esa-sceva/satcom-chunk-collection huggingface dataset:
# CETD: Counterfactual Explanations by Considering Temporal Dependencies in Sequential Recommendation
## 3 Propose Model
### The CETD Model
#### 3.3.2 Causal Rule Learning Model
```
Input: users \(\mathcal{U}\), items \(\mathcal{V}\), user history \(\mathcal{H}^{u}\), counterfactual number \(w\), black-box recommendation model \(\mathcal{F}\), embedding model \(\mathcal{E}\), GRU model \(\mathcal{G}\), perturbation model \(\mathcal{P}\), causal rule learning model \(\mathcal{M}\) Output: counterfactual explanations
1 Pass user real history into embedding model \(\mathcal{E}\) to obtain item embedding \(E\);
2 Use GRU model \(\mathcal{G}\) to learn the temporal dependencies of the previous history;
3 Use \(\mathcal{G}(E)\) and real history to train perturbation model \(\mathcal{P}\);
4 forEach user \(u\in\mathcal{U}\)do
5 foriffrom 1 to \(w\)do
6\(\hat{\mathcal{H}}^{u}_{i}\leftarrow\mathcal{P}(\mathcal{H}^{u})\); \(\tilde{B}^{u}_{i}\leftarrow\mathcal{F}(\widetilde{\mathcal{H}}^{u}_{i})\);
7
8 end for
9 Construct counterfactual input-output pairs \(\{(\widetilde{\mathcal{H}}^{u}_{i},\tilde{B}^{u}_{i})\}_{i=1}^{w}\);
10\(\{(\hat{\mathcal{H}}^{u}_{i},\hat{B}^{u}_{i})\}_{i=1}^{w+1}\leftarrow\{(\tilde {\mathcal{H}}^{u}_{i},\tilde{B}^{u}_{i})\}_{i=1}^{w}\cup(\mathcal{H}^{u},B^{u })\);
11 fori from 1 to (w+1)do
12 forfrom 1 to \(T\)do
13\(\theta_{\hat{\mathcal{H}}^{u}_{i(t)},\hat{B}^{u}_{i}}\leftarrow\mathcal{M}^{u}( \hat{\mathcal{H}}^{u}_{i(t)},\hat{B}^{u}_{i})\);
14
15 end for
16
17 end for
18 Rank \(\theta_{\hat{\mathcal{H}}^{u}_{i(t)},\hat{B}^{u}}\) and select top-k pairs \(\{(H_{n},B^{u})\}_{n=1}^{k}\);
19 if\(\exists H_{min\{n\}}\in\mathcal{H}^{u}\)then
20 Generate counterfactual explanation \(H_{min\{n\}}\Rightarrow B^{u}\);
21 else
22 No explanation for \(B^{u}\);
23
24 end if
25
26 end for return all counterfactual explanations
```  
**Algorithm 1**Counterfactual explanations by considering temporal dependencies  
In Algorithm 1, generating counterfactual interpretations of their recommended items for each user \(u\) can be divided into three phases: lines 5-8 are the perturbation phases, where the perturbation model perturbs the history \(\mathcal{H}^{u}w\) times to generate counterfactual histories, passes them into the recommendation model to generate their corresponding recommendation results, and ultimately composes \(w\) pairs of counterfactual input-input pairs \(\{(\widehat{\mathcal{H}}^{u}_{i},\widetilde{B}^{u}_{i})\}_{i=1}^{w}\); lines 9-14 are the causal rule learning phase, where the causal rule learning model \(\mathcal{M}\) is utilized to learn the causal dependency \(\theta_{\hat{\mathcal{H}}^{u}_{i(t)},\hat{B}^{u}_{i}}\) between each input term \(\hat{H}^{u}_{i(t)}\) and the output item \(\hat{B}^{u}_{i}\); lines 15-20 are the generation of the interpretation phase, where the model generates its corresponding counterfactual explanations for the item \(B^{u}\) recommended to the user \(u\).
# In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning
## 2 Development of BMS  
A printed circuit board was developed for the proposed BMS, focusing on collecting essential data for battery health prediction and initiating actions based on predictions. The developed BMS used the in operando data for modern battery health prediction including battery voltage, current, and temperature [18; 31]. The collected data were then fed to a DL model for the live prediction of the battery health. The development process is described in brief in the following sections.
# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 2 Related Work
### Deep Learning for Landing Site Recognition  
Deep learning is effective for template matching, and prior training allows autonomous UAVs to better meet user needs by reducing the randomness associated with landing in complex environments. Dynamic landing, where the specific UAV landing location is not predetermined, requires the UAV to analyze the suitability of various landing sites based on the surroundings. For example, [PERSON] et al. [30] used a modified ResNeXt50 architecture and trained it on the Places365 dataset to develop a seven-class method for assessing landing safety. Although this classification method was not deployed in real scenarios, it provided a useful reference. [PERSON] et al. [31] employed an architecture to isolate pedestrians in crowded urban areas and determine the most suitable location for UAV landing based on the maximum circumscribed circle. This method ensured the safety of the UAV landing site but did not account for the impact of the actual terrain on the landing. [PERSON] et al. [25] used point clouds and neural networks to assist in landing site determination. They identified platform features using the ResNet50 network and evaluated the 3D attributes of the platform with 3D point clouds to ensure a safe landing. Compared with a dynamic analysis using a single sensor, the strategy proposed herein also includes a terrain analysis to enhance UAV landing safety. However, owing to the lack of an optimized terrain judgment algorithm, it cannot attain the same advantages as multiple sensors.  
There are also approaches that help the UAVs detect deployed landing sites. For example, [PERSON] et al. [32] implemented target point recognition within the visual range using the MobileNetV3 framework and reported long-distance detection of target points; however, that approach required significant prior deployment across a wide range of jobs. [PERSON] et al. [33] developed YOLO-V4 for dynamic unmanned vehicle recognition. They incorporated Ultra-Wide Band (UWB) for assisted positioning during landing and an RGB-D camera for landing area segmentation and pose estimation. Although short-range RGB-D is less affected, the early deployment of outdoor UWB was heavy and difficult to maintain. [PERSON] et al. [34] evaluated tracking problems in dynamic scenarios and developed a precise and robust strategy for mobile platform target tracking. However, their method relied on the GPS information of the target mobile platform under basic conditions, which is not always available.
## 3 Results and Discussion
### Refrigeration/Air Conditioning  
Since the average daily temperature in the summer time for the analyzed regions (except Astrakhan) does not exceed 25 \({}^{\circ}\)C, the air conditioning application is demanded only for zone I, located in the South of Russia. For all other regions, adsorption refrigeration is considered for food/drugs storage. The boundary adsorption potentials \(\Delta F_{1}\) and \(\Delta F_{2}\) are calculated (Table 1) according to Equations (1) and (2). For each location, the condenser/adsorber temperature, \(T\)con = \(T\)ads, is assumed to be equal to the maximum day temperature \(T\)d.max of the hottest month of a year (July). The useful effect is produced in the evaporator, and the evaporator temperature Tev equals the temperature \(T\)uh of the required cold, namely, 3 \({}^{\circ}\)C for refrigeration and 10 \({}^{\circ}\)C for air-conditioning. The operating conditions of the refrigeration cycle are presented in Figure 4 as the colored areas confined between the boundary potentials \(\Delta F_{1}\) and \(\Delta F_{2}\) for the climatic zones analyzed with water as refrigerant.  
The most promising adsorbents for the refrigeration cycle are LiCl/MWCNT, LiCl/vermiculite, and MIL-125-NH\({}_{2}\) for Astrakhan, Omsk, and Archangelsk regions, respectively (Table 1, Figure 4). The sorbent CAU10 is the best for Moscow and Yakutsk.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**City** & **Treg.**\({}^{\circ}\)**C** & \multicolumn{2}{c}{**\(\Delta\)F kJ/mol**} & \multirow{2}{*}{**Adsorbent**} & \multirow{2}{*}{**\(\Delta\)_w_, g/g**} \\ \cline{5-6}  & & \(\Delta\)F\({}_{2}\) & \(\Delta\)F\({}_{1}\) & & \\ \hline \multicolumn{6}{c}{Air conditioning} \\ \hline Astrakhan & 85 & 4.0 & 6.9 & LiCl/MWCNT & 0.6 \\ \hline \multicolumn{6}{c}{Refrigeration} \\ \hline Astrakhan & 85 & 5.2 & 6.9 & LiCl/vermiculite & 0.3 \\  & & & LiCl/MWCNT & 0.4 \\ Omsk & 75 & 4.8 & 5.9 & LiCl/vermiculite & 0.5 \\  & & & ALPO—18 & 0.2 \\ Yakutsk & 65 & 4.5 & 4.8 & CAU10 & 0.2 \\ Moscow & 65 & 4.2 & 5.0 & CAU10 & 0.3 \\ Oimyakon & 65 & 4.2 & 4.8 & CAU10 & 0.3 \\ Vladivostok & 60 & 3.9 & 4.7 & MIL125 NH2 & 0.3 \\ Arkhangelsk & 60 & 3.8 & 5.1 & MIL125 NH2 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The boundary conditions of cooling cycles analyzed and the amount of water exchanged under the discussed conditions for the adsorbents optimal for each zone.  
Figure 4: The operating ranges of the adsorption potential (colored areas) which correspond to the refrigeration cycle (Tev = 3 \({}^{\circ}\)C, \(T\)con = \(T\)ads = \(T\)d.max) for the climatic zones analyzed and the universal curves of water adsorption on the adsorbents, optimal for each zone (Table 1). The refrigerant is water vapor.
## 2 Materials and Methods
### Antimicrobial Activity  
Antimicrobial activity of the six extracts of _G. turuturu_ was evaluated through the microdilution technique [15; 16] with slight modifications, using a fungal strain of _Candidia albicans_ (DSM-1386), the Gram-negative bacterium _Escherichia coli_ (DSM-1103) and the Gram-positive bacterium _Staphylococcus aureus_ (DSM-1104). The _C. albicans_ two-day grown culture (Yeast and Mold Agar; VWR, cc) and the _E. coli_ and _S. aureus_ over-night grown cultures (Nutrient Agar; Sigma, Germany) were dissolved in saline solution (0.85% NaCl; Merck Millipore, Germany) and adjusted to a concentration of 1 \(\times\) 10\({}^{7}\) (for bacteria) and 2 \(\times\) 10\({}^{4}\) CFU-mL\({}^{-1}\) (for fungus). The final inoculum concentrations on the microplates were 5 \(\times\) 10\({}^{5}\) (bacteria) and 1 \(\times\) 10\({}^{3}\) CFU.mL\({}^{-1}\) (fungus), using Mueller-Hinton broth 2 (Sigma, Darmstadt, Germany) and RPMI-1640 (Sigma, Darmstadt, Germany). The positive control of inhibition used for _E. coli_ was Ciprofloxacin (4 \(\mu\)g:mL\({}^{-1}\); Sigma, Darmstadt, Germany), for S. aureus was Tetracycline (16 \(\mu\)g:mL\({}^{-1}\); Sigma, Darmstadt, Germany) and Ambhoricrin B (4 \(\mu\)g:mL\({}^{-1}\); Sigma, Darmstadt, Germany) was used for the _C. albicans_ positive control; 4% (\(\
u\)/\(\
u\)) DMSO (Dimethyl sulfoxide; [PERSON], Spain) was used as negative control of microbial inhibition. _Gratelopain turuturu_ extracts were tested at 0.075, 0.75, 1.5 and 3 mg: mL\({}^{-1}\) (diluted in phosphate saline buffer), using sterile round-bottom microplates (Thermo Scientific, Waltham, MA, EUA). For _E. coli_ and _S. aureus_, the incubation period was 20 h at 35 \({}^{\circ}\)C, and for _C. albicans_, it was 48 h at 35 \({}^{\circ}\)C. After this time, the optical density (DO) was measured at 625 (bacteria) or 530 nm (fungus), in a microplate reader (Epoch2, BioTek, Winooski, VT, USA). The test was performed using 3 independent assays. Results are expressed in percentage of bacterial growth inhibition.
# Experimental Study on Particle Size Distribution Characteristics of Aerosol for Fire Detection
## 3 Results
### Typical Non-Fire Disturbance Aerosol Particle Size Distribution Measurements  
As the most common non-fire interference aerosols, standard dust (JIS Z 8901 Class 11, ISO 12103-1 A1, and ISO 12103-1 A2), water mist, and oil spray are studied by our platform. The particle size distributions of standard dust samples are on the micron scale, mostly ranging from about 1 to 10 \(\upmu\)m. According to the datasheets of standard dusts, the order of particle sizes from small to large is JIS Z 8901 \(<\) ISO 12103-1 A1 \(<\) ISO 12103-1 A2. The results in Figure 5 show a similar trend among different types of dust samples, but the particle size distributions are all smaller than the reference described in the datasheets because larger particles are more difficult to blow up and settle faster. It is similar to the real operation condition where fire smoke detectors are usually fitted to the ceiling and only blowing dust particles can enter the detector chamber.  
\begin{table}
\begin{tabular}{c c c} \hline
**Combustibles** & **Median Particle Size (nm)** & **Standard Deviation** \\ \hline Cotton wicks & 123.0 & 1.74 \\ Fir & 177.3 & 1.65 \\ Bamboo & 104.0 & 1.84 \\ Cotton fabric & 173.0 & 1.69 \\ Cardboard & 229.9 & 1.67 \\ Polyester & 128.8 & 2.08 \\ Papers & 272.2 & 1.69 \\ Woolen thread & 80.6 & 1.52 \\ Nylon & 96.3 & 1.55 \\ PVC (Polyvinyl chloride) & 263.2 & 1.77 \\ Insulation rubber & 137.6 & 1.99 \\ Polystyrene & 80.2 & 1.93 \\ Mosquito coil & 165.1 & 1.85 \\ Polyethylene & 180.3 & 1.54 \\ Kerosene & 300.8 & 1.70 \\ PPR & 278.3 & 1.55 \\ N-Heptane & 308.5 & 1.45 \\ NBR/PVC (Nitrile Butadiene & & \\ Rubber/Polyvinyl chloride) & 176.8 & 1.53 \\ \hline \end{tabular}
\end{table}
Table 1: Statistical parameters of particle size distribution of typical fire smokes.  
Figure 4: Particle size distribution of open fire smokes.  
Water mist is another common source of false alarms, especially prone to trigger false alarms in hotels and kitchens. We tested the particle size distribution of different types of spray on our platform, including high-pressure gas sprays (fire test spray, sunscreen sprays, gels, freshener, and Yunnan Baiyao spray), manually pressed sprays (perfumes, mosquito repellents), and water sprays generated by the fog cabinet and humidifier. The results are shown in Figure 6. It can be seen that most of the spray particles are in the micron range, similar to dust.  
The particle size distribution of typical interference aerosols was fitted according to the experimental data in Figures 5 and 6. It can be seen that the particle sizes of these interfering aerosols generally follow the lognormal distribution. Table 2 summarizes the statistical characteristic parameters of the particle size distribution of typical interference aerosols.  
Figure 5: Particle size distribution of standard dust samples.  
Figure 6: Particle size distribution of spray.
# Solving the Time-Varying Inverse Kinematics Problem for the Da Vinci Surgical Robot
## 3 Inverse Kinematics Solution
### Initial Value of the Inverse Kinematics Solution  
The motion ranges of each joint in the Da Vinci slave manipulator are as follows: \(\theta_{1}\) from \(-90^{\circ}\) to \(90^{\circ}\), \(\theta_{2}\) from \(-135^{\circ}\) to \(0^{\circ}\), \(l_{\rm H}\) from \(0\) to \(315\) mm, \(\theta_{3}\) from \(-180^{\circ}\) to \(180^{\circ}\), \(\theta_{4}\) from \(-90^{\circ}\) to \(90^{\circ}\), and \(\theta_{5}\) from \(-90^{\circ}\) to \(90^{\circ}\). In the previously defined \(\mathbf{x}_{1}=\cos(\mathbf{q}_{1})+\sin(\mathbf{q}_{1})\), \(\mathbf{q}_{1}\) is the rotation angle of the first joint with a range of \([-\pi/2,\pi/2]\). A finite number of values are obtained by extracting values within these available ranges at a certain angle interval (our subsequent calculation results show that a less than \(5^{\circ}\) angle interval is acceptable) to solve the forward kinematics, the results of which are stored as the initial value database for the inverse kinematics solution. By selecting the data from the initial value database that is the least squares approximate to the required inverse kinematics as the initial values, the Newton iteration method will successfully converge.
## 4 Results and Discussion  
### Experimental Design  
In the experimental design, a hierarchical backtesting window approach was adopted to ensure that the evaluation of the model reflected true predictive capability on unseen future data. Specifically, the full time series spanning from 2014 to 2023 was split chronologically into three phases: a training set, validation set, and test set. The training set, comprising 70% of the data, was used for model fitting; the validation set, accounting for 15%, was utilized for hyperparameter tuning and early stopping; and the remaining 15% was reserved as the test set to evaluate the generalization ability on out-of-sample data. Let the total length of the dataset be \(T\), then the training interval has a length of \(T_{\text{train}}=\pi T\), the validation interval is \(T_{\text{val}}=\beta T\), and the test interval is \(T_{\text{test}}=(1-\alpha-\beta)T\), with \(\alpha=0.7\) and \(\beta=0.15\). In terms of prediction granularity, this study focused on generating signals at a daily frequency, where an input sample was constructed each day to forecast either the next day's or the next week's return direction or value. A sliding window mechanism was implemented to extract continuous historical observations of length \(w\) as model input, and the target variable was the return \(\delta\) steps ahead. Given a current timestamp \(t\), the input sequence is denoted as \(x_{t-w+1t}\) and the prediction target is \(r_{t+\delta}\), where \(\delta=1\) indicates one-day-ahead forecasting and \(\delta=5\) corresponds to one-week-ahead forecasting. The sliding window ensures that every training and prediction iteration is based on the most recently available data, thereby aligning with real-world decision-making logic, while improving sample efficiency. This approach enhanced the model's ability to learn temporal dependencies and pattern evolutions in the sequence data, ensuring that the experimental process remained rigorous and that the evaluation outcomes were reliable and practically relevant.
# Bonding Performance of Steel Rebar Coated with Ultra-High-Performance Concrete
## 3 Results and Discussion
### Analytical Modelling of UHPC-Coated Steel Rebars  
#### 3.4.1 Normalized Bond Strength  
The normalized bond strength of the steel rebar embedded in ordinary concrete showed a relationship with \(\tau_{max}\) = 2.0\(\ell_{c}\)\({}^{r0.5}\)[42]. On the other hand, the normalized bond strength of the steel rebar embedded with UHPFC exhibited a very high bond strength. The relationship between the bond strength and compressive strength in the study by [PERSON] et al. [20] was suggested to be \(\tau_{max}\) = 5.0\(\ell_{c}\)\({}^{r0.5}\). In this study, a test specimen was prepared using ordinary concrete, with a thin layer of UHPC coated on the steel bar surface, and the change in bond strength was observed by mixing a certain ratio of structural steel fibers into the concrete matrix. As a result of the test, the normalized bond strength increased to about 2.8 in both the specimen simply reinforced with the UHPC coating and the specimen mixed with a certain ratio of steel fibers (Figure 12).  
Figure 11: Comparison of the initial bond stiffness ratios: (**a**) uncoated rebars [32]; (**b**) UHPC-coated rebars.
# Evaluation of Land Subsidence during Groundwater Recovery
## 3 Methodology
### Soil Parameters  
The soil parameters in this study were adopted from the boring log data on the eight boreholes located in Zone D. The water content, liquid limit, plastic limit, total density, undrained shear strength, and standard penetration test (SPT) results were obtained for each borehole. The initial void ratio was calculated using the phase relationship equation from the borehole parameters. The boring log data were interpreted to obtain the soil parameters to calculate the soil surface displacement based on the one-dimensional consolidation of [PERSON]'s theory. For some soil parameters, we needed to adopt the equations of other researchers to calculate the soil surface displacement. The value of \(\mathrm{Cc}\) is related to the value of water content (Wn), liquid limit (LL), and void ratio (\(\mathrm{e_{o}}\)), as described by [PERSON] [12], [PERSON] [22], [PERSON] [23], [PERSON] [24], and [PERSON] [25]. The soil parameters and thickness of the soil layers were calibrated with the data from the Department of Groundwater Resources. The soil parameters of each soil layer are shown in Table 3. Some parameters could not be gathered from the boring log data. There were some parameters, such as the specific gravity of the soil (equal to 2.7 [1]), the overconsolidation ratio (OCR) [26], and the effective stress parameter as the matrix of the suction coefficient (c) [27,28], that we based on the data reported by [PERSON] et al. [29], which were gathered from the same area.  
\begin{table}
\begin{tabular}{c c} \hline
**Name** & **Description** \\ \hline NP & NahonPathom \\ SS & SamutSakhon \\ BK-1 & Bangkok (Taling Chan) \\ BK-2 & Bangkok (Kholng Sam Wa) \\ NB & Nonthaburi \\ SP & SamutPrakon \\ PT-1 & PathumThani (KholngLuang) \\ PT-2 & PathumThani (Thanya) \\ BH-1 & Chatchak Park \\ BH-2 & Lumphini Park \\ BH-3 & Suea Pa Park \\ BH-4 & Rommannith Park \\ BH-5 & Rajamangala University \\ BH-6 & Kasetsart University \\ \end{tabular}
\end{table}
Table 2: Name and location of each bored hole on the map.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
## 3 Results  
### Performance of GPP Products against EC Tower  
The GPP was extracted from all datasets and validated against the EC tower in Yatir Forest (Israel) for the common period of 2001-2016. Overall, the accuracy metrics suggested a good agreement between the GLASS-GPP and EC-GPP datasets with a Pearson's correlation coefficient of 0.76 and an RMSE of 84 gCm\({}^{-2}\) yr\({}^{-1}\). This finding was confirmed by the Taylor diagram given that the averaged-GPP correlation value was 0.77 but with a higher RMSE value (139 gCm\({}^{-2}\) yr\({}^{-1}\)). In comparison to GLASS-GPP, the other products showed lower correlations (0.52 to 0.61) with EC-GPP (Figure 2 and Table 2).
# Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms
## 3 Methodologies
### Support Vector Machines  
In classification problems, the goal of support vector machines is to find an optimal hyperplane that separates the data of different categories. Its basic principle is to find an optimal decision boundary (or hyperplane) between data points so that the data points of different categories are the farthest away from the boundary. This article uses a nonlinear support vector machine. If the sample can be mapped from the original space to a higher-dimensional space, then a hyperplane can be found to divide the sample in the new feature space and converted into a linearly separable support vector machine.
# Smart Airport Cybersecurity: Threat Mitigation and Cyber Resilience Controls +
## 3 Theoretical Framework
### Airport Intelligence Classification  
As airports have enhanced their interoperability capabilities, by using IoT technology and intelligent applications, to achieve on effectiveness, infrastructure complexity has been enormously increased. According to [PERSON] [1], there is an evolution pace in today's airports, which can be classified into three broad categories: (i) Basic Airports, which focus on capabilities necessary for safe and efficient management of landings, departures, and other aircraft operations, by offering basic passenger services; (ii) Agile Airports, which adapt to this changing digital environment, by offering shared services on a common platform; and (iii) Smart Airports, which fully exploit the power of emerging and maturing technologies of IoT, with advanced and pervasively deployed sense-analyze-respond capabilities. By enabling the exchange of real-time information, profound collaboration, and airport-wide process integration, smart airports significantly improve operational efficiencies, passenger services, and advanced security capabilities.  
According to ENISA, smart airports are those who make use of networked, data driven response capabilities that, on the one hand, provide travelers with a better travel experience and on the other hand, aim to guarantee higher levels of security for the safety of passengers, operators and general public [7]. Since, safety and security are the most significant domains in the aviation context, a safe environment must be ensured by proactively handling difficult cyber challenges, while minimizing operations disruption. In this work, we have concentrated our research on mapping smart airport's threats, initiated from malicious actions, in order to develop a variety of attack scenario analysis, along with recommended mitigations and resilience measures.