 2.2 was used. Multiplying and dividing Equation (9) by the non-zero term \(\delta_{v}^{c}\), the vertical stiffness as a function of the horizontal displacement becomes.  
\[K_{v}^{\delta_{H}}=\frac{F_{v}}{\delta_{v}^{c}}\bigg{(}\frac{\delta_{v}^{c}}{ \delta_{v}^{c}+\delta_{v}^{v}}\bigg{)}=K_{v}^{\delta_{H}=0}\cdot\psi(\delta_{ H}) \tag{10}\]  
Equation (10) shows how the vertical stiffness of a U-FREI under combined axial and shear loads can be obtained starting from the vertical stiffness under pure compression using the function \(\psi(\delta_{H})\), which is defined as the ratio of the vertical displacement under  
Figure 1: (**a**) Definition of the geometric parameters for a strip-shaped U-FREI under a compressive load; (**b**) vertical displacement due to horizontal displacement of a strip-shaped U-FREI (adapted from [43]).  
pure compression to the total vertical displacement. Here, \(\psi(\delta_{H})\) modifies the vertical stiffness of the bearing to account for the reduction due to the horizontal deformation and is herein referred as the \(\psi\)_function_.  
\(\psi(\delta_{H})\) is a decreasing function with the horizontal displacement, ranging from a maximum value of 1 to a minimum value of 0 as:  
\[\lim_{\delta_{H}\to 0}\psi(\delta_{H})=1\ \lim_{\delta_{H}\to\delta_{H},b}\psi(\delta_{H})=0 \tag{11}\]  
where \(\delta_{H,b}\) is the horizontal displacement where buckling occurs. Accordingly, the vertical stiffness under combined axial and shear loads ranges from a maximum value equal to the vertical stiffness under pure compression to a minimum of 0. Using Equation (1) and recalling the definition of the secondary shape factor of a strip-shaped bearing, the vertical stiffness \(K_{v}^{\delta_{H}}\) can be also written as:  
\[K_{v}^{\delta_{H}}=\frac{E_{c}^{\delta_{H}=0}A_{c}}{t_{r}}\cdot\psi(\delta_{H} )=E_{c}^{\delta_{H}=0}\cdot S_{2}\cdot\psi(\delta_{H}) \tag{12}\]  
A similar approach can be used for the effective compressive modulus of FREIs under combined axial and shear loads. The definitions of \(E_{c}^{\delta_{H}=0}\) (Equation (2)) and of the secondary shape factors lead to:  
\[E_{c}^{\delta_{H}}=\frac{\sigma_{v}t_{r}}{\delta_{v}^{t}}=\frac{K_{v}^{\delta_ {H}}t_{r}}{A_{c}}=\frac{K_{v}^{\delta_{H}}}{S_{2}}=\frac{K_{v}^{\delta_{H}=0} \cdot\psi(\delta_{H})}{S_{2}} \tag{13}\]  
This equation shows how the effective compressive modulus as a function of the horizontal displacement can be obtained from the effective compressive modulus under pure compression using the \(\psi\)_function_.
# Hierarchical Mobile Edge Computing Architecture Based on Context Awareness
## 3 Proposed Context-Aware Hierarchical Mobile Edge Computing Architecture
Figure 4 shows the format and example of the HER message. The operation timestamp field is used to record the time at which each operation defined in the profile is performed. The time when the MECS sends the HER message to the central mobile cloud data center is recorded in the request timestamp field. Then, it is used to check the network status between the central mobile cloud data center and the MECS. Also, to ensure secure registration between the MECS and the central mobile cloud data center, the signature generated by the identifier, and the hash value from the central mobile cloud data center is used. The central mobile cloud data center checks the operation timestamp field of the HER message transmitted by the MECS and determines the hierarchy level of the MECS according to its own policy. After determining the hierarchy layer of the MECS, the central mobile cloud data center transmits a HER Acknowledgment (HERA) message, including a task type and hierarchy layer information suitable for the MECS. After receiving the HERA message, the MECS configures its task type and layer information. In other words, each MECS can provide layer-specific services through the Edge Performance Scoring process and hierarchical level that is configured based on its own computing ability. Figure 5 shows the format and example of the HERA message. Figure 4: Hierarchy Edge Registration Request message format and example. Figure 5: HER Acknowledgement message format and example. Figure 3: HEPR Acknowledgement message format and example. **Phase 3. Context Awareness Migration:** Since the proposed CoHEC architecture constructs a hierarchical architecture of MECS segmentalized through the Edge Performance Scoring process, it is possible to perform the operation of content transmission and specific services efficiently. The proposed CoHEC architecture consists of task migration and content migration.
# Fungi and Arsenic: Tolerance and Bioaccumulation by Soil Saprotrophic Species
## 3 Results  
Evaluation of Growth and Tolerance of Tested Fungi in the Presence of Arsenic and Culture Medium pH Analysis  
The values of diametric growth, biomass production, and tolerance indexes of the tested species in the presence of different As concentrations are shown in Tables 1-3. In all tested conditions, As did not inhibit the growth of the tested fungal species, which showed normal colony development and sporulation. Moreover, growth differences among the tested species, also in relation to the tested media, could be appreciated (Tables 1 and 2). In fact, on CDA, _A. spinosa_ showed greater growth than other species, while the lowest values of diameter and dry weights in both nutritional conditions were observed in _C. nanum. M. marquandii_ like _C. nanum_ showed greater diametric growth on CDA than on MEA, and biomass production of _C. nanum_ on CDA was higher than on MEA. Most of the calculated values for the tolerance indexes were higher than 80%, disclosing a strong tolerance of the tested strains (Table 3). _A. spinosa_ on MEA with 20 mg/L and 50 mg/L of sodium arsenite represented the only exception, showing both Rt:Rc and T.I. indexes lower than 70%. As the arsenic concentration increased in the medium, the values of both indexes reduced up to 50.3% for Rt:Rc and 52.8% for T.I. This trend was observed only on MEA, as the tolerance indexes for _A. spinosa_ on CDA were >90%. It is worth mentioning that in some cases, the tolerance indexes were higher than 110%. In fact, the Rt:Rc for _C. nanum_ grown on MEA amended with 50 mg/L sodium arsenite was 116.7%, while the T.I. for _C. nanum_ on MEA with 10 mg/L and 50 mg/L were 110.6% and 111.4%, respectively (Table 3). However, the diametric values and biomass production of _C. nanum_ on MEA for all tested As concentrations were not significantly different from that of the control (Tables 1 and 2). Regarding _M. marquandii_, the T.I. was 113.5% on MEA with 20 mg/L, even if biomass production on MEA control was not significantly different from that on MEA with 20 mg/L (Tables 2 and 3). The addition of different concentrations of sodium arsenite to both media did not change the medium pH (Table 4). Regardless of As concentration, _A. spinosa_ lowered the pH of both tested media, while _P. lilacinum_, _M. marquandii_, and _C. nanum_ increased CDA pH (Table 4). No variations of MEA pH were observed in _M. marquandii_ and _C. nanum_, while _P. lilacinum_ was able to slightly acidify MEA (control, 10, and 20 mg/L of sodium arsenite) (Table 4). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Diameter (mm)**} \\ \multicolumn{1}{c}{**Media**} & \multicolumn{3}{c}{**MEA**} & \multicolumn{3}{c}{**CDA**} \\ \hline
**NaAsO\({}_{2}\) Concentration** & **Control** & **10 mg/L** & **20 mg/L** & **50 mg/L** & **Control** & **10 mg/L** & **20 mg/L** & **50 mg/L** \\ \hline \multicolumn{1}{c}{_A. spinosa_} & \multicolumn{1}{c}{_A.
# Fault Prediction for Rotating Mechanism of Satellite Based on SSA and Improved Informer
###### Abstract
Predicted data were used to generate thresholds for fault detection, and results were validated against RNN, LSTM, and transformer models, demonstrating improved prediction accuracy. The remainder of this paper is structured as follows. In Section 2, we introduce the working principles and background of the rotating mechanism involved in fault prediction. Section 3 presents the signal processing and fault prediction methods proposed in this paper. Section 4 describes the simulation experiments and analysis. Finally, Section 5 provides the conclusion of this paper.
## 3 Results
### Anti-Interference  
Dichloromethane (CH\({}_{2}\)Cl\({}_{2}\)) and chloroform have similar properties and are both commonly used as solvents in some industries, therefore, dichloromethane and chloroform often exist at the same time in industrial environments. In this case, when the system is used for chloroform gas detection, it must remove the interference of dichloromethane gas. Although there is no obvious absorption peak of dichloromethane in this band, it is necessary to experimentally determine the interference from high concentrations of dichloromethane. In this work, dichloromethane gas with a concentration of 1052 ppm was used to verify the interference. Figure 8 shows the acquired 1f PA signals of pure nitrogen (black line), 1052 ppm dichloromethane (red line), 526 ppm dichloromethane with 313 ppm chloroform (blue line), and 313 ppm chloroform alone (green line), respectively. Obviously, in the effective area (colored area), 526 ppm Dichloromethane, and nitrogen have the same effect on the detection of chloroform, which is shown by the consistency of peak values between the blue line and the green line. This demonstrates that, in this area, even a high concentration of dichloromethane (e.g., up to 526 ppm) will not have any impact on the detection of chloroform gas.  
Figure 7: Plot of continuously measured data (at every 20 s) of chloroform gas concentration over 1 h and the corresponding Allan-Werle deviation analysis as the measurement time length (for averaging over each period of 20 s) increases.