esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Treatment and Analysis
### Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Ball Toss Impact (cm)** & \multicolumn{4}{c}{**Men**} & \multicolumn{4}{c}{**Women**} \\ \hline  & \(x\)**-Axis** & \(y\)**-Axis** & \(z\)**-Axis** & \(x\)**-Axis** & \(y\)**-Axis** & \(z\)**-Axis** \\ \hline
**FS professional** & & & & & & \\ Number of mean results & 4 & 3 & 4 & 1 & 1 & 1 \\ Number of participants & 41 & 26 & 41 & 8 & 8 & 8 \\ Participants’ height (cm) & 180 & 177.5 & 180 & 169 & 169 & 169 \\ Ball impact (Mean \(\pm\) SD, cm) & 15.73 \(\pm\) 15.15 & 56.2 \(\pm\) 15.83 & 271.75 \(\pm\) 9.87 & 14 \(\pm\) 16 & 61 \(\pm\) 5 & 254 \(\pm\) 7 \\
**FS junior** & & & & & & \\ Number of mean results & 4 & 4 & 4 & 3 & 3 & 3 \\ Number of participants & 40 & 40 & 40 & 33 & 33 & 33 \\ Participants’ height (cm) & 172 & 172 & 172 & 157 & 157 & 157 \\ Ball impact (Mean \(\pm\) SD, cm) & 25.4 \(\pm\) 18.13 & 59.05 \(\pm\) 14.78 & 252.25 \(\pm\) 16.4 & 7.17 \(\pm\) 18.5 & 51.27 \(\pm\) 9.93 & 236 \(\pm\) 9.4 \\
**KS professional** & & & & & & \\ Number of mean results & 3 & 1 & 3 & 1 & 1 & 1 \\ Number of participants & 31 & 6 & 31 & 10 & 10 & 10 \\ Participants’ height (cm) & 184 & 183 & 184 & 161 & 161 & 161 \\ Ball impact (Mean \(\pm\) SD, cm) & 46.78 \(\pm\) 16.61 & 51.44 \(\pm\) 22.72 & 276.15 \(\pm\) 11.48 & 25.1 \(\pm\) 22 & 23.9 \(\pm\) 13.7 & 246.9 \(\pm\) 12.5 \\
**KS junior** & & & & & & \\ Number of mean results & 2 & 2 & 2 & 0 & 0 & 0 \\ Number of participants & 21 & 21 & 21 & - & - & - \\ Participants’ height (cm) & 174 & 174 & 174 & - & - & - \\ Ball impact (Mean \(\pm\) SD, cm) & 39.3 \(\pm\) 29.85 & 35.1 \(\pm\) 16.95 & 258.05 \(\pm\) 17 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._leftwards BI location from the origin was performed by junior men; on the \(y\)-axis: professional women performed the BI location the most forwards from the origin; on the \(z\)-axis: junior men performed the BI location the lowest from the origin. For KS on the \(x\)-axis: the least leftwards BI location from the origin was performed by the junior women; on the \(y\)-axis: the junior women performed the least forward BI location from the origin, and on the \(z\)-axis: the lowest BI location from the origin was performed by junior men. Figure 3: The forest plots present the ball impact location. There are presented the normalized mean and 95% confidence interval of the ball impact location in chosen studies. The subtotal presented a normalized weighted mean and their 95% CIs of the groups divided according to the serve type (upper part = Flat serve and lower part = Kick serve). Note: \(\leftarrow\) L (leftwards from the origin on the \(x\)-axis); F \(\rightarrow\) (forwards from the origin on the \(y\)-axis; V \(\rightarrow\) (upwards from the origin on the \(z\)-axis. References: [14; 17; 19; 20; 26; 27; 28; 29; 30; 31].
## 3 Results and Discussion  
### Measurements of 232 Th Activity in Gas Mantle  
The activity of thorium was investigated for the five different types of mantle (masses 1.320 to 3.490 g), with 232 Th activity ranging from 5.1 \(\pm\) 1.31 to 16.33 \(\pm\) 1.92 Bq/mantle. Samples M1 to M4 were low in radioactivity, compared to mantles M5 which recorded a relatively high 232 Th activity, at 12,517 \(\pm\) 1173 Bq/mantle (Table 1). In addition, the activity of thorium in samples M5 exceeds guidance on an exemption limit of 1 \(\times\) 10\({}^{3}\) Bq for Th-232, as in the international basic safety standards for protection against ionizing radiation IAEA [24], also in update IAEA 2014 GSR Part 3 [15] of 1 \(\times\) 10\({}^{4}\) Bq. [PERSON] et al. [17] suggest the update links to research showing many mantles exceeding prior guidance, with for instance the authors obtaining 4.5 \(\times\) 10\({}^{3}\) Bq of Th-232 in mantles, also with the U.K. the National Radiological Protection Board (NRPB-R263) proposing a limit of radioactive thorium of not more than 1 kBq per mantle [25].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**Activity \(\pm\) 1 Sigma (Bq/Mantle)**} \\ \cline{3-6}  & & **Weight (g)** & \multicolumn{2}{c}{**Ra-228**} & **Th-228** & **Th-232 \({}^{*}\)** \\ \hline M1 & Butterfly ART No.4D (500-600 C.P) & 2.79 \(\pm\) 0.0027 & 14.11 \(\pm\) 2.2 & 12.5 \(\pm\) 1.35 & 13.3 \(\pm\) 1.77 \\ M2 & Kovea KL 101/102 -TKL929 & 1.42 \(\pm\) 0.0014 & 6.65 \(\pm\) 0.6 & 8.6 \(\pm\) 1.2 & 7.62 \(\pm\) 0.9 \\ M3 & U-shape ** & 1.32 \(\pm\) 0.0013 & 4.7 \(\pm\) 0.73 & 5.5 \(\pm\) 1.9 & 5.1 \(\pm\) 1.31 \\ M4 & Kovea TKL-N894 KL103 & 1.75 \(\pm\) 0.0017 & 14.11 \(\pm\) 2.24 & 18.56 \(\pm\) 1.6 & 16.33 \(\pm\) 1.92 \\ M5 & Butterfly No.999 (300-400 C.P) & 3.49 \(\pm\) 0.0034 & 13,393 \(\pm\) 1398 & 11,642 \(\pm\) 948 & 12,517 \(\pm\) 1173 \\ \hline \hline \end{tabular}  
* Activity of Th-232, averaged from Ra-228 and Th-228. ** Name of manufacturer and model not available on the mantle package.  
\end{table}
Table 1: Activity of radionuclides of the Th-232 series in different types of the mantle.  
Figure 3: Medical internal radiation dose (MIRD) human phantoms and juxtaposition of lantern mantle M5, color represent different composition and density of tissues; green, yellow, and aqua represent lung tissue, soft tissue, and bone, respectively.  
Present study results as in (Table 1), are comparable to the literature review data in (Table 2), in the case of M5 recording a value greater than that of the previous data.  
Results from pristine M5 mantles prepared using two different methods (Table 3) showing the activity of Th-232 obtained in direct measurement of the gamma-ray spectrum from 'as is' samples to be comparable to within 11% with that from mantles dissolved in 0.5 M HNO\({}_{3}\), indicative of a good degree of efficiency in thorium released into solution.
# LESS: Link Estimation with Sparse Sampling in Intertidal WSNs
## 2 Related Work
### Applications of Compressive Sensing  
Compressive sensing is widely used in WSNs for data collection ([22; 23; 24]), routing path recovery [25] and topology reconstruction. [26] confirms that compressive sensing prolongs network lifespan significantly with rigorous experiment and theoretical analysis. Liu. [24] utilizes a signal compression algorithm and a reconstruction algorithm based on compressive sampling theory for accurate in-network earthquake timing. CSPR [25] exploits the sparsity that a routing path size is far less than network size and designs a path reconstruction algorithm. NS-RTR [27] utilizes the sparsity of difference between routing path changes and encodes the changes in data packets. Although CSPR [25] and NS-RTR [27] can also achieve topology reconstruction with compressive sensing, the difference from LESS mainly exists in two aspects. First, LESS exploits compressive sensing to recover the basic link quality values instead of data packets. Second, LESS recovers topology based on the recovered link quality values and therefore it can obtain all the potential links besides the routing links (i.e., a complete topology instead of a routing topology). However, the recovered topology for both CSPR [25] and NS-RTR [27] only contains routing links.
## Laser-Plasma Acceleration with _Flame_ and _Ili_ Ultraintense Lasers  
**[PERSON] \({}^{1,2,3,*}\), [PERSON] \({}^{4}\), [PERSON] \({}^{1}\), [PERSON] \({}^{3}\), [PERSON] \({}^{1,\dagger}\), [PERSON] \({}^{3}\), [PERSON]\({}^{1}\), [PERSON] \({}^{1,2,5}\), [PERSON] \({}^{1,2}\), [PERSON]\({}^{1,2}\), [PERSON]\({}^{1,3,\dagger}\), [PERSON] \({}^{1,5,\dagger}\) and [PERSON] \({}^{5}\)**  
\({}^{1}\) Intense Laser Irradiation Laboratory (ILIL), INO-CNR, [PERSON] 1, 56124 Pisa, Italy;  
E-Mails: c.ecchetti@light4 tech.com (C.A.C.); [PERSON]] (A.G.);  
[EMAIL_ADDRESS] (A.G.); [PERSON](D.G.); [PERSON]S] (P.K.);  
[EMAIL_ADDRESS] (L.L.); [PERSON]S] (T.L.); [EMAIL_ADDRESS] (N.P.)  
\({}^{2}\) National Institute of Nuclear Physics (INFN) Sezione di Pisa, Largo B. Pontecorvo 3, I-56127, Pisa, Italy  
\({}^{3}\) Frascati National Laboratories, INFN, Via E. Fermi 40, I-00044 Frascati, Italy;  
E-Mails: [PERSON]G.D.); [EMAIL_ADDRESS] (G.G.)  
\({}^{4}\) University of Bologna and Sezione INFN, 40126 Bologna, Italy; E-Mail: [EMAIL_ADDRESS]  
\({}^{5}\) \"[PERSON]. Fermi\" Physics Department, University of Pisa, Largo Pontecorvo 3, I-56127 Pisa, Italy;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) Present address: NEST, SNS, Pisa, Italy; also at University of Rome \"Tor Vergata\", Roma, Italy &  
Fyzikalni vstav AV CR v.v.i., Praha, Czech Republic  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +39-050-315-225-7; Fax: +39-050-315-224-6.  
_Received: 22 April 2013; in revised form: 23 May 2013 / Accepted: 14 June 2013 / Published: 5 July 2013_  
**Abstract:** We report on the development of radiation and electron sources based on laser-plasma acceleration for biomedical and nuclear applications, using both the table top TW laser at ILIL and the 220 TW FLAME laser system at LNF. We use the ILIL laser to produce wakefield electrons in a self-focusing dominated regime in a mm scale gas-jet to generate large, uniform beams of MeV electrons for electron radiography and radiobiology applications. This acceleration regime is described in detail and key parameters are given to establish reproducible and reliable operation of this source. We use the FLAME laser to drive laser-plasma acceleration in a cm-scale gas target to obtain stable production of \(>\)100 MeV range electrons to drive a Thomson scattering \(\gamma\)-ray source for nuclear applications.  
**Keywords:** ultraintense lasers; laser-plasma acceleration; radiation sources
## 1 Introduction  
Inflammation is a local reaction to cell damage, characterized by increased blood flow, white blood cell penetration, capillary dilation, and the local production of host chemical mediators that eliminate toxicants and initiate damaged tissue repair [1]. In particular, chronic low-grade inflammation is the principal cause of various chronic diseases, such as cardiovascular disease, type 2 diabetes mellitus, metabolic syndrome, non-alcoholic fatty liver disease, and various cancers [2, 3]. During a chronic inflammatory state, nitric oxide and proinflammatory cytokines such as interleukin-6 (IL-6), IL-1\(\beta\), and tumor necrosis factor-\(\alpha\) (TNF-\(\alpha\)) are secreted by activated macrophages [4, 5]. Therefore, anti-inflammatory activity has been widely used as a screening method for identifying functional ingredients.  
Dendropanax morbifera_LEV (DM) is an evergreen broad-leaved tree mainly grown on Jeju Island, Korea. DM has been used in the production of golden varnish and is consumed as a tea. The leaves, roots, seeds, and stems of this plant have been used as folk medicine for infectious diseases, liver disease, skin diseases, and immunological enhancement, in addition to food supplementation [6; 7; 8]. DM ethanol extracts display xanthine oxidase inhibitory effects that can prevent diseases related to hyperuricemia [9]. Furthermore, DM water extracts exhibit lower mRNA and protein levels of fat-related genes and anti-obesity effects in 3T3-L1 cells [10; 11]. DM leaves have been proven to contain chlorogenic acid, rutin, polyacetylenes, and oleifolioside A. Moreover, polyacetylenes from DM have been reported to display anti-complement activity [12; 13]. Rutin isolated from DM has been shown to prolong the activated partial thromboplastin time, prothrombin time, and closure time [14]. Additionally, DM chloroform extracts have been reported to possess potent anti-inflammatory and analgesic effects [15]. In another study, DM leaf extracts reduced D-galactose-induced mouse senescence by reducing proinflammatory cytokines, such as IL-1\(\beta\), IL-6, and TNF-\(\alpha\)[16]. Oleifolioside A isolated from DM targeted both the nuclear factor \(\kappa\)B (NF-\(\kappa\)B) and mitogen-activated protein kinase (MAPK) signaling pathways in RAW264.7 cells [17]. [PERSON] et al. [18] reported that the DM chemical composition varied with seasonal changes in the sampling time. Most previous studies using DM extracts have involved methanol or chloroform. Our previous study also reported that the antioxidant capacity and major flavonoids such as chlorogenic acid and rutin exhibited seasonal variation [19]. The current study aimed to confirm seasonal and extract solvent variation in the anti-inflammatory activity on RAW264.7 mouse macrophage cells stimulated with lipopolysaccharide (LPS). In addition, major active chemicals, including terpenoid and polyphenolic compounds, were analyzed.
# Enhancing Tactile Internet Reliability: AI-Driven Resilience in NG-EPON Networks
## 4 Proposed Architecture
### System Throughput  
Figure 11 depicts the system throughput under normal and fault conditions. The system throughput of the network demonstrates a consistent increase as the traffic load rises, indicating the network's robust capacity to handle escalating demands. This pattern shows an efficient RDBA that successfully adapts to increasing traffic demands. Moreover, in fault conditions (1 Fault and 3 Fault Averages), there is an observed increase in throughput efficiency compared with normal conditions. This is because the overhead communication required for inactive or faulty ONUs decreases, allowing more bandwidth to be allocated to active connections, thus improving the overall efficiency of the NG-EPON systems.
# Fusion Information Multi-View Classification Method for Remote Sensing Cloud Detection
## 4 Experiment
### Quantitative Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Jaccard Index** & **Pression** & **Recall** & **Specificity** &
\begin{tabular}{c} **Overall** \\ **Accuracy** \\ \end{tabular} & **FI-Score** \\ \hline Cloud-Net [15] & 81.93 \(\pm\) 7.22 & 87.03 \(\pm\) 5.48 & 93.49 \(\pm\) 4.36 & 82.86 \(\pm\) 4.34 & 88.46 \(\pm\) 3.68 & 91.21 \(\pm\) 4.46 \\ HFCNN [19] & 87.94 \(\pm\) 5.12 & 91.87 \(\pm\) 4.05 & 96.02 \(\pm\) 5.13 & 91.28 \(\pm\) 3.74 & 92.79 \(\pm\) 5.04 & 94.36 \(\pm\) 4.76 \\ SVM & 67.12 \(\pm\) 3.77 & 71.36 \(\pm\) 5.14 & 92.13 \(\pm\) 5.49 & 72.56 \(\pm\) 4.87 & 78.85 \(\pm\) 3.24 & 79.62 \(\pm\) 3.64 \\ MCPK & 85.32 \(\pm\) 4.19 & 87.93 \(\pm\) 5.33 & 96.07 \(\pm\) 3.96 & 85.77 \(\pm\) 5.08 & 90.76 \(\pm\) 4.39 & 92.43 \(\pm\) 3.13 \\ Our method & **91.69 \(\pm\) 1.34** & **94.67 \(\pm\) 2.01** & **97.38 \(\pm\) 1.65** & **93.04 \(\pm\) 2.12** & **96.31 \(\pm\) 1.46** & **96.03 \(\pm\) 1.72** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance on GF-1_WHU test dataset (mean \(\pm\) variance (%)).
# The Development of a Soft Robot Hand with Pin-Array Structure
## 5 The Prototype and the Experiments
### Experiments with the Exploration of Grab Performance
#### 5.3.3 The Position of the Object  
The degree of the effect of the position of the target object on the grasping performance is one of the important criteria for judging the performance of a gripper. This section will explore the influence of the position of the target object on the grasping performance. The method is: in the circle with a diameter of 30 mm under the vertical projection of the CTSA-II hand, the distance from the center of the object to the center of the circle was recorded as the offset distance; CTSA-II hands with different pins were used to grab a 5 mm \(\times\) 5 mm \(\times\) 10 mm cube with different offset distances. Each set of tests was conducted 10 times and the success rate of each set was recorded.  
The experimental results obtained are shown in Table 6. From Table 6, it can be concluded that the CTSA-II can effectively grasp the object within the deviation range of 5 mm without any mistakes. However, when the offset distance is too large, the CTSA-II hand grab is prone to failure. In the three types of CTSA-II hands, the prototype with 8 pins is less affected by the offset distance.
## 2 Materials and Methods  
### Data and Preprocessing  
#### 2.1.1 Flux-Site Data  
The daily-scale data from 180 flux sites from the FLUXNET2015 tier 2 dataset [31] were used for the analyses in the current study. These flux sites cover nine different plant function types (PFTs) including crops (CRO), deciduous broadleaf forests (DBF), evergreen broadleaf forests (EBF), evergreen needleleaf forests (ENF), grasslands (GRA), mixed forests (MF), savannahs (SAV), shrubs (SHR), and wetlands (WET). SHR include closed shrubs and open shrubs. A deciduous needleleaf forest site was included in ENF and woody savannah sites were included in SAV. The number of these flux sites among the 9 PFTs is as follows: CRO: 18, DBF: 21, EBF: 14, ENF: 42, GRA: 34, MF: 8, SAV: 14, SHR: 14, and WET: 15.  
The data we used from the aforementioned dataset included seven meteorological factors, carbon dioxide mole fraction (C\({}_{\text{a}}\)), and observed GPP data. Temporally continuous meteorological factors on a daily scale include air temperature (T\({}_{\text{a}}\)), precipitation, incoming shortwave radiation (R\({}_{\text{g}}\)), incoming longwave radiation (R\({}_{\text{L}}\)), vapor pressure deficit (VPD), atmospheric pressure (P\({}_{\text{a}}\)), and wind speed (WS). Considering the water storage capacity of the soil, we treated the accumulated precipitation value of 8 days including that day as the precipitation data of the day for model calculation, as described in a previous study [32]. The method to calculate the accumulated precipitation value of 8 days (P) on the nth day is as follows:  
\[P=\left\{\begin{array}{ll}PREC_{1}+PREC_{2}+\cdots+PREC_{n},&1\leq n<8\\ PREC_{n-7}+PREC_{n-6}+\cdots+PREC_{n},&n\geq 8\end{array}\right. \tag{1}\]  
where \(PREC_{n}\) represents the precipitation on the nth day of a site. There are several GPP versions available in the dataset, among which we utilized the version labeled as 'GPP_NT_VUT_REF'. In this version, 'NT' denotes that the GPP was retrieved by partitioning the observed net ecosystem carbon exchange (NEE) applying the nighttime method [33], 'VUT' indicates that the data were filtered utilizing the friction velocity threshold ([[https://fluxnet.org/data/fluxnet2015-dataset/data-processing/](https://fluxnet.org/data/fluxnet2015-dataset/data-processing/)]([https://fluxnet.org/data/fluxnet2015-dataset/data-processing/](https://fluxnet.org/data/fluxnet2015-dataset/data-processing/)), accessed on 21 May 2021), and 'REF' represents the most common reference value among those from 40 different FLUXNET2015 workgroups [34].
## 4 Examples for the Prediction of One-Dimensional Consolidation Settlement
While using the AJOP equation, the single-layer division method yielded the lowest settlement due to the mid-layer being positioned farther from the ground surface. On the other hand, the multilayer division method provided more accurate results because the mid-points of some layers were closer to the ground surface, which is a critical factor for the AJOP method. In case (c), where layer thickness varies, the results were most accurate as the mid-points of the layers were correctly positioned. This demonstrates that both the calculation method and the layer division approach significantly affected the accuracy of the results. It should be noted that for this low surface loading value, we found that some soil layers were calculated with a final stress lower than the maximum past pressure. Table 4 presents the calculation results under high loading conditions. In these calculations, both the compression ratio \(\overline{C_{r}}\) and the compression index \(\overline{C_{\rm{c}}}\) were used for Boston and Bangkok clays. However, for London clay, only the compression ratio \(\overline{C_{r}}\) was utilized in the calculations in some depths. The findings show that although using different methods in layer division, the consolidation settlement was similar for all methods due to the fact that a single layer expressed the lowest value. In both low-loading 200 and high-loading 1000 kN/m\({}^{2}\), it was revealed from the investigation that Case (1) (in Figure 5a) can occur but is not common at a specific depth, whereas Case (2) (in Figure 5b) generally occurs more frequently in both cases of low and high pressures. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Soil** & **Case** & **LF** & **CF** & **AJOP** \\  & & **(Equation (3))** & **(Equation (4))** & **(Equation (6))** \\ \hline \multirow{3}{*}{Boston blue clay} & (a) Single layer & 1.566 & 0.302 & 0.284 \\ \cline{2-5}  & (b) Equal layer thickness & 1.967 & 0.323 & 0.310 \\ \cline{2-5}  & (c) Varied layer thickness & 1.988 & 0.323 & 0.309 \\ \hline \multirow{3}{*}{London clay} & (a) Single layer & 0.806 & 0.102 & 0.089 \\ \cline{2-5}  & (b) Equal layer thickness & 1.012 & 0.109 & 0.097 \\ \cline{2-5}  & (c) Varied layer thickness & 1.023 & 0.108 & 0.097 \\ \hline \multirow{3}{*}{Bangkok clay} & (a) Single layer & 2.334 & 3.453 & 1.832 \\ \cline{2-5}  & (b) Equal layer thickness & 2.933 & 3.667 & 1.781 \\ \cline{1-1} \cline{2-5}  & (c) Varied layer thickness & 2.964 & 3.663 & 1.780 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of total consolidation settlement of normally consolidated clay using LF, CF, and AJOP methods for three soil datasets.
## 1 Introduction
They used a graphite-cement paste layer as the anode for a conductive mortar coupled with a primary graphite anode. They assessed the proposed systems using specimens immersed in chloride. There were also previous investigations on the effect of the surrounding environment and the sustainability of conductive mortar (e.g., [31; 32]). In this study, a novel conductive mortar was investigated to enhance the effectiveness of CP by reducing its resistivity. In contrast to previous investigations that primarily emphasized electrical conductivity to support the primary anode of the ICCP system, this study placed greater emphasis on the role of conductive mortar in facilitating ionic movement from the anode to the rebar in the SACP system. Specifically, the objective was to decrease concrete resistivity and sustain its humidity over an extended period. The integration of the proposed conductive mortar can significantly enhance the applicability of SACP in tidal and splash zones. In Part 1 [33], a variety of potential conductive mortars, such as activated carbon, bentonite, zeolite, and geopolymers, were compared through laboratory experiments. Additionally, chemical agents were added to the admixtures to further enhance conductivity. The chemical agents selected were sodium hydroxide, calcium hydroxide, lithium hydroxide, and sodium chloride. The findings indicate that the conductive mortar based on activated carbon exhibits superior effectiveness in terms of resistivity, CP potential and current, and depolarization potential. In Part 2 of this paper, the SACP performance using a conductive mortar containing activated carbon was tested over four years in an actual bridge structure in South Korea. The bridge experiences tidal variations twice per day and is thus regarded as a corrosion-accelerating region. The performance of the proposed conductive mortar and SACP system was evaluated through basic physical tests before CP application, visual inspection after four years, and depolarization measurements for four years. Given that previous studies have not investigated similar applications of conductive mortars in SACP systems or their field applications, the findings of this study can provide valuable insights for engineers and researchers in related fields.