esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Physicochemical Data
Values are mean \(\pm\) standard error (in = 4 for each season). There were no significant differences (\(p\) > 0.05) between inlet and outlet in what concerns the values of pH and conductivity. pH values from inlet and outlet ranged from 6.18-7.61 and 6.50-8.02, respectively, whereas conductivity varied from 158-2120 to 100-1050 \(\upmu\)S/cm, respectively. These values are within the range of previous assessments on this CW system [1, 28]. The variation in terms of removal efficiencies along the seasons is shown in Figure 2. According to [PERSON] et al. [1], it is expected that the composition of the wastewater at the CW inlet and outlet varies throughout the year, along seasons, being directly related with overnight guest stays. This type of wastewater is thus typically characterized by flow oscillations and high variability in its composition. The ratio of BOD/COD showed a mean value of 0.4 \(\pm\) 0.1, considered within the range of typical values for untreated municipal wastewater [32]. For an inlet COD of 518 \(\pm\) 188 mg/L the average removal efficiency attained was 88 \(\pm\) 7%, reaching 99%. For an inlet BOD\({}_{5}\) of 213 \(\pm\) 73 mg/L the average removal efficiency attained was 91 \(\pm\) 4%, reaching 97%. In relation to the other parameters (TSS, PO4\({}^{-3-}\), NO\({}_{2}\)\({}^{-}\), NO\({}_{3}\)\({}^{-}\), NH\({}_{4}\)\({}^{+}\)), the removal efficiency varied on average between 61% and 86%. There were no significant (\(p\) > 0.05) differences between seasons regarding the removal efficiencies for all the analyzed parameters (COD, BOD\({}_{5}\), TSS, PO4\({}^{-3-}\), NO\({}_{2}\)\({}^{-}\), NO\({}_{3}\)\({}^{-}\)). However, the percentage of removal of NH\({}_{4}\)\({}^{+}\) was significantly (\(p\) < 0.05) higher in H3 if compared to the other sampling campaigns. The removal efficiency of this CW was in general considered high and within what was expected when taken in consideration past trails in the same CW [1].
## 5 Experiments and Analysis
### Feature Engineering for Regression  
#### 5.1.1 Lasso Regression  
Using Lasso regression, we determine which characteristics contributed most to the prediction of the target class, \"weighted_score\", in a regression issue using this class as the target. The characteristics \"code_module\", \"imd_band\", and \"total_click\" have coefficient scores of 3.43, 0.31, and 9.95, respectively, making them the best predictors of the target variable. This indicates that these characteristics significantly affect the anticipated value of \"weighted_score\". The method's results show the success of Lasso regression in feature selection and finding the most important characteristics for prediction.
## 2 Results and Discussion
### Specific Energy Consumption  
Profiles of specific energy consumptions are shown in Figures 3 and 4. Curve shapes revealed that as moisture content decreases, specific energy consumption increased, with a drastic increment at the final stage of drying, as characterized by the critical moisture content, \(K\). The \(K\) values of all drying methods (below 0.1847 kg water kg\({}^{-1}\) dw) are indicated in Figures 3 and 4. The shape of characteristic curves resulted from the course of the drying kinetics shown in Figure 1a,b. The shape of the drying curves in Figure 1a,b shows that moisture removal was time consuming when moisture content was low. The prolonged time period to remove moisture at the final stages of drying corresponded to the high energy requirement for the removal of the same amount of moisture at the final stage of drying compared to initial drying stages. This behavior is usually seen in plant materials with osmotic cellular structure to limit the escape of moisture from plant tissues [21].  
Figure 2: Drying rate for _P. nodiflora_ leaves dehydrated using CD method at 40, 50, and 60 \({}^{\circ}\)C, VMD at 6, 9, and 12 W/g, and VMFD at 9 W/g preceded by CPD at 50 \({}^{\circ}\)C.  
VMD required less energy to remove moisture compared with CD. However, energy consumption of CD can be minimized with VMFD, reducing final specific energy consumptions of CD by 80.2% and 51.1% (kJ/g water). CPD reduced water load by 92.4%, from an initial moisture content of 7.032 to 0.537 kg water kg\({}^{-1}\) dw. Drying intensity was also an influencing factor on the respective specific energy consumptions. Higher drying intensities in terms of increased hot-air temperature and microwave wattage reduced final specific energy consumptions (Figure 5), which is consistent with observations of past reports [21; 22].  
Figure 4: Profile of specific energy consumption related to the gram of water evaporated from sample of _P. nodiflora_ leaves, dried using CD, VMD, and CPD–VMFD methods.  
Figure 3: Profile of specific energy consumption for drying a gram of _P. nodiflora_ leaves, dried using CD, VMD, and CPD–VMFD methods.
## 3 Results
### Change of ZnO Concentration with a 0.20 Step Size  
Considering that there is very little change in the ZnO-related reflections in our samples and no change in prominent Zn\({}_{2}\)TiO\({}_{4}\)-related reflections, we optimized the ZnO ratio in a comparably larger step of 1.80:1.00 (samples A08), 1.60:1.00 (sample A09), and 1.40:1.00 (sample A10). The results of the three samples are shown in Figure 3a,b. Interestingly, the prominent small peaks that were obtained in Sections 3.2 and 3.3, for example, at 31.75 \(2\theta\) degrees, are not obtained now. Instead, a new reflection at 32.75 \(2\theta\) degree is observed. This reflection was also observed in sample A01, cf. Figure 1a,b. The origin of the two reflections cannot be the same as they are obtained at different experimental conditions. While looking into the literature, we observe that the reflection at 31.75 \(2\theta\) degrees is due to ZnO in wurtzite phase, while the 32.75 \(2\theta\) degrees reflection is due to zinc blende-type ZnO [14]. This new reflection can also be associated with Zn\({}_{2}\)Ti\({}_{3}\)O\({}_{8}\) phase. However, the position of Zn\({}_{2}\)TiO\({}_{4}\) related reflections is unaffected by the change in ZnO concentration up to sample A10.  
To further understand the effect of a decrease in the concentration ratio of the two precursors, the amount of ZnO is decreased to 1.20:1.00 (sample A11) and 1.00:1.00 (sample A12). For these samples, several additional reflections are observed in the pXRD pattern (results not shown). These additional reflections are present due to a mix of several phases from the ZnO-TiO\({}_{2}\) family. As the present work focuses on Zn\({}_{2}\)TiO\({}_{4}\) synthesis and its optimization, we choose to continue our investigations on samples with codes between A01-A10 only. As an important observation from the present results, we infer that sample A10 with ZnO:TiO\({}_{2}\) concentration of 1.40:1.00 gives optimized pXRD results where the reflections due to unreacted ZnO vanish, while the main reflections due to Zn\({}_{2}\)TiO\({}_{4}\) are conserved.
## 6 Security Analysis
### Informal Security Analysis
#### 6.1.2 Resistance to Impersonation Attacks  
Similar to Section 6.1.1, an adversary \(\mathcal{A}\) possesses the values stored in the user's mobile device. To impersonate the user and generate a login request message, \(\mathcal{A}\) requires the user's \(UI_{i}\). However, \(\mathcal{A}\) cannot obtain \(UI_{i}\) without knowing the user's real identity \(ID_{i}\), password \(PW_{i}\), and random number \(r_{i}\). Thus, our scheme defends against user impersonation attacks. Furthermore, if \(\mathcal{A}\) captures the sensor \(IoT_{SL_{j}}\), \(\mathcal{A}\) can obtain \(\{SID_{j},SA_{j},CH_{j}\}\). However, \(\mathcal{A}\) cannot compute \(SI_{j}\) because the PUF of the sensor cannot be duplicated. Therefore, our scheme prevents sensor impersonation attacks.
## 5 Site Experiment Results
### Deformation Prediction Comparison
In practice, the positional coding value in the testing stage remains unknown. Therefore, the auto-aggression method was adopted to generate the transformer model's prediction output. Due to the influence of the construction activities in the tunnel, the monitoring value has errors, but it can still reflect the trend of tunnel face deformation. The predicted value by the transformer model can accurately capture the same trend as the monitored deformation measurements.
# Person Identification and Gender Classification Based on Vision Transformers for Periocular Images
## 3 Materials and Methods
### Proposed Approach
#### 3.4.2 Proposed Model  
After the extraction of the ROI from the input image, the resultant region is subjected to following set of steps in order to develop the ViT model during the training phase. The trained model is stored in the system for testing purposes. During the testing phase, the model predicts labels. These predictions are utilized to compute accuracy, which determines the overall performance of the trained model.  
* The initial step of development is to apply pre-processing algorithms like resizing to 224 \(\times\) 224 and normalizing the images in sync with the image-net dataset, whose pre-trained weights are utilized for training, which forms the backbone of the architecture.
* After this pre-processing step, the images and corresponding labels are randomly shuffled and passed as batches to the model to obtain the raw logits. The images are first broken down into patches and flattened using the linear projection matrix, and the positional embeddings are then added to it.
* The transformer encoder block, analogous to a block contemplated by [PERSON] et al. [25], consists of self-attention blocks, normalization layers, fully connected layers, and residual connections. The attention blocks are multi-headed, and hence they can focus on different patterns of the image.
* The embedding pertaining to the terminal FC layer is then passed to two pooling layers-the average pool and the max pool. These are then concatenated before finally being passed to the classification head.
* Instead of the custom train-test split method, a stratified k-fold with k = 5 is used to counter-balance the class imbalance. Instead of randomly splitting the dataset, this ensures that the classes are equally stratified into each fold and hence the model becomes less prone to overfitting.
* The fully connected layer will output the desired class prediction using the Softmax function, and the class having the foremost value becomes the predicted class.  
Step 7--Setting this reliable cross-validation strategy is beneficial during inference because now we have five folds, after completing training for every fold, and we can take the mean of the model predictions as the final class.  
The proposed architecture is shown in Figure 6. It may be noted that the average pool and max pool layers are added and concatenated in the ViT architecture before processing the vectors with the MLP head.
## 3 Materials and Methods
### Data Cleaning and Preprocessing
#### 3.1.1. Inputting Missing Values and Eliminating Outliers  
In this task we proceeded to separate the data set into numerical with 40 columns and categorical 330 columns, then imputed by mode for the categorical variables. According to the Ref. [40,41] this procedure consists of filling in the missing data for each variable by mode when it is a categorical variable and by median for numerical variables. In relation to  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Name** & **Value** \\ \hline Rows & 15,087 \\ Columns & 942 \\ Discrete columns & 66 \\ Continuous columns & 873 \\ All missing columns & 3 \\ Missing observations & 4,717,895 \\ Complete rows & 0 \\ Total observations & 14,211,954 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overall summary of ENDO-2018 data set.  
Figure 3: Chi-Square scores in the prediction of teacher job satisfaction.  
the numerical variables, it is important to clarify that this data set presented extreme values, so it was decided to impute the missing values by the median, because this measure is more representative and is not affected by the presence of these. These types of imputations in the statistical field are called simple imputation methods [42; 43].  
The Local Outlier Factor (LOF) algorithm identified 1785 instances as outliers in the numerical data set, and after identification these instances were removed in parallel in both data sets. For this algorithm the degree to which an object is extreme depends on the density of its local neighborhood, and it is possible to assign a LOF value to each object that would represent the probability that it is an extreme value [44]. For this technique we identified outliers as points with considerably lower density than their neighbors.
## 3 Modeling of the System and Optimization
### Exergy Model  
Exergy balance equation of the CV at steady state can be written as [14,23]:  
\[\dot{E}_{in}+\dot{E}_{out,f}+\dot{E}_{l}+\dot{E}_{d}=0 \tag{11}\]  
The exergy input to the system comes from the exergy of the flowing fluid and the insolation. The exergy inflow from the flowing fluid is [26]:  
\[\dot{E}_{in,f}=\dot{m}[(h_{in}-h_{a})-T_{a}(s_{in}-s_{a})+\frac{c^{2}}{2}+gz] =\dot{m}[\int_{T_{a}}^{T_{in}}C_{p}(T)dT-T_{a}\int_{T_{a}}^{T_{in}}\frac{C_{p}( T)}{T}dT+\frac{c^{2}}{2}+gz] \tag{12}\]  
After some simplification considering constant specific heat and using [PERSON]'s equation, it can be written as [14,25]:  
\[\dot{E}_{in,f}=\dot{m}C_{p}(T_{in}-T_{a}-T_{a}\ln(\frac{T_{int}}{T_{a}}))+ \frac{\dot{m}\Delta P_{in}}{\rho} \tag{13}\]  
The exergy input from the insolation is [26]:  
\[\dot{E}_{in,solar}=G_{T}A_{a}\psi \tag{14}\]  
Conversely,  
\[\dot{E}_{in,solar}=G_{T}A_{a}[1-\frac{4}{3}\frac{T_{a}}{T_{s,b}}+\frac{1}{3}( \frac{T_{a}}{T_{s,b}})^{4}] \tag{15}\]  
where \(\psi\) is the Petela efficiency [31], written as:  
\[\psi=1-\frac{4}{3}\frac{T_{a}}{T_{s,b}}+\frac{1}{3}(\frac{T_{a}}{T_{s,b}})^{4} \tag{16}\]  
However, this equation (Equation (16)) violates the Second Law of Thermodynamics for this type of systems [14]. In terms of apparent sun temperature, the corrected equation is [32]:  
\[\dot{E}_{in,solar}=G_{T}A_{a}(1-\frac{T_{a}}{T_{s}}) \tag{17}\]Similar to the inflow exergy, the exergy outflow of the system can be written as [14; 26]:  
\[\dot{E}_{out,f}=-\dot{m}C_{p}(T_{out}-T_{a}-T_{a}\ln(\frac{T_{out}}{T_{a}}))- \frac{\dot{m}\Delta P_{out}}{\rho} \tag{18}\]
## 3 Proposed Methodology
### Dataset Preprocessing  
The values in all features are converted into numeric form, even if those are in categorical format. After obtaining the refined numeric values in feature columns, clamping is performed with certain conditions as described in Equations (1)-(3).  
\[F_{1}=Max(FV_{i})>10 \tag{1}\]  
Equation (1) represents a flag \((F_{1})\) that is made to check if the iterating feature vector \((FV_{i})\) maximum value is greater than 10 times:  
\[F_{2}=Med(FV_{i})*10>F_{1} \tag{2}\]  
The second flag \((F_{2})\) in Equation (2) checks whether the feature vector value is 10 times greater than the median of all data of this particular feature: \((FV_{i})\) maximum value is more than 10 times or not.  
\[Prun_{x}=if(F_{1}\&F_{2})=1 \tag{3}\]  
In Equation (3), if the feature vector values are flagged to true means, then at a particular instance of the feature vector, the value is found to be 10 times greater than the median of this feature, and then that particular instance value with 98 th percentile of that column should be replaced. In this way, all values of all features do not go beyond the limits and are pruned over the 98% percentile. The clamping plays an important role in normalizing the values.  
The greater the categorical values, the greater the skewness.Therefore, in order to reduce the skewness among those features that have more than 50 categorical values, the log function is applied, and the mathematical representation is shown in Equation (4):  
\[Norm_{FV}=log(Prun_{i}) \tag{4}\]  
Figure 2: Features histograms for 18 columns in original dataset (**Left**) and GAN-based dataset (**Right**).  
In Equation (4), we can see that all features that are collected after performing the pruning operations are given to a log function to remove the skewness in those columns that have uniqueness greater than 50 times.
# Toward Long-Term Implantable Glucose Biosensors for Clinical Use
## 1 Introduction  
Diabetes mellitus is a chronic disease that affects more than 400 million people worldwide [1]. Since high or low blood glucose concentration episodes and high glycemic variability can cause diabetic complications (e.g., diabetic retinopathy, kidney failure, heart disease, diabetic neuropathy, and diabetic foot disease) and even lead to death, it is crucial for diabetic patients to maintain their blood glucose concentration in a normal range and to decrease glycemic variability [2; 3; 4; 5]. To maintain blood glucose concentration in the normal level and reduce blood glucose variability, people with diabetes must first understand their blood glucose concentrations. Blood glucose concentration measurement is accompanied with painful finger-pricking. Such intermittent monitoring is not only painful, but also ineffective to prevent abnormal blood glucose concentrations between measurement points [6]. Alternately, continuous glucose monitoring (CGM) is proposed. CGM can constantly measure blood glucose concentrations and give users alarm when abnormal blood glucose concentration occurs (Figure 1). Thus, CGM enables to reduce occurrence of hypo- or hypergly and high glycemic variability [7; 8]. An implantable sensor is a key component of continuous glucose monitoring system (CGMS). The implantable sensor decides comfort or pain during wearing CGMS, accuracy, and lifespan of CGMS. Since Minimed released the first CGMS in 1999, CGMS technologies have been innovated to open the new era of glucose monitoring: Zero-finger-pricking, continuous glucose monitoring [9; 10; 11; 12]. In this review, we emphasize commercial CGMS technologies including sensor requirements and the state of the art. We also introduce regulation change in CGMS to help researchers develop commercial-quality CGMSs.
# Collaborative Caching for Implementing a Location-Privacy Aware LBS on a MANET
## 6 Performance Evaluation
### Privacy Loss  
To evaluate privacy protection effectiveness, we analyze privacy loss patterns across different k-anonymity values for all three approaches. Our analysis is based on entropy-based privacy metrics established in information theory [31] and follows standard practices in k-anonymity evaluation [3,4].  
Figure 5 demonstrates several key insights aligned with the privacy-preserving LBSs literature [2,8]:  
1. _Anonymity-Privacy Relationship_: All approaches show a decrease in privacy loss as k increases, validating theoretical predictions about the effects of the size of the anonymity set [36]. This confirms that larger anonymity sets strengthen privacy protection even in distributed environments.
2. _Comparative Performance:_ The baseline consistently shows a higher loss of privacy (110.5 K to 67.3 K) compared to our collaborative approaches (M-LBSv1: 72.5 K to 48.3 K; M-LBSv2: 75.7 K to 50.4 K). This substantial improvement comes from reduced centralized LBS queries through effective collaborative caching.
3. _Diminishing Returns:_ Privacy improvement rates diminish at higher k-values, particularly beyond k = 6, which is in line with recent research on the preservation of privacy by LBSs [2]. This suggests that there is an optimal k-value that balances privacy protection with system overhead.
## 3 Problem Description and Formulation  
### The Complete and Incomplete Version of the ITLP  
The ITLP aims to determine which of a set of potential sites locations to open as terminals and how to route the supply and demand of a set of customers (representing zones of supply and demand) through the network. The transportation of these demands may be done using a unimodal link (road mode) or a combination of two modes (rail-road). The objective is to minimize the total cost of the transportation as well as the installation cost of terminals. Up to now, the studied version of the ITLP introduced by [1; 25], studied by [27; 29], etc. assumes that the inter-terminals network is a complete graph. Figure 1 shows a small complete intermodal terminal network with three terminals \(T_{1}\)-\(T_{3}\) and 5 customers (1-5).  
As in hub problems, the assumption that the induced graph by the located terminals must be a fully-connected graph (complete graph) is a restrictive constraint that may increase the total investment cost. Moreover, this restriction is non-realistic since, in real life cases, a rail-road network is usually incomplete due to the heavy cost incurred for the construction of its infrastructure. The current work relax this assumption and proposes a  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Research Type** & **References** \\ \hline Deterministic models & [1; 5; 9; 10; 11; 13; 14; 17; 25; 26; 27; 32] \\ Stochastic/fuzzy models & [3; 7; 8; 12; 15; 16; 34; 42; 48; 49] \\ Exact methods & [1; 7; 8; 11; 25] \\ heuristic methods & [10; 12; 15; 27; 32; 50] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Intermodal Terminal Location Problem: models and solving methods.  
Figure 1: Small complete network.  
general model in which the located rail-road terminals are partially connected. Figure 2 shows a small incomplete intermodal terminal network. The induced graph by terminals is not incomplete. For instance, there is no rail link between rail terminals \(T_{1}\) and \(T_{2}\).
## 5 Applications
### Intelligent Driving  
Human activity recognition techniques are also employed to assist drivers by providing different cues regarding the state of the driver while driving a vehicle. It has been reported that the secondary tasks performed by the drivers such as answering the phone, sending or receiving text messages, eating or drinking while operating a vehicle cause inattentiveness which may lead to accidents [196; 197]. A multi-modal vision frame-work for driver's activity recognition was proposes in [198]. This method extracted the head, eyes, and hand cues to describe the state of the driver. These cues were fused using support vector machine for activity classification. Another technique for driver-activity recognition was proposed in [199]. This method is based on head and eye tracking of the drivers while operating the vehicle.
# Realistic Simulation for Body Area and Body-To-Body Networks
## 3 Proposed Simulator
### Deterministic (Bio-Mechanical) Radio Link and Mobility Modeling  
Wireless body-to-body networks (BBN) are a relatively new dimension of BAN in which multiple bodies interact and share certain information. In this section, we will explain various cross-layer components of the BBN system, which have a direct impact on the performance evaluation of BBN. The accurate mobility, path loss and channel models are essential to gain more insight into the performance of wireless communication stacks under real deployment and operating assumptions [58; 66; 67]. This is especially true in the context of BANs and BBNs, whose radio channels might undergo harsh multi-path fast fading and time-varying slow fading due to human body shadowing effects [68]. To that end, we consider in this work the intra-BAN biomechanical mobility and radio link models, which were recently introduced in [66], and we extend them to handle the inter-BAN case.