esa-sceva/satcom-chunk-collection huggingface dataset:
# Revisiting Low-Resolution Images Retrieval with Attention Mechanism and Contrastive Learning
## 3 Results
### Quantitative Results
#### 3.3.4 The Effectiveness of Loss Components  
We study the effectiveness of loss components that impact the performance of image retrieval. We add auxiliary loss functions such as triplet loss and classification loss into the contrastive loss to train the model end-to-end. In particular, we experiment with a tuple of three parameters \((\alpha,\beta,\gamma)\in\{0,\ 1\}^{3}\) that characterize the presence of self-contrastive loss (\(\alpha=1\)), otherwise supervised contrastive loss (\(\alpha=0\)), the presence of classification loss (\(\beta=1\)), and triplet loss (\(\gamma=1\)). As mentioned in the previous section, the classification loss used in this study is the cross-entropy loss with label smoothing (\(p=0.1\)) and temperature scaling (\(\tau=0.5\)). Otherwise, the temperature scaling factor in contrastive loss is set to 0.5. In addition, the margin parameter in triplet loss is set to 1. Table 5 demonstrates that the case of using supervised contrastive loss in combination with classification and triplet loss achieves the best performances (\(mAp=0.9379\)). The experiment also shows that stand-alone contrastive loss is not enough to accomplish the category image retrieval task, as shown in the cases of self-supervised contrastive loss (\(mAP=0.6026\)) and supervised contrastive loss (\(mAP=0.6214\)). In addition, the effectiveness of classification loss is higher than that of triplet loss, as shown in the case of (self-)contrastive loss combined with classification loss (\(mAP=0.9120\), \(mAP=0.8929\)), compared to the case of (self-)contrastive loss combined with triplet loss (\(mAP=0.8445\), \(mAP=0.8910\)). The results confirm that classification loss is a good option to supply category information for image retrieval tasks.
## 2 Design and Simulation
When the IL is 3 dB or higher, the signal will continue to be at half strength or less. This means that in order to comprehend the signal, the IL must be lower than 3 dB. The power of the signal remains over 50% while being filtered out, as shown in the simulated results of the proposed filters. After simulating all four filters according to the requirements, the next step was to design the layout of SP4T switches, and the specification of the switch is already mentioned in Table 2. The layout of the SP4T switch can be seen in Figure 10. There are twenty-four total pins on it, five of which are used for input and output, three for control, one for operating voltage, and one for ground. The other pins are all grounded as well. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **BPF 1** & **BPF 2** & **BPF 3** & **BPF 4** \\ \hline Centre & & & & \\ Frequency & 2.1 & 2.4 & 3.2 & 4.0 \\ (GHz) & & & & \\ \(L_{1}\) & 3.41 & 3.41 & 3.41 & 3.41 \\ \(L_{2}\) & 10.77 & 10.76 & 10.77 & 10  
There are a total of four states that the system can provide: channel 1 enabled only (00), channel 2 enabled only (01), channel 3 enabled only (10), and channel 4 enabled only (11). Some capacitors and resistors are used at the control and voltage pins according to the characterisation circuit of the switch. The list of SMD components integrated with it is shown in Table 4. The layouts of all four bandpass filters were then integrated together on a single substrate along with two SP4T switches, shown in Figure 11. The total size of the filter bank is 129.579 mm \(\times\) 99.916 mm. \begin{table}
\begin{tabular}{c c} \hline \hline
**Components** & **Value** \\ \hline C2 & 1 uF \\ C7, C8, C9, C10 & 100 pF \\ R7, R8, R9, R10, R11, R12 & 0 M\(\Omega\) \\ R16, R17, R18 & 1 M\(\Omega\) \\ S2 & HSWA4-63 DR+ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: SMD components and their values. Figure 11: Three-dimensional layout of designed RF switched filter bank. Figure 10: Layout of SP4T switch and SMD components.
## 3 Simulation Model Development
### Simulation Setup  
The Ansys-CFX solver is used for the calculation by assuming the working fluid is an ideal gas. The working process is set as an adiabatic process, and the wall condition is assumed to be adiabatic. The SST turbulence model is adopted in the simulation and second order precision schemes are used. The pressure boundary condition is used for the inlet and outlet.  
For a fluid of mixture, the gas constant can be defined by the mole fraction of each component:  
\[R_{g}=\frac{R}{\Sigma n_{i}\cdot M_{i}} \tag{1}\]  
where \(R\) is general gas constant which equals to 8.314 J/(mol\(\cdot\)K).  
In addition, its volumetric heat capacity \(c_{v}\) and isobaric heat capacity \(c_{p}\) are determined by the mass fraction of each component:  
\[c_{p}=\Sigma x_{i}\cdot c_{p_{i}}\]  
\[c_{v}=\Sigma x_{i}\cdot c_{p_{i}}\]  
Assuming that the mixture is an ideal gas, its isentropic index \(\kappa\) can be defined as follows:  
\[\kappa=\frac{c_{p}}{c_{v}}=\Sigma x_{i}\cdot\kappa_{i} \tag{2}\]  
The mole fraction of hydrogen \(n_{H_{2}}\) can be obtained according to [PERSON]'s law of partial pressure. According to the working principle of a fuel cell stack, a lot of water during operation will be produced,  
Figure 3: Simulation model of the Roots pump: (**a**) fluid domain; (**b**) rotor grids.  
which needs to be separated before entering the Roots pump as shown in Figure 1. Thus, the mixture entering the Roots pump is saturated with water vapor.  
\[n_{H_{2}}=\frac{p_{s}-p_{sat,water}(T_{s})}{p_{s}}-n_{N_{2}} \tag{3}\]  
where \(p_{s}\) is the suction pressure, \(T_{s}\) is the suction temperature and \(p_{sat,water}\) is the saturation pressure of water vapor at a specific temperature.
## 2 Proposed Methodology
### Performance Evaluation Metrics  
Accuracy assessment is very important in evaluating the classification performance of machine learning algorithms. The performance of the proposed ensemble classification model was valued using specific performance metrics selected from [43].  
* Accuracy (A): This is the ratio of the model's true predictions to the overall prediction. Mathematically, it can be formulated as Equation (5). \[Accuracy\left(A\right)=\frac{T_{P}+T_{N}}{T_{P}+T_{N}+F_{P}+F_{N}}\] (5) where \(T_{P}\) denotes true positive, representing the accurately predicted positive cases. \(T_{N}\) represents true negatives, indicating the accurately predicted negative cases. \(F_{P}\) corresponds to false positives, showing negative events wrongly predicted as posi  
Figure 8: SqueezeNet architecture for PQDs classification. Here, ReLU is an acronym for activation function.  
tive; \(F_{N}\) represents false negatives, representing the positive occurrences incorrectly predicted as negative.
* Precision (P): this denotes the ratio of accurately predicted positive occurrences out of the total number of predicted positive occurrences and is expressed as Equation (6). \[Precision=\frac{T_{P}}{T_{P}+F_{P}}\] (6)
* Recall (R): this refers to the proportion of accurately predicted positive instances among all instances in the class and can be stated as Equation (7). \[Recall=\frac{T_{P}}{T_{P}+F_{N}}\] (7)
* F1-score: this denotes a weighted mean of the precision and recall, formulated as Equation (8). \[F1-score=\frac{(2\times P\times R)}{(P+R)}\] (8)
## 5 Experimental Results
### MLOps Model and Latency  
In this study, the processes of developing a LightGBM-based regression model, containerization, distribution through web services, and subsequent monitoring and updating are discussed. The LightGBM model was developed based on the characteristics of the dataset and the target variable. After the model training was completed, it was prepared in the Docker package. The integration of the LightGBM model into Docker enabled the model to work consistently across different environments.  
The model is exposed as a web service using Representational State Transfer (RESTful) APIs at the application layer. These APIs have made it possible for users and applications to remotely call the model and make predictions by providing data inputs. API testing and development tools such as Postman were used to test and monitor the performance of the model after it was presented via web services at the application layer. In this process, the response time performance of the model was evaluated by making API calls based on various scenarios via Postman. In the experiments carried out, 100 different values were prepared and sent to the API sequentially. Thus, information about the response time of the model was obtained. The average response time (latency) of the model was determined to be 9 ms.  
Figure 9: Comparisons of LightGBM models.
## 3 Results and Discussion
### Modelling and Analysis of Variance(ANOVA)
#### 3.2.2 Graphical Response of Input Factor Interaction  
Three-dimensional (3D) response surface plots (Figure 4) revealed the visualization of the interaction effect of the input variables on the output responses. This was carried out by varying the input variables within the ranges to obtain a high peak. It was deduced that with a high nanoparticle load (A) of 0.6 g and a biochar load (B) of 1.5 mgVS/L, a high pH of 7.14 was achieved (Figure 4). This correlated to the fact that decreasing the AB load had a significant impact on the ionic binding of the substrate and the acidogenic and methanogenic activities about biogas production. Thus, a nanoparticle load (A) of the range 0.3-0.6 g and a biochar load (B) of the range 0.9-1.2 mgVS/L resulted in the highest biogas production at a level of 24 mL/day (Figure 4). On the other hand, the lower load of biochar (B) at 0.5 mgVS/L within any load of the nanoparticle (A) in a range of 0.2-0.6 g resulted in 78% COD removal. The effectiveness of lower proportions of the biochar might be due to the inadequate availability of pollutant molecules to absorb the excess active surface of the biochar at higher loads (B > 0.5 mgVS/L) [48,49]. The above trend was also in agreement with the result obtained by [20,49].
## 2 Materials and Methods
### Evaluation Methods  
#### 2.4.1 Object Detection  
We used MATLAB to classify coronary arteries as RCA, LCA-LAD, and LCA-CX. For our primary evaluation metric, we adopted Average Precision (AP), which integrated both precision and recall. Following a common convention, we set IoU \(\geq\) 0.5 as the criterion for a correct detection and calculated AP at this threshold. We then split the original set of 52 subjects (19,047 images) into training and test subsets in a 4:1 ratio. We performed five-fold cross-validation to capture the potential variations across the different patient subsets  
Figure 1: Example of ROI annotation and bounding box placement, illustrating alignment with arterial lumens, adjustments for branching or overlapping segments, and classification rules for coronary. (**a**) A fixed-size region of interest (ROI) of 64 \(\times\) 64 pixels is set so that the blood vessel is centered in the cross-section. (**b**) If a transverse blood vessel is continuously visualized without interruption, the ROI is expanded until it fits within a single ROI. (**c**) A blood vessel running horizontally in the cross-sectional image but appearing interrupted is assigned separate ROIs. (**d**) If the cross-sections of two arteries do not fit within a 64 \(\times\) 64 pixel ROI, two ROIs are assigned. (**e**) Left main trunk (LMT) is defined as left anterior descending artery (LCA-LAD). (**f.h**) If other vascular branches are visualized, the label of the most prominently visualized vessel is assigned. (**g**) Even if a blood vessel fits within a single 64 \(\times\) 64 pixel ROI, separate ROIs are defined if the vessel segments are different.  
and to estimate the model's ability to generalize. We then calculated AP by graphing precision against recall and measuring the area under the precision-recall curve, producing a single score between 0 and 1. Higher AP values indicated superior performance in terms of localization and classification accuracy.
# Multi-Objective Optimization for High-Dimensional Maximal Frequent Itemset Mining
## 2 Related Concepts  
### Association Rules  
Association rule mining is an important branch in big data and data mining technology. The concept of association rules is proposed to search the relations of data in the transaction database. Such relations are defined as association rules, such as \(A\Rightarrow B\), where \(A\) and \(B\) both are sets of items (itemsets). \(A\) is called the former item of association rules, and \(B\) is the rear item of association rules. If \(D=\{T_{1}\), \(T_{2}\), \(T_{3}\),..., \(T_{k}\}\) is the big data transaction set that is used to extract the association rule, then \(T_{i}=\{\textit{l}_{1i}\), \(\textit{l}_{2i}\), \(\textit{l}_{3i}\),..., \(\textit{l}_{k!}\}\) is a transaction in the transaction sets. If \(I=\{\textit{l}_{1}\), \(\textit{l}_{2}\),..., \(\textit{l}_{m}\}\) is the set of all items, then \(T_{i}\) is the non-void subset \(T_{i}\subseteq I\) of \(I\). If \(A\Rightarrow B\) is a rule, then \(A\subset I\), \(B\subset I\), and \(A\cap B=\emptyset\). In association rule mining, the support degree (_support_) and confidence (_Confidence_) are used to measure the quality of one association. These two parameters are defined as  
\[support(A\Rightarrow B)=P\Big{(}A\bigcup B\Big{)} \tag{1}\]  
\[confidence(A\Rightarrow B)=\frac{P(A\bigcup B)}{P(A)} \tag{2}\]  
where \(P(X)\) is the probability of occurrence (_support_) of the itemset \(X\) in the objects \(D\) of transaction sets. Thus, the _support_ and _Confidence_ of the corresponding association rule \(A\Rightarrow B\) could be derived if \(P(A\bigcup B)\) and \(P(A)\) are known, and whether association rules are strongly correlated can be determined. Hence, the problem of exploring the association rules can be summarized as the exploration of a frequent itemset.
# Direct-Write Spray Coating of a Full-Duplex Antenna for E-Textile Applications
## Materials and Methods
### Antenna Fabrication
(**b**) Direct-write spray coating of barium titanate (BT) UV curable dielectric ink onto the knit fabric substrate assembly (six layers of knit fabric laminated with a thermoplastic polyurethane (TPU) web) to modify the dielectric constant. (**c**) Final assembly of the printed textile-based antenna (two layers of nonwoven fabric and six layers of knit fabric spray coated with BT dielectric ink). Exploded views indicate the different layer compositions. (**d**) Final product image of the printed textile-based antenna. A separate TPU web was used as an encapsulation layer to protect the antenna. Two standard SubMiniature version A (SMA) connectors with a 50 \(\Omega\) characteristic impedance were soldered with Chipquik SMDSWLLTFP32 (a low-melting-temperature solder) onto the patch and ground patterns to form a connection. The metal in the solder melted at 170 \({}^{\circ}\)C, which enabled robust connections without damaging the textile component of the antenna. An additional UV-curable encapsulation (Dymax 9001-E-V 3.5) was applied to the edges of the solder areas between the textile antenna and connectors. The encapsulation protected the antenna from breaking and ensured a strong mechanical and electrical connection between the textile antenna and SMA connectors. The final product can be seen in Figure 2d.
# A Blockchain-Assisted Security Protocol for Group Handover of MTC Devices in 5G Wireless Networks
## 6 Security Evaluation
### Formal Proof by BAN Logic
#### 6.1.1 Formalized Protocol  
To idealize the protocol, we describe the messages in the proposed protocol as follows:  
**Messages-1**: The s-gNB sends \(\{TIDs\}\) to AMF.  
**(M1) \(AMF\triangleleft TIDs\)**  
**Messages-2**: The AMF sends \(\{(TID_{1}||\ldots||TID_{i}),\{GID,TS_{t}\}_{\delta},MAC\}\) to s-gNB.  
(M2) \(s\)-gNB \(\triangleleft\{\{TID_{1}||\ldots||TID_{i}\},\{GID,TS_{t}\}_{\delta},MAC\}\)  
**Messages-3**: The s-gNB sends \(\{GID,TS_{t}\}_{\delta},MAC\) to MTCD.  
(M3) \(MTCD\triangleleft\{\{GID,TS_{t}\}_{\delta},MAC\}\)  
**Messages-4**: The \(MTCD_{1}\) sends \(\{TID_{1},TS_{1},n_{1}\cdot p,Sig_{1}\}\) to s-gNB.  
(M4) \(s\)-gNB \(\triangleleft\{TID_{1},TS_{1},n_{1}\cdot p,Sig_{1}\}_{PK^{-1}}\)  
**Messages-5**: The s-gNB sends \(\{TID_{1},n_{1}\cdot p,Sig_{1}\}\) to AMF.  
(M5) \(AMF\triangleleft\{TID_{1},n_{1}\cdot p,Sig_{1}\}_{PK^{-1}}\)  
**Messages-6**: The AMF sends \(\{GID,TS_{1},(TID_{1}||\ldots||TID_{i}),n_{1}\cdot p,Sig_{1}\}\) to t-gNB.  
(M6) \(t\)-gNB \(\triangleleft\{GID,TS_{1},(TID_{1}||\ldots||TID_{i}),n_{1}\cdot p,Sig_{1}\}_{PK ^{-1}}\)  
**Messages-7**: The t-gNB sends \(\{TID_{t},GID_{1},TS_{t},n_{t}\cdot p,Sig_{t}\}\) to \(MTCD_{1}\).  
(M7) \(MTCD_{1}\triangleleft\{TID_{t},GID_{1},TS_{t},n_{t}\cdot p,Sig_{t}\}_{GID}\)  
**Messages-8**: The \(MTCD_{1}\) sends \(\{GID_{1},TID_{t},TID_{1},TS_{1}\}\) to \(MTCD_{1}\).  
(M8) \(MTCD_{i}\triangleleft\{GID_{1},TID_{u},TID_{1},TS_{1}\}_{GID}\)  
**Messages-9**: The \(MTCD_{i}\) sends \(\{TID_{i},TS_{i},n_{i}.p,Sig_{i}\}\) to \(MTCD_{1}\).  
(M9) \(MTCD_{1}\triangleleft\{TID_{i},TS_{i},n_{i}.p,Sig_{i}\}_{PK^{-1}}\)  
**Messages-10**: The \(MTCD_{1}\) sends \(\{(TID_{2}||\ldots||TID_{i}),n_{2}.p||\ldots||n_{i}p,Sig,TS_{i}\}\) to t-gNB.  
(M10) \(t\)-gNB \(\triangleleft\{(TID_{2}||\ldots||TID_{i}),n_{2}.p||\ldots||n_{i}.p,Sig,TS_{i} \}_{PK^{-1}}\)  
**Messages-11**: The t-gNB sends \(\{TID_{u},TS_{u},n_{i}.p,Sig_{i}\}\) to \(MTCD_{1}\).  
(M11) \(MTCD_{1}\triangleleft\{TID_{u},TS_{u},n_{i}.p,Sig_{i}\}_{GID}\)  
**Messages-12**: The \(MTCD_{1}\) sends \(\{TID_{u},TS_{1},TS_{t},n_{i}.p,Sig_{i}\}\) to \(MTCD_{1}\).  
(M12) \(MTCD_{1}\triangleleft\{TID_{u},TS_{1},TS_{t},n_{i}.p,Sig_{i}\}_{GID}\)  
We refer to all MTC devices, including MTCD and \(MTCD_{i}\), as MTCD.
## 2 Materials and Methods
### Statistical Analysis  
The main and interactive effects of straw return modes and soil depths on SOC, total N, available N, total P, and available P were assessed by two-way ANOVA. The differences of SOC, total N, available N, total P, available P, and maize yield among straw return modes as well as among soil depths were analyzed by one-way ANOVA as the data contained a normal distribution (Shapiro-Wilks) and variances were homogenous (Levene, both \(p>0.05\)), and the data were log-transformed if necessary. Multiple comparisons were performed using least significant difference (LSD) test at significance level of \(p<0.05\). Pearson correlation coefficients were used to examine the relationships between SOC, total N, total P, available N, available P, and maize yield at significance levels of \(p<0.05\) and \(p<0.01\). Statistical tests were carried out with SPSS software (version 22.0; SPSS Inc., Chicago, IL, USA).  
The similarities and distinctions of SOC, total N, total P, available N, and available P contents among straw return modes were determined using Principal component analysis (PCA) with Canoco software (version 5.0; Microcomputer Power, Ithaca, NY, USA). The major soil properties affecting maize yield were determined by redundancy analysis (RDA), and the Monte Carlo Permutation test (499 permutations) at significance level of \(p<0.05\) was used to assess the significance of explanatory environmental variables.  
To test the causal relationships among straw return modes, soil layer depths, SOC and nutrient contents, and maize yield, a structural equation model (SEM) was constructed using AMOS software (version 24.0; SPSS Inc., Chicago, IL, USA). The most optimum model was acquired by gradually removing the insignificant paths. The probability (\(P\)), insignificant chi-square test (\(\chi^{2}\)/df), goodness-of-fit index (GFI), and root mean square error of approximation (RMSEA) were used to evaluate the fitness of final model.
# Imaging the Permittivity of Thin Film Materials by Using Scanning Capacitance Microscopy
## 1 Introduction
When an insulating film with thickness \(h\) and relative permittivity \(\varepsilon_{r}\) was coated on the Si substrate ground, each line was scanned two times. The topography image and SCM image (with the tip lifted to a proper height \(z\)) were acquired almost at the same time. Step 4. From the SCM image, every signal of SCM could be changed to the value of \(\frac{\text{d}C^{dielectric}_{\text{total}}}{\text{d}z}\) at the corresponding point. By using \(R_{0}\), \(\theta_{0}\), \(z^{\prime}\), and \(k^{dielectric}_{stray}\) et al. for data reconstruction, every value of \(\frac{\text{d}C^{dielectric}_{\text{total}}}{\text{d}z}\) could be changed to the value of \(h/\varepsilon_{r}\) at the corresponding point. The topography image was used to determine the thickness \(h\) of the film at every point. Then, \(\varepsilon_{r}\) at every point was synthesized and the 2D distribution of \(\varepsilon_{r}\) on the nanometer scale could be obtained. This method of measuring permittivity of thin films can be applied to semiconductor materials and photoelectric materials, obtaining the two-dimensional doping type and doping level distribution, and providing an effective tool for manufacturing process testing such as semiconductor devices and optical appliances. In addition, this method plays an important role in photonics in different applications [13].
## 2 Pharmacology of Piperine: In Vitro and In Vivo Studies  
### Immunomodulatory and Anti-Allergic Effect  
The immunomodulatory potential of piperine have demonstrated promising potential. [PERSON] et al. [9] reported that the incubation of tumor cell lines with 5-fluorouracil (5-FU) in the presence of piperine produced an increase in growth inhibition, observed by lower IC\({}_{50}\) values for 5-FU. At the same time, leucopoenia induced by treatment with 5-FU was reduced by the combined use with piperine, showing improved immunocomp hampered by 5-FU [9]. In the study of [PERSON] et al. [10], which evaluated the effect of piperine to B cell functioning and on the humoral immune response to T-un/dependent antigens, it was found that, in vitro, it inhibits proliferative response induced by lipopolysaccharideacharide (LPS) and immunoglobulin \(\alpha\)-IgM antibody. Also, piperine resulted in inhibition of IgM antibody secretion and reduced expression of cluster of differentiation CD86 [10]. A recent study of [PERSON] et al. demonstrated that piperine in combination with gamma-aminobutyric acid (GABA) mediated p38 and JNK MAPK activation, which increased EPO and EPO-R expression, resulting in up-regulation of IL-10 and NF-kB [11].  
In addition to immunomododulation, piperine exhibits significant anti-allergic activity in ovalbumin-induced allergic rhinitis in mice. Piperine significantly ameliorated sneezing, rubbing, and redness induced by sensitization of nerve endings resulted from histamine released in response to antigen-antibody reaction [12], but also decreased nitric oxide (NO) levels due to lower migration of eosinophils into nasal epithelial tissue. As in the histopathological section of the nasal mucosa, it was found that piperine treatment attenuated inflammation, redness, and disruption of alveoli and bronchiole [12]. In an ovalbumin-induced asthma model, the administration of piperine decreased the infiltration of eosinophils and reduced airway hyperresponsiveness by suppressing T cell activity and Th2 cytokine production [13].