esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Dummy Generation Scheme
### Algorithm Framework  
In our scheme, generating dummy locations and query content requires consideration of two conditions: (1) The LBS provider owning users' full information as an active adversary; (2) in our independent architecture, we select dummy locations and query content from historical location datasets and basic service sets which store on the mobile terminal and the mobile terminal has less storage. In the independent architecture, they mean that the client cannot submit the exact locations to the server and only stores fewer data. The process generating dummy locations and query content is as shown in Figure 6. The main steps are as follows:  
* Step 1: The user submits the privacy protection parameters \(<k,s,l>\) and her current location \(L_{0}\) to the server. To ensure that the LBS provider cannot identify \(L_{0}\), we use an algorithm, called the _Area_ generation algorithm, to generate _Area_ containing \(L_{0}\) and submit _Area_, not the exact location \(L_{0}\).
* Step 2: After receiving _Area_, the server generates \(G_{sub}\) (a sub-dataset of \(G\)) and sends it to the mobile terminal. According to the _Area_, the server uses an algorithm, called the historical location sub-dataset generation algorithm, to generate \(G_{sub}\). The \(G_{sub}\) is a small dataset and can be stored on the mobile terminal.
* Step 3: The mobile terminal constructs a query request _Req_ and submits it to the server. In this step, the mobile terminal uses the dummy location generation algorithm to select dummy locations and the dummy query content generation algorithm to select dummy query content. The two algorithms can ensure that the constructed _Req_ meets the privacy protection model.
* Step 4: The server calculates and sends the query results _Res_ to the mobile terminal. To ensure that the user knows the query results and the server does not, we use the user query request construction algorithm to calculate the _Res_.
## 4 Materials and Methods  
We started with five peptides (\({}^{15}\)QKLVFF [20], \({}^{16}\)KLVFF [20], \({}^{17}\)LVFF [20], \({}^{16}\)KLVF [19] and \({}^{15}\)QKLV [18]), to which 14 different organic compounds were attached at the N-terminal sequence of the peptides (lauric acid, hexanoic acid, palmitoyl acid, octanoic acid, decanoic acid, stearoyl acid, taurine, 4-hydroxy-3-methoxymandelic acid, indole-3-butyric acid, 3,5-dinitrosalcylic acid, naphthaleneacetic acid, nicotinic acid, salicylic acid and acetylsalicylic acid). Due to the structural nature of fatty acids, each selected organic compound from this classification (lauric acid, hexanoic acid, palmitoyl acid, octanoic acid, decanoic acid, stearoyl acid) could increase the lipophilicity of the synthesized peptide-based drugs. Hence, the fatty acid might assist a better BBB permeability, as reported by [PERSON] et al. [49]. In addition, taurine directly binds to A\(\beta\) oligomers and recovers cognitive deficits in Alzheimer's model mice [50]. Furthermore, the benzene ring from 4-hydroxy-3-methoxymandelic acid, indole-3-butyric acid, 3,5-dinitrosalcylic acid and naphthaleneacetic acid could also develop the lipophilicity feature. Moreover, nicotinic acid, salicylic acid and acetylsalicylic acid have been previously reported as possessing neurobehavioral recovery and brain plasticity proprieties [51; 52; 53].  
Following preliminary analysis, we synthetized four conjugates from the investigated line-up using solid-phase peptide synthesis (SPPS), based on the peptides with the best administration biocompatibility. Consequently, these peptides were purified using reverse-phase high-performance liquid chromatography (RP-HPLC) and characterized by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF MS), tandem MS and Circular Dichroism (CD).
## 3 Results and Analysis
### CHN6-CUG road dataset  
The CHN6-CUG dataset is from Google Earth, and each image has a resolution of 1 m and a size of \(512\times 512\) pixels. The dataset covers the areas of Shanghai, Macau, Hong Kong, Wuhan, Beijing, and Shenzhen in China. The road markings in the dataset take into account the pavement coverage, distinguishing between tracked and untracked pavements. Analyzed from the point of view of geographic and physical characteristics, the roads are classified to cover a wide range of categories such as railroads, highways, urban roads, as well as rural roads.  
Similarly, multispectral images were obtained using down-sampling. Operations such as cropping and data enhancement are used before the training of the network model, and finally, 6293 images are selected as the experimental objects with an image size of 400 \(\times\) 400. They are divided into the training set and the test set at a ratio of 8:2.
# Port-Related Shipping Gas Emissions--A Systematic Review of Research
## 3 Discussion and Results
### Analysis of the Most Commonly Applied Methodologies and Datasets--Third Layer
#### 3.3.3 POLA and POLB Research  
The Port of Los Angeles' (Port or POLA) annual activity-based emissions inventories serve as the primary tool for tracking the Port's efforts to reduce air emissions from maritime industry-related sources. This study was prepared in coordination with the Port of Long Beach (POLB) and the following air regulatory agencies: the U.S. Environmental ProtectionAgency, Region 9 (US EPA), California Air Resources Board (CARB) and the South Coast Air Quality Management District (South Coast AQMD) [81]. The methodology for estimating emissions was taken from the San Pedro Bay Ports Emissions Inventory Methodology Report, in which the EB approach was applied to every movement activity of OGVs within the harbour district for 40 nautical miles (NMs) [81,82]. The aforementioned methodological background is summarised in Equation (7). The traffic data for the emission estimation is provided through AIS and various statistical reports. The Energy component is determined by combining LF with the time spent in a particular activity mode [82]. Emission sources for all vessel categories include ME (propulsion), AE (generators) and auxiliary boilers (ABs). LF defaults are provided for AE and ABs for all movements (Transit, Manoeuvring, At Berth, Shift, At Anchorage), while ME load is estimated through propeller law [81,82]. In addition, average values of vessel characteristics relevant to emission estimation are introduced. The mentioned data correspond to the OGV traffic in the port area.  
\[\text{E}_{\text{i}}=\text{Energy}_{\text{i}}\times\text{EF}\times\text{FGF} \times\text{CF} \tag{7}\]  
where:  
\(\text{E}_{\text{i}}\): Emissions by mode--in grams (g);  
\(\text{Energy}_{\text{i}}\): Energy demand by mode as the energy output of the engine(s) or boiler(s) over the period of time--in grams per kilowatt hour (g/kWh);  
EF: Emission factor depends on engine type, IMO tier and fuel used--in grams per kilowatt hour (g/kWh);  
FCF: Fuel correction factors are used to adjust from a base fuel associated with the EF and the fuel being used--dimensionless;  
CF: Control factor(s) for emission reduction technologies--dimensionless.
# Transforming Customer Digital Footprints into Decision Enablers in Hospitality
## 4 Results
### Emotion-Aspect Representation and Segmentation of Reviews  
Figure 8 displays the emotion aspect profile derived from a comprehensive online review, showcasing the intensities of emotions towards both best-performing and poor-performing hotel aspects. The discernible differences in emotion variations and the distinct characteristics of emotions and aspects underscore the emotion-aspect profile's role as a unique and granular representation of customer satisfaction. Hence, this representation can be aptly described as a \"digital representation of customer satisfaction\".  
It can be observed that the aspects 'Staff' and 'Food&Beverage' have a higher intensity of 'Anger' and 'Sad' expressed in reviews compared to other aspects. Moreover, the aspect 'Room' has shown higher intensity in 'Disgust'. In positive emotions, it can be seen that 'Public Areas' and 'Bathroom' have increased intensity in 'Surprise', denoting a positive experience. The aspects 'Access' and 'Room' have mixed emotions of 'Disgust', 'Joy' and 'Trust'. The spectrum of emotions per each aspect denotes how customers position their experiences and enables them to identify the best- and worst-performing services of a hotel.  
Subsequently, the emotion-aspect profiles of the reviews were vectorized and utilized as input data for the GSOM algorithm. For the experiment, we aggregated reviews specific to a particular hotel and generated a GSOM representation. The segmentation output by the GSOM is depicted in Figure 9. In the GSOM visualization, each node represents an online review and the nodes positioned in closer proximity (within the same neighbourhood) indicate similarity in their content. Based on this premise, we can identify distinct profiles of reviews for a given hotel.  
As depicted in Figure 9, the GSOM algorithm distinctly segregates the reviews based on the different aspects being discussed. Clusters 1, 2 and 3 predominantly exhibit negative emotions, yet there is a clear separation in concerns, with Cluster 1 focusing on public areas, Cluster 2 on location and Cluster 3 on bathroom. On the left side of the GSOM map, the clusters reflect more positive emotions, with Cluster 4 showing mixed emotions for room and bathroom, and Cluster 5 displaying heightened positive emotions towards staff.  
Notably, the positive emotions cluster is positioned far from the other clusters, indicating a distinct separation. This GSOM representation not only facilitates the segmentation of online reviews but also offers a holistic view of the reviews for a specific hotel. It enables an insightful understanding of how emotions and concerns are related to different aspects  
Figure 8: Emotion-aspect profile of a hotel based on online reviews. Red colour denotes the negative emotions while positive emotions are shown in green.  
of the hotel experience, providing valuable information for decision-making and customer service enhancements.
# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 3 Mainstream Models and Methods
### Analytical Modeling
#### 3.2.4 Ensemble Learning  
Ensemble learning accomplishes the task by constructing multiple learners; the two most common types of ensemble learning are Bagging and Boosting. Table 6 summarizes research on the application of ensemble learning algorithms in taxi time prediction over the last five years. In 2018, [PERSON] et al. [54] established a macro network topology from the perspective of aggregation, and compared the accuracy of the linear regression, support vector machine, and random forest three machine learning methods; and the results show that the prediction accuracy of the random forest model is significantly better than the other two models under one month training samples. In 2021, [PERSON] et al. [6] combined the takeoff rate saturation curve to establish a departure aircraft taxi-out time prediction model based on random forest to optimize the control of pushback rate. [PERSON] et al. [64] for the first time considered the runway operation mode, cheap/non-cheap airlines and aircraft speed characteristics, compared five prediction models, Multi-Layer Perceptron (MLP), Linear Regression (LR), Polynomial Regression (PR), Gradient Boosting Regression Trees (GBRT), and Random Forest (RF), and the results showed that the random forest-based prediction model was superior to other prediction models. In addition, the important features are extracted from the random forest model using backward exclusion, which is a quantitative analysis of the importance of features that is rarely seen in previous studies. [PERSON] et al. [72] considered the controller's decision preferences and achieved high-precision taxi-out time prediction using both random forest regression and linear regression. In 2022, [PERSON] et al. [66] established a prediction model based on random forest regression and kernel density estimation, and used the kernel density estimation method to fit the set of results predicted by the decision tree with probability distributions, to obtain the probability density function of the taxi time; this method can analyze the probability distribution of the taxi time of a single aircraft while predicting the uncertainty of the taxi time. [PERSON] et al. [32] selected the XGBoost algorithm to construct the arrival and departure aircraft taxi time prediction model, this algorithm is better than the random forest and support vector machine algorithms and at the same time, the impact of the sample data volume on the accuracy of the prediction time is analyzed for the first time.
# Integrity Monitoring of PPP-RTK Positioning: Part II: LEO Augmentation
## 4 LEO Augmentation
### Orbital Errors
Based on Equation (10), the daily averaged standard deviations of the orbital influences on different baselines are shown in Figure 8b. The blue and red dots represent the LEO orbits with an altitude of 975 and 1100 km, respectively (see Section 2.1). The daily averaged standard deviations are calculated as the square root of the averaged \(\sigma_{rv}^{2}\) for all satellites in the corresponding LEO layer and all epochs on the test day 26 June 2021, with a sampling interval of 1 min. From Figure 8b it can be observed that the \(\sigma_{rv}\) is generally below 1 mm for the tested LEO constellation, even for baselines over 500 km. The influences of orbital errors are thus not further considered in this study. However, as shown by the lower values of the red dots than the blue dots, it is visible that the altitude of the LEO satellites plays an important role in the \(\sigma_{rv}\). For LEO satellites with lower altitudes, or for orbital products with significantly lower precision, the influences of the orbital errors need to be assessed for the corresponding situation, and if necessary, considered in the IM. Figure 8: (**a**) Location of assumed baselines; (**b**) Daily averaged standard deviations of the orbital influences on the baselines, i.e., \(\sigma_{rv}\) (Equation (10)). The standard deviations of the orbital errors are assumed to be 1 cm in the radial, along-track and cross-track directions each.
## 5 Discussion
### Effect of Experimental and Simulation Parameters
#### 5.1.1 Effect of Deep Rolling Pressure  
The deep rolling pressure significantly affects the degree of plastic deformation and the resulting compressive residual stresses within the material. In our experiments and simulations, the applied pressure was set at 150 bar (experimentally) and 157 bar (FEA), leading to substantial residual stress concentrations directly under the tool. Similar findings have been reported by [PERSON], [PERSON] et al. [29], who observed that higher deep rolling pressures resulted in increased depth and magnitude of compressive residual stresses. In our study, we found that a slight increase in pressure, as seen in the FEA simulation, leads to marginally higher stress concentrations, which is consistent with the trends reported by [PERSON], [PERSON] et al. [30]. Their research indicated that compressive stress layers deepen as the pressure increases, but they also warned of the risks of over-pressurizing, which could lead to tensile stresses in deeper layers. In our work, no such negative effect was observed at the applied pressure level, suggesting that the optimal pressure range was maintained for inducing beneficial compressive stresses without risking over-deformation.
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 1 Introduction
### Main Contributions  
* This study evaluates metrics often overlooked in prior research, such as energy consumption and jitter.
* It provides an in-depth analysis of hybrid protocols, demonstrating their adaptability and efficiency across diverse scenarios.
* It offers practical guidelines for selecting the most suitable routing protocols for specific applications, based on network requirements.
* This study fills gaps in prior research by focusing on underexplored metrics, such as jitter and energy consumption, and by providing a detailed evaluation of hybrid protocols in dynamic MANET environments.  
The rest of this paper is organized as follows: Section 2 reviews related work and highlights the gaps addressed by this study. Section 3 describes the routing protocols, categorized into proactive, reactive, and hybrid types. Section 4 explains the simulation setup and the metrics used for evaluation. Finally, Section 5 presents the results, summarizes the findings, and suggests directions for future research.
## 3 Materials and Methods
### Proposed Method
#### 3.1.4 DeepFM with and without Concurrent Purchase Information  
In this study, we employed two variations of the DeepFM model: DeepFM with concurrent purchase information (_DeepFM\({}_{\textit{with}}\)_) and DeepFM without concurrent purchase information (_DeepFM\({}_{\textit{without}}\)_). These two configurations are designed to evaluate the impact of concurrent purchase data on the model's performance. Additionally, the data used to create the DeepFM models were identically applied to the XGBoost model to ensure consistent comparisons.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**User Purchase History** &
\begin{tabular}{l} **Next** \\ **Purchase Product** \\ \end{tabular} & **Label** \\ \hline
9 May 2023 09:533 & \(\pm\)140:94 & \(\pm\)100:94 & \(\pm\)15 & \(\pm\)15 & \(\pm\)15 \\
3 May 023 09:533 & \(\pm\)140:94 & \(\pm\)100:94 & \(\pm\)100:94 & \(\pm\)  
Features Used in the Models  
The following features were used in both variations of the DeepFM model:  
* 1 st_purchase_product_id to 5 th_purchase_product_id: The product IDs of the last five purchases made by the user.
* 1 st_concurrent_purchase_flag to 5 th_concurrent_purchase_flag: Binary flags indicating whether each of the last five purchases was part of a concurrent purchase. These flags are used only in _DeepFM\({}_{with}\)_ to distinguish concurrent purchases.
* 6 th_purchase_product_id: The product ID of the predicted next purchase.
* label: A label indicating whether the prediction was accurate (1 for correct predictions, 0 for incorrect).  
DeepFM with Concurrent Purchase Information  
In this configuration, the model is enhanced with additional features, specifically the 1 th_concurrent_purchase_flag to 5 th_concurrent_purchase_flag, that indicate whether multiple items were purchased together within the same transaction. This setup helps the model capture the context and relationships between concurrently purchased items, which can lead to more accurate predictions.  
DeepFM without Concurrent Purchase Information  
In this configuration, the model processes the data without any indication of concurrent purchases, meaning the 1 th_concurrent_purchase_flag to 5 th_concurrent_purchase_flag are not included. The features provided to the model are limited to basic item attributes, user information, and purchase sequences, but do not include any explicit information about whether multiple products were bought together. This serves as a baseline to determine how much the concurrent purchase information contributes to the model's effectiveness.  
By comparing the results of these two DeepFM configurations, we can assess the significance of concurrent purchase data in improving recommendation accuracy. This analysis contributes to understanding the role of additional contextual information in enhancing the performance of recommendation systems in e-commerce environments.
# Influence of Soil Parameters on Detecting Voids behind a Tunnel Lining Using an Impact Echo Method
## 1 Introduction
Nevertheless, compared to the inspection of piles or plates, it is far more difficult to apply to tunnel inspection as a result of the complicated soil-structure interaction, as well as the wave propagation and reflection, in a 3D structure. For a long tunnel, the soils behind the tunnel are inhomogeneous and anisotropic. The characteristics and parameters (such as mass density, wave velocity and damping) of the surrounding rocks/soils vary at different tunnel mileages. However, few existing studies have analysed whether alteration of soil parameters affects the NDT results. In this study, a principal experiment was designed and performed in the laboratory. The voids behind the concrete plate, modelling the tunnel lining, were designed to model the ineffective contact between the soil and tunnel. The IE method was employed to inspect the existence of voids by means of different signal analysis approaches. Then, by changing the soil parameters, the influence of soil parameters on detecting voids behind the tunnel lining was analysed. Finally, a finite element (FE) numerical simulation of the IE process and parameter analysis were accomplished using the software ABAQUS (Abaqus/cae 6.14.4, Dassault Systemes, Welize-villacubrai, France).
# A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation
## 5 Factor Analysis to Characterize Expense, Call, and SMS Attributes
### Factor Analysis of Telecom Customer Calls  
#### 5.2.1 Variable Selection  
Customer call data, such as data for total monthly calls, long-distance calls, and roaming calls, are suitable for use in a factor analysis to investigate the main factors influencing customer preference for the service provider [37]. Factor analysis was conducted on several variables, including customer call data, to identify the main factors determining customer loyalty. It was concluded that better call quality and service will positively influence customer loyalty [38]. Thus, the telecom customers' call data were selected to conduct the following factor analysis. The following call-related factors were used to conduct the factor analysis and analyze the characteristics of cost factors: (1) total monthly traffic MOU; (2) total monthly caller MOU; (3) total monthly called MOU; (4) total local MOU; (5) total local called MOU; (6) total long-distance MOU; (7) total roaming MOU. Later KMO and Bartlett tests of sphericity were applied to identify whether these factors were suitable for factor analysis.