esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This paper presents a case of coordinated use of geological, hydrochemical, and geophysical data to support characterizing a contaminated site at high resolution, designing a remediation intervention, monitoring and validating a pilot test, and implementing an effective remediation strategy at full scale. The hydrogeophysical model and thematic database act as integrated and continuously updated tools that can optimize the investigation during the characterization phase, support the choice of strategies in the planning phase, and manage and calibrate the progress of the intervention modality according to innovative approaches during the remediation phase. Representing the geological structure with a 3D model facilitates its understanding and depicts the hydrogeological settings. The high-resolution lithost stratigraphic reconstruction caught the variability of geological heterogeneities, which exert a decisive influence on the contamination dynamics and decontamination mechanisms. The combination of geophysical evidence strengthened and refined the 3D geological/hydrogeological model by providing data spatialization. The hydrogeological uniqueness and chemical peculiarities supported the selection of a remediation technology and the identification of application points. The indications acquired by the pilot test optimized the full-scale operation. Time-lapse geophysical surveys and soil and groundwater sampling during pilot testing delivered critical information, especially in terms of where and how in situ remediation actions affected different portions of the subsurface, as an effect of subsoil hydraulic heterogeneity. The operating project provided an innovative remediation strategy for sites contaminated by chlorinated DNAPLs. The remediation strategy implied the creation of \"reactive\" zones capable of significantly and permanently reducing the concentration of chlorinated solvents in groundwater through the combined action of adsorption and biodegradation. The results of full-scale intervention provide evidence for intense biological dechlorizing activity. Achieving the remediation objectives and project closure is based on integrating multidisciplinary data using a multiscale approach. This research represents the first completed example in European territory of the remediation of an aquifer contaminated with chlorinated solvents by a combination of adsorption and biodegradation.  
Conceptualization, P.C., C.E., and M.P.P.; methodology, P.C., C.E., and M.P.P.; software, P.C. and P.V.; validation, M.P.P., C.E., P.V., J.B., and G.C.; formal analysis, M.P.P., C.E., G.C., and P.V.; investigation, J.B. and G.C.; resources, M.P.P.; data curation, P.C., M.P.P., and G.C.; writing--original draft preparation, P.C.; writing--review and editing, C.E. and G.C.; visualization, P.C.; supervision, C.E., G.C., and M.P.P.; project administration, M.P.P.  
This research received no external funding.  
We gratefully acknowledge [PERSON], PhD, for groundwater circulation numerical modeling and Dr. [PERSON] for geotechnical laboratory analyses.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Remaining Shelf Life Determination--Cold Chain Management  
Cold chain management is required in order to reduce meat products' quality failure and thus reduce food loss and food waste while offering consumers high quality, safe food products. The Cold Chain Database and Cold Chain Predictor software developed within the FRISBEE project could be applied for the building of a representative profile for meat products based on approximately 4500 records of contributed profiles with real time-temperature data measured at different cold chain stages from production to domestic storage [24]. A sequence of real cold chain stages could be used to build a representative t-T profile for meat products such as sliced cooked ham and turkey, as presented in [PERSON] et al. [24]. The remaining shelf life (SLR) after each stage was calculated considering that microbial growth follows first order kinetics (exponential growth), using Equation (4):  
\[SLR=\ \frac{logN_{F}-logN_{0}-k_{\text{ref}}\cdot\exp\left[-\frac{\text{E}_{ \text{a}}}{R}\cdot\left(\frac{1}{\left(T_{\text{ref}}+273.16\right)}-\ \frac{1}{\left(T_{\text{ref}}+273.16\right)}\right)\right]\cdot t}{k_{\text{ ref}}} \tag{4}\]  
where \(N_{F}\): maximum microbial load before rejection, \(N_{0}\): the microbial count at the beginning of the cold chain stage, \(k_{\text{ref}}\): growth rate at a reference storage temperature \(T_{\text{ref}}=277.15\) K (4 \({}^{\circ}\)C), \(T_{\text{ref}}\) is the effective temperature, a constant temperature resulting in the same effect expected by the real t-T data from the database, \(\text{E}_{\text{a}}\): activation energy (J/mol), \(R\): universal gas constant (8.314 J\(\cdot\)mol\({}^{-1}\cdot\)K\({}^{-1}\)), and t: the duration of the stage.
## 5 Limitations and Future Work  
This study proposes a cultural information optimization and presentation method by integrating user-generated content (UGC) from social media, large language models (LLMs), and affective computing techniques. The method's effectiveness in cultural heritage communication was validated through VR experiments. While this research introduces an innovative approach to cultural information display, further exploration is needed in terms of the data's applicability, ethical considerations, and future research directions.  
The affective computing model based on UGC data relies on users' authentic experiences and multisensory feedback. However, VR experiments cannot fully replicate their immersed perceptions of real-world settings. As a result, the VR experiment primarily measures users' immediate allocation of attention and emotional arousal rather than their long-term engagement and comprehensive evaluation of cultural content. Additionally, the eye-tracking data do not fully capture all of the emotional dimensions encompassed in the UGC data, limiting their ability to represent the depth of user sentiment.  
From an ethical standpoint, LLMs may introduce biases or historical distortions when generating cultural information. Future research could integrate expert review mechanisms and information traceability techniques to ensure that the AI-generated cultural interpretations remain historically accurate and to enhance the content's credibility.  
The affective computing method based on UGC data is not only applicable to commercial heritage sites but could also be extended to a broader scope of cultural heritage exhibitions and digital cultural communication. Heritage site managers, researchers, and designers can leverage emotion-driven data insights to optimize cultural heritage display strategies. For instance, this method could be applied to smart tourism systems, enabling  
Figure 8: Visual heatmap of user attention in VR experiment.  
personalized cultural guidance services based on visitors' emotional feedback and interest preferences, thereby enhancing tailored cultural experiences.  
Furthermore, through a large-scale UGC data analysis, this approach could be utilized to monitor the sustainable development of cultural heritage tourism, assessing visitors' emotional responses to issues such as over-commercialization and environmental degradation.
# A Novel Process Recommendation Method That Integrates Disjoint Paths and Sequential Patterns
## 7 Conclusions  
Starting from the behavioral semantics of business processes, this study proposed a novel process recommendation method that integrates disjoint paths and sequential patterns. The EPE and CPSPan were introduced to calculate the process-matching degree using the edge-disjoint paths of the reference model and the CPSP of the process library model. Lift, confidence, and support were combined to provide more of a reference basis for the recommendation results. Comparison experiments with existing methods demonstrated that the proposed method effectively improved the process recommendation effect, performed better in terms of accuracy, and could meet the demand for time efficiency. In addition, this study presented a visual prototyping system that demonstrated the effectiveness of the methodology proposed in this study for supporting practical applications. In order to further enhance the contribution of the proposed method to both the research and industrial communities, the following future work is proposed: (1) To recommend a required number of process segments or the entire process model, a possible enhancement would be to include the currently recommended node as the next modeling node by invoking the process recommendation algorithm recursively until the desired number of process segments or the whole process model is recommended. (2) To better assist modelers in modeling and optimizing the execution performance of business processes, real business process event logs containing reliability metrics, node execution frequency, etc., can be collected and a process recommendation method combining the log data and model structure can be designed to better predict node execution times and execution outcomes.  
**Author Contributions:** Conceptualization, D.H. and C.W.; methodology, D.H.; validation, D.H. and T.S.: writing--original draft preparation, D.H.; visualization, D.H. and T.S.; writing--review and editing C.W., G.B., and B.S.; funding acquisition B.S. and G.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (No.62072363) and Shaanxi Provincial Natural Science Foundation (S2019-JC-YB-1191).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Decision Tree-Based Adaptive Reconfigurable Cache Scheme
## 6 Conclusions  
In this paper, we propose a novel decision tree-based adaptive associativity reconfigurable cache scheme. The scheme leverages the time-varying behavior of applications and adopts a decision tree to dynamically tune the cache associativity at runtime to reduce the average memory access time of the cache. Firstly, the optimal associativity scheme of the cache is obtained. Then, the decision tree model on optimal associativity is trained. Finally, by monitoring the behavior of the application at runtime, this decision tree model is used for adaptive control of cache associativity. We implemented the proposed scheme in the GEM5 simulator. The simulation results show that, compared with the reconfigurable cache based on the miss rate-threshold and the cache with fixed associativity, the scheme proposed in this work can effectively reduce the average memory access time and improve the system performance while maintaining low hardware overhead.  
Figure 16: Miss rate of different L2 caches.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Application** & **Basic Cache** & **MRARC [6]** & **DTARC** \\  & **(C.C.)** & **(C.C.)** & **(C.C.)** \\ \hline Barnes & 10.12 & 10.14 (\(-\)0.20\%) & 10.17 (\(-\)0.49\%) \\ Blackscholes & 10.34 & 10.24 (0.99\%) & 10.33 (0.10\%) \\ Canneal & 11.93 & 11.69 (2.04\%) & 11.01 (7.71\%) \\ Dedup & 10.58 & 10.25 (3.17\%) & 10.18 (3.78\%) \\ Ferret & 11.33 & 11.29 (0.33\%) & 10.89 (3.84\%) \\ Freqmine & 10.52 & 10.34 (1.75\%) & 10.19 (3.14\%) \\ Raytrace & 11.13 & 10.80 (2.98\%) & 10.37 (6.83\%) \\ Streamcluster & 11.80 & 10.75 (8.87\%) & 10.31 (12.60\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average memory access time of different L2 caches.  
**Author Contributions:** Funding acquisition, Supervision, X.Z.; Methodology, Software, Investigation, W.Z.; Writing--original draft preparation, W.Z.; writing--review and editing, X.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China (NSFC), grant number 61525401.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion
Traditionally, IR pathway has been the focus of antidiabetic therapies [5]. IR had received less attention as a therapeutic target in cancer because of the potential impact on glucose homeostasis [7]. Nevertheless, recent evidence from in vitro and in vivo studies had indicated that inhibitors of IR have potent antitumor activity [17; 57; 58; 59; 60]. [PERSON] et al. showed that IR antagonist S961 suppressed growth and downregulated IR expression in breast cancer cells [17]. KW-2450 (Kyowa Kirin Pharmaceutical Development, Inc., Princeton, NJ, USA) is an orally active multi-targeted tyrosine kinase inhibitor with both IGF-1R and IR inhibitory activity [58]. KW-2450 exerts synergistic antitumor effects in combination with lapatinib and letrozole. This drug is underway in early phase clinical trials to evaluate tolerability and efficacy in combination with lapatinib plus letrozole in patients with HER2-positive advanced or metastatic breast cancer [58]. The dual IGF-1R/IR tyrosine kinase inhibitor BMS-754807 (Bristol-Myers Squibb, New York, NY, USA) resulted in synergistic antitumor effects in combination with letrozole or tamoxifen in preclinical breast cancer models [59]. OSI-906 (OSI Pharmaceuticals, Melville, NY, USA) is another dual tyrosine kinase inhibitor of IGF-1R/IR that has been shown to effectively suppress the growth of hormone-independent breast tumors in the animal model alone, and in combination with the ER down regulator fulvestrant [7]. Several c-MET inhibitors are commercially available, and some are approved for treatment of human cancers [47]. Currently, c-MET inhibitors are being investigated in breast cancer clinical trials as monotherapy or in combination with targeted treatments [61; 62]. The main limitation of this study is the comparatively small sample size of our breast cancer patients. This may explain the lack of statistical significance for the association and correlation analysis performed in this study. However, patients in our study are included from a single center with unified diagnostic and management protocols. It is highly recommended to extend this analysis to involve a larger number of breast cancer patients to better elucidate the impact of receptor expression on clinicopathological and prognostic factors.
# An Interactive Personalized Garment Design Recommendation System Using Intelligent Techniques
## 3 General Scheme of the Design Knowledge Base Construction
### Construction of Garment Design Adaptation Model
#### 3.2.3 Calculation of the New Garment Ease Allowances  
The ease allowance is denoted as a metric indicating the extra space difference between the human body and the garment to express the various characteristics of the style design elements and the garment fit levels [39; 62; 63].  
The new garment ease allowance in this study is calculated by Equation (11):  
\[GE_{i}^{new}=\left\{\begin{array}{cc}OP_{i}\cdot GE_{i}^{std}&OP_{i}\;is\; SC_{i}^{new}\\ OP_{i}\cdot GF_{i}^{std}&OP_{i}\;is\;FC_{i}^{new}\end{array}\right. \tag{11}\]  
where \(GE_{i}^{new}\) refers to the new garment ease allowance of the new garment design solution at the \(i\) feature position; \(OP_{i}\) represents the \(i\)-th output of the design solution generation model, which could be \(SC_{i}^{new}\) or \(FC_{i}^{new}\); \(GE_{i}^{std}\) is denoted as a knowledge-based standard garment ease allowance vector at the \(i\) feature position; \(GF_{i}^{std}\) is denoted as a knowledge-based standard garment fitting vector at the \(i\) feature position (see Figure 3).  
For example, if \(OF_{i}=SC_{waist line}^{new}=\left[\begin{array}{ccc}lw&nw&lw\\ 0.3&0.7&0\end{array}\right]\), indicating that the new waistline was close to the normal waistline by 70% and the lower waistline by 30%, \(GE_{waist line}^{std}=\left[\begin{array}{ccc}lw\\ g\epsilon_{1}&g\epsilon_{2}&g\epsilon_{3}\end{array}\right]^{T}\), then the new garment ease for the waistline position \(GE_{waist line}^{new}\) can be computed by \(GE_{waist line}^{new}=0.3\times ge_{1}+0.7\times ge_{2}+0\times ge_{3}\).  
If \(OF_{i}=FC_{waist~{}girth}^{new}=\left[\begin{array}{ccc}t\ \ \ \ \ \ \ \ \ \ \ \ \ \ \
## 4 Non-Overshooting Tracking Control
We have  
\[\dot{\eta}_{n-1}(t)\leq-\varepsilon_{n-1}\eta_{n-1}(t). \tag{100}\]  
This subsystem (100) is integrated along \([0,t]\). Then,  
\[\eta_{n-1}(t)\leq\eta_{n-1}(0)e^{-\varepsilon_{n-1}t}. \tag{101}\]  
If \(\eta_{n-1}(t)\leq 0\), then  
\[\eta_{n-1}(0)\leq 0\Rightarrow\eta_{n-1}(t)\leq 0. \tag{102}\]Similarly, we get  
\[\eta_{1}(t)\leq\eta_{1}(0)e^{-\varepsilon_{1}t}. \tag{103}\]  
To sum up, the following inequality is satisfied  
\[\left\{ \begin{array}{ll}\eta_{1}(0)\leq 0,\\ \eta_{i}(0)\leq 0,i=2,\cdots,n-1,\ \ \Rightarrow\eta_{1}(t)\leq 0.\\ \eta_{n}(0)\leq 0.\end{array} \right. \tag{104}\]  
According to (33) and (34), \(\eta_{1}(t)\leq 0\Rightarrow e(t)\leq 0\). Therefore, inequality (104) can satisfy that the tracking error satisfies \(e(t)\leq 0\). Then, inequality (104) is a NOTC condition. **Case 2:**\(\eta_{n}(0)\leq 0\). From (98), if \(\eta_{n}(t)\geq 0\), then  
\[\eta_{n}(0)\geq 0\Rightarrow\eta_{n}(t)\geq 0. \tag{105}\]  
According to \(\dot{\eta}_{n-1}(t)=-\varepsilon_{n-1}\eta_{n-1}(t)+\eta_{n}(t)\). We get  
\[\dot{\eta}_{n-1}(t)\geq-\varepsilon_{n-1}\eta_{n-1}(t). \tag{106}\]  
We integrate this subsystem along \([0,t]\). The following inequality can be obtained  
\[\eta_{n-1}(t)\geq\eta_{n-1}(0)e^{-\varepsilon_{n-1}t}. \tag{107}\]  
If \(\eta_{n}(t)\geq 0\), then  
\[E_{n}(0)\geq 0\Rightarrow E_{n}(t)\geq 0. \tag{108}\]  
Similarly, we have  
\[\eta_{2}(t)\geq\eta_{2}(0)e^{-\varepsilon_{2}t}. \tag{109}\]  
Then, we have  
\[\eta_{2}(0)\geq 0\Rightarrow\eta_{2}(t)\geq 0. \tag{110}\]  
So \(\dot{\eta}_{1}(t)\geq-\varepsilon_{1}\eta_{1}(t)\). This subsystem is integrated along \([0,t]\). We have  
\[\eta_{1}(t)\geq\eta_{1}(0)e^{-\varepsilon_{1}t}. \tag{111}\]  
To sum up, the condition of \(\eta_{1}(t)\leq 0\) needs to satisfy the following inequality  
\[\left\{ \begin{array}{ll}\eta_{1}(0)\geq 0,\\ \eta_{i}(0)\geq 0,i=2,\cdots,n-1,\ \ \Rightarrow\eta_{1}(t)\geq 0.\\ \eta_{n}(0)\geq 0,\end{array} \right. \tag{112}\]  
According to (33) and (34), \(\eta_{1}(t)\leq 0\Rightarrow e(t)\leq 0\). Therefore, inequality (104) can satisfy that the tracking error satisfies \(e(t)\leq 0\). Then, inequality (112) is a NOTC condition. To sum up, we obtain the following theorem. Theorem 4: _For the \(n\)-order system (1), we constrain the controller (27) so that all signals of the CLS are bounded, and the tracking error satisfies \(\lim_{t\rightarrow\infty}e(t)=0\), and the states constraint is not violated. At the same time, NOTC can create the following two situations according to initial value \(\eta_{n}(0)\): (1) If \(\eta_{n}(0)\leq 0\), the NOTC condition needs to satisfy the condition (104); (2) If \(\eta_{n}(0)\geq 0\), the NOTC condition needs to satisfy the condition (112)._
# Borehole Trajectory Optimization Design Based on the SAC Algorithm with a Self-Attention Mechanism
## 6 Discussion  
The self-attention-enhanced SAC algorithm in this study improves the obstacle avoidance success rate by 12.5% compared with the traditional SAC in the two-obstacle scene. The convergence stability is better (the standard deviation of reward fluctuation is 0.03 vs. 0.07 of the traditional SAC), and the continuous action space design is better than the DQN algorithm of [PERSON] et al. The multi-objective reward function is more suitable for complex geological constraints, which is better than the NDQN model of [PERSON] and [PERSON]. At the same time, there are limitations, including that the data used is WGAN simulation-generated (based on the logging samples of Daqing Oilfield, but not verified), the scenarios, such as ultra-deep wells, are not tested, and the self-attention mechanism increases the computational complexity (the training time is increased by about 20%). In the future, we plan to combine the field data optimization model of Daqing Oilfield and introduce a lightweight attention mechanism. Due to the limitations of computing resources and time, compared with the research based on the reference in the document, no additional experiments are carried out.  
Methodology-Validation, H.G., X.L. and Y.W.; survey-Resources, H.G.; data monitoring, X.L.; writing--First Draft Preparation, Y.W.; writing--Review and Editing, Z.H.; funding acquisition, Z.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 52274005) and the Guiding Innovation Fund of Northeast Petroleum University: Intelligent Optimization Design of Horizontal Well Trajectory (Grant No. 2022 YDL-16).  
Not applicable.  
The data presented in this study are not publicly available due to confidentiality and security restrictions. For further inquiries, please contact the corresponding author.  
The authors would like to thank Northeast Petroleum University for providing technical and administrative support during this research. The authors also appreciate the constructive feedback provided by colleagues, which greatly improved the quality of this study.  
The authors declare no conflicts of interest.
## 4 Calculation and Results
### Queuing Distance \(S_{2}\)  
Throughout the queuing phase, it is assumed that the driver maintains a constant vehicle speed within the current lane, awaiting an available gap in the target lane. The queuing distance \(S_{2}\) is calculated using Equation (13):  
\[S_{2}=\frac{\tilde{V}}{3.6}t_{w} \tag{13}\]  
where \(\tilde{V}\) denotes vehicle speed during the queuing process (km/h), derived from the mainline design speed and the diversion vehicle speed. Diversion vehicle speed is the vehicle speed in the curb lane of the mainline at the beginning cross-section of the deceleration lane taper while indicating the queuing time (s).  
Figure 14: Calculation model for decision sight distance.  
When the time headway of vehicles in the diversion area adheres to a 2-stage Erlang distribution, Equation (14) depicts the average count of vehicles \(n\) within non-insertable gaps and the average duration \(h\) for the occurrence of non-insertable gaps:  
\[\begin{array}{l}n=\frac{1-P(h\geq t_{c})}{P(h\geq t_{c})}\\ h=\frac{\int_{0}^{t_{c}}tf(t)dt}{1-P(h\geq t_{c})}\end{array} \tag{14}\]  
where \(t_{c}\) is the minimum insertable gap critical value, which can be taken as 3.75 [33].  
The calculated average queuing time for insertable gaps for lane-changing vehicles, denoted as \(t_{w}\), is determined by Equation (15):  
\[t_{w}=nh=\frac{\int_{0}^{t_{c}}tf(t)dt}{P(h\geq t_{c})}=\frac{e^{-\lambda t_{c} }\left(-\lambda t_{c}^{2}-2t_{c}-\frac{2}{\lambda}\right)+\frac{2}{\lambda}}{( 1+2\lambda t_{c})e^{-2\lambda t_{c}}} \tag{15}\]  
where \(\lambda\) denotes the average rate of vehicles reaching the curb lane of the mainline per unit of time (pcu/s), \(\lambda=Q/3600\), and \(Q\) represents the maximum service traffic volume of a single lane for the mainline (pcu/h/ln), the level of service is taken as the LOS-C.