esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Overall System Design

Table 5: Environmental impact of baseline buses. case, zones 1, 3, 4, 9, 11, 14, and 15 are still within the group. As a result, the efficiency will be higher due to removing zones with higher losses. Zones such as 2, 5, 6, 7, 8, 10, 12, and 13 were not included in the calculation in this scenario. Less energy is generated in the third scenario (34,696.00 MWH/Year), but it still covers the needs of the proposed systems. Scenario 4 is tackling the highest efficiency by incorporating the lowest losses zones. This includes zones 4, 9, 11, and 14. Despite the efficiency being at higher limits in this scenario, it excludes most of the zones from the calculations. The result is a lower amount of energy that could be insufficient to cover the project needs. Scenario five is selecting zones which are close to parking areas. These areas are large and close to the energy consuming systems. In scenario 6, zones next to Mahatta parking only are considered. That is because Mahatta parking is a potential area for BRT buses to park overnight. Therefore, most of the consumption is expected to be there. It can be seen that energy produced is less than what the green streets model requires. The score for each scenario is depicted in Figure 4 by utilizing Equation (1), and the results of each zone's specifications in Table 3.  
Based on these results, scenario 3 has the highest score of (93.3/100), making this scenario the best candidate. Scenario 3 meets capacity requirements; has lower losses than scenarios 1, 2, 5, and 6; and has a high specific yield. Scenario 3 suggests installing PV panels with an overall capacity of 20,017.00 kW in order to generate 34,696,000.00 kWh per year. This energy is slightly above the required energy needed to run the green street model (32,000,000.00 kWh/Year); therefore, this scenario covers and exceeds energy demand with a slight indirect safety factor. Table 7 lists the details of scenario 3 design. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scenario** & **Scenario Definition** & **Equivalent** & **Area** & **Pnom** & **Net Production** \\
**Number** & & **Length (m)** & **Ang. ** & **Directions (From** & **Shading** & **Kwp** & **Orth
###### Abstract  
To investigate the hysteretic behavior of an X-shaped metal damper (XMD) with an oblique angle, cyclic loading tests were carried out on nine specimens, including two XMDs without buckling-restrained devices, four XMDs with stiffening ribs, and three XMDs with cover plates as references. The test results showed that the oblique angle could effectively increase the stiffness, strength, and energy dissipation of the XMD. When the oblique angle of an XMD with stiffening ribs increased from 0\({}^{\circ}\) to 30\({}^{\circ}\) at the applied displacement of 8.4 mm, the mean strengths and cumulative energy dissipation of specimens increased by about 80.77% and 80.57%, respectively. Although asymmetric hysteretic loops were also observed in specimens with an oblique angle and buckling-restrained devices, stable hysteretic curves were obtained. This indicated that the stiffening ribs and cover plates can effectively constrain the buckling behavior of XMDs. Additionally, the mean strengths of specimens with stiffening ribs were a little higher than those of specimens with cover plates. Subsequently, the finite element analysis models of the XMDs were proposed, in which the metal plasticity model considering isotropic and kinematical hardening was used to model the material properties of steel, and the simulation results matched well with the test results. Finally, the theoretical calculation method was proposed to predict the elastic stiffness of specimens, and the theoretical elastic stiffness matched well with the test results.  
X-shaped metal damper; oblique angle; hysteretic behavior; numerical simulation; theoretical analysis 2023
# Stripe-Assisted Global Transformer and Spatial-Temporal Enhancement for Vehicle Re-Identification
## 4 Experiment
### Implementation Details  
All our experiments were conducted on eight Nvidia Tesla T4 GPUs with the PyTorch Toolbox. Both training images and testing images were resized to 256 \(\times\) 256. The training images were augmented with horizontal flipping and random erasing. Four images for each identity were sampled in a training mini-batch. We employed the stochastic gradient descent (SGD) to optimize our model with 300 epochs, where the momentum and the weight decay were set at 0.9 and 0.0001, respectively. The learning rate was initialized to 0.045 with a linear warmup and cosine learning rate decay.  
For our model, we initialized the 13 transformer layers with the pre-trained weight of the ViT on ImageNet, where 11 transformer layers were utilized to embed the basic features, 1 layer was applied to extract a global feature, and 1 shared layer was employed to construct the stripe-based features. Each transformer layer consisted of an eight-head self-attention module. We split the original image into 256 non-overlapping patches with size 16 \(\times\) 16 as the input of the model and constructed \(K=8\) horizontal stripe-based features. The feature dimension was 768. The width of bin \(\Delta t^{\prime}\) was set to 4 s, and variance \(\sigma^{2}\) was set to 100 in the STPro stream.
## 3 Proposed Method and Mathematical Analysis
### Encryption and Decryption Methodology  
#### 3.3.1 Arnold Transform  
There are various methods for encrypting existing images; the Arnold Transform is used in this paper. The traditional Arnold Transform uses 2D transformation, but the 2D transformation of Arnold is easier to crack due to the transformation period and transformation step, and the encryption effect of the 2D transformation is not as secure as the 3D encryption effect. Hence, this paper uses 3D Arnold encryption, where the original image cannot be cracked by brute-force methods alone.  
As this paper upgrades the 2D Arnold transformation into a 3D Arnold transformation, the coefficient matrices A and B are also transformed into third-order matrices, and the transform coefficients are increased from two coefficients in the 2D Arnold transformation to six coefficients, both of which are innovations that increase the complexity of the algorithm as well as the security of the final result. The transformation coefficients are also randomly generated by random numbers from (1, 1,000,000,000), and new random numbers are generated for each operation, both of which greatly enhance the security of the algorithm. Following Figure 1, after the Digital Signature operation, the Arnold Transform is used to perform encryption. The first steps of encryption are as follows:  
The generalized 3D Arnold Transform is Equation (3):  
\[\begin{pmatrix}x^{\prime}\\ y^{\prime}\\ z^{\prime}\end{pmatrix}=A\begin{pmatrix}x\\ y\\ z\end{pmatrix}mod\ N \tag{3}\]  
with the 3D Arnold Transform being Equation (4):  
\[\begin{pmatrix}x\\ y\\ z\end{pmatrix}=B\begin{pmatrix}x^{\prime}\\ y^{\prime}\\ z^{\prime}\end{pmatrix}mod\ N \tag{4}\]  
Matrix B is the inverse matrix of matrix A in Equation (5).  
\[\begin{array}{l}A=\left[\begin{array}{ccc}a_{11}&a_{12}&a_{13}\\ a_{21}&a_{22}&a_{23}\\ a_{31}&a_{32}&a_{33}\end{array}\right]\\ a_{11}=1+\text{ace}\\ a_{12}=\text{c}\\ a_{13}=\text{c}+\text{ac}+\text{abce}\\ a_{21}=\text{f}+\text{ae}+\text{acef}\\ a_{22}=\text{cf}+1\\ a_{23}=\text{bf}+\text{abcef}+\text{acf}+\text{abe}+\text{a}\\ a_{31}=\text{ade}+\text{e}\\ a_{32}=\text{d}\\ a_{33}=\text{abde}+\text{ad}+\text{be}+1\end{array} \tag{5}\]  
After the above equations, it is clear that the transformation coefficients a, b, c, d, e, and f in the Arnold Transform are important for encryption and decryption, and that obtaining the above data will lead to a complete conclusion. Therefore, it is extremely important to encrypt the above data.  
The encryption process used in 3D Arnold-encrypted images ensures that the image cannot be easily hacked or manipulated, making it ideal for use in applications where security is a top priority.
## 3 Results and Discussion
### Analysis of FTIR Spectroscopy
\begin{table}
\begin{tabular}{c c c} \hline
**Wavenumber (cm\({}^{-1}\))** & **Functional Group** & **Vibration Type** \\ \hline \(2921\) & C–H (alkanes) & Asymmetric stretching \\ \(2853\) & C–H (alkanes) & Symmetric stretching \\ \(1458\) & C-H (methylene) & Bending (scissoring) \\ \(1376\) & C–H (methyl) & Symmetric bending \\ \(722\) & C-C (chaines longues) & Rocking of methylene \\ \hline \end{tabular}
\end{table} 
Table 3: Characteristic FTIR absorption bands of virgin mineral oil [30; 31]. Figure 8: ATR-FTIR analysis of fresh and aged natural esters (**a**) eN 1204 and (**b**) eN 1215 after breakdown. Figure 7: ATR-FTIR analysis of fresh and aged natural esters (**a**) eN 1204 and (**b**) eN 1215 before breakdown. Figures 10 and 11 present the ATR-FTIR spectra of paper samples impregnated with the different oils, both in the fresh state and after aging, before and after dielectric breakdown. The FTIR spectra of impregnated paper uncovered several structural changes induced by thermal aging (Table 5):  
Progressive reduction of the O-H band (\(\sim\)3300 cm\({}^{-1}\)) indicates the breaking of hydrogen bonds, reflecting a loss of molecular cohesion and hydrophilicity. We found this decrease is most pronounced for Polaris GX (\(-\)38% between S0 and S3), compared to \(-\)22% for NE 1204 and only \(-\)13% for NE 1215. Decline of C-O bands (\(\sim\)1030-1060 cm\({}^{-1}\)), associated with the glucosidic structure of cellulose, signals a degradation of the polysaccharide backbone. The measurements show the intensity loss reaches \(-\)40% at S3 for Polaris GX, indicating accelerated depolymerization. In contrast, we observed the reductions are moderate for NE 1204 (\(-\)27%) and minimal for NE 1215 (\(-\)14%), demonstrating better preservation of cellulose chains. An increase in carbonyl (C=O) bands on the paper is most evident with Polaris GX, as confirmed by the C=O/C-O intensity ratio, which rises from 0.28 at S0 to 0.71 at S3 (+154%). Notably, this increase is lower for NE 1204 (+92%) and NE 1215 (+49%), confirming that paper oxidation is significantly more advanced when impregnated with mineral oil. Figure 9: ATR-FTIR analysis of fresh and aged mineral oil (a) before and (b) after breakdown. Figure 10: ATR-FTIR analysis of fresh and aged paper before breakdown. (**a**) eN 1204, (**b**) eN 1215, and (**c**) MO. Figure 11: ATR-FTIR analysis of fresh and aged paper after breakdown. (**a**) eN 1204, (**b**) eN 1215 and (**c**) MO.
## 2 Geological Prospecting and Field Monitoring
### Geological and Hydrogeological Conditions  
The geological prospecting team obtained geotechnical test samples by drilling in the field and then analyzed them through a series of tests. Moreover, microtremor exploration detectors were arranged at the locations of detection holes ZK082, ZK085, ZK086, and ZK093, respectively. The analysis of geological drilling exploration verified the reliability of the microtremor exploration results. The geological conditions in the interval mainly include: 1-1 gravel fill, 1-2 plain fill, (20)-a1 fully weathered argillaceous siltstone, (20)-a2 strongly weathered argillaceous siltstone, (20)-a3 moderately weathered argillaceous siltstone, (20)-c2 strongly weathered tufaceous gravelly sandstone, and (20)-c3 moderately weathered tufaceous gravelly sandstone. The phreatic pore aquifer in the survey scope is characterized by a shallow burial, small water volume, and significant variation. The dynamic change amplitude is generally about 1.0-2.0 m. During the survey, the measured phreatic water was 1.40 m to 5.80 m below the overburden depth. The geological profile of the monitoring section is shown in Figure 2, and the specific soil physical and mechanical parameters are shown in Table 1.  
Figure 1: Project Overview.  
Figure 2: Longitudinal geological profile of the interval tunnel.
# A Method for Processing Static Analysis Alarms Based on Deep Learning
## 5 Experiments and Results
### Research Questions and Findings
#### 5.3.2 RQ2: Effectiveness
The bidirectional recurrent neural network designed in our study achieved a recall rate of 98.07% and an AUC score of 98.3% in predicting actionable alarms. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Average Precision** & **Average Recall** & **AUC** \\ \hline [PERSON] et al. [34] & 81.72\% & 73.09\% & 77.16\% \\ [PERSON] et al. [35] & 92.14\% & 87.6\% & 97.3\% \\ Our CNNs & 96.49\% & 93.50\% & 97.6\% \\ Our BiRNNs & 95.77\% & 98.07\% & 98.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of performance with relevant research methods. Figure 9: ROC curves and AUC scores.
## 1 Introduction  
The latest COVID-19 spread has highlighted the importance of mobile energy supply systems for temporary hospitals, mobile testing laboratories, and first aid services, as well as for self-isolation units. Despite the ongoing COVID-19 pandemic, a gradual recovery is already taking place in many countries, meaning the return to such activities as outdoor festivals, summer camps and other outdoor recreation events. These activities usually take place in temporary tents without any ventilation. Quickly deployable sustainable temporary structures provide ventilation and adequate indoor quality and comfort conditions for people who have to be isolated or treated (mobile hospitals) or for those who have to stay in quarantine during an epidemic or pandemic. Temporary structures have been widely used on various occasions since the beginning of the COVID-19 outbreak as a low-cost and fast solution to accommodate the growing number of patients [1]. Quickly deployable energy supply systems can also be used for setting up refugee camps (shelters). About 82.4 million people worldwide were forcibly displaced by the end of 2020 [2]. There are around 900 registered refugee camps, and supposedly around 500 unregistered refugee camps worldwide [3]. In addition to refugee shelters, current data estimates show that there are around 500 temporary military camps deployed across the world. About 20,000 troops around the world are currently deployed in NATO missions [4]. Energy consumption in such campuses (refugee camps, military camps, etc.) is much higher in comparison to regular buildings, as the building envelope has a crucial role in maintaining heat balance and preserving heat loss from the building, accounting for up to 60% of building thermal performance [5], while in temporary shelters and tents the envelope oftentimes consist of light material with little to no insulation and protection from outdoor conditions. For instance, a recent study suggested that energy consumption in military facilities significantly exceeds that of residential buildings, mainly due to the poor thermal properties of military building stock [6].
# Fast Performance Modeling across Different Database Versions Using Partitioned Co-Kriging
## 5 Experimental Evaluation
### Evaluation of Prediction Accuracy  
**Data collection**. We use the random sampling strategy to generate a set of configurations for each database and test them on the database system with given workloads on different versions to get the performance measurements. A configuration-performance pair is regarded as a sample. The numbers of samples for MySQL, PostgreSQL, SQLite, Redis, and Cassandra are 294, 300, 280, 323, and 1129, respectively. The collection of all source samples serves as auxiliary training data. In the target domain, a subset of these samples is selected randomly for training dataset; the remaining samples serve as the testing dataset. **Evaluation metric**. Holdout validation is employed to compare the prediction accuracy between different methods. We use the training dataset and auxiliary training data to generate a performance model for each method, and then use this model to predict performance values of configurations in the testing dataset. We select _Mean Relative Error (MRE)_ as a metric for evaluating prediction accuracy, which is computed as follows:  
\[MRE=\frac{1}{N}\sum_{i=1}^{N}\frac{|a_{i}-p_{i}|}{a_{i}}\times 100 \tag{6}\]  
where \(N\) is a total number of configurations in the testing dataset, and \(a_{i}\) and \(p_{i}\) represent the actual performance value and predicted performance value, respectively. We choose this metric as it is widely used to measure the accuracy of prediction models [5; 10; 33; 34]. **Performance results**. We run PCK method and six baseline algorithms for five subject database systems independently. The size of training dataset in target domain is set to \(c\times n\), where \(c\) is the number of selected configuration parameters for each subject database system (which is shown in the column # of selected parameters of Table 1), and \(n\) ranges from 1 to 15. To evaluate the consistency and stability of the approaches, for each sample size of each subject database system, we repeat the random sampling, training and testing process 5 times. We then show and compare the mean of the MREs obtained with the 7 different approaches for each sample size. The experiment results of five subject database systems for larger version changes are listed in Tables 2-6, respectively. As expected, our proposed method has achieved better performance than all other six algorithms. Specifically, for five subject database systems, PCK outperforms all other six baseline algorithms: 24.99-53.18% improvement over DeepPerf, 14.20-51.19% improvement over CART, 4.42-46.58% improvement over Finetune, 25.26-53.13% improvement over DDC, 9.34-50.03% improvement over Model-shift, and 46.80-69.42% over Ottertune. The improvement percentages are shown in Figure 4. For the sake of simplicity, the data in Figure 4 is obtained by averaging the MRE data of different sample sizes in each subject database system.
## 3 A Simulation and Verification Platform for Avionics Systems Based on FACE Architecture
### Configuration Management  
Configuration management can configure table, variable, mapping, and host information for the memory database. The configuration management tool can intuitively, easily, and quickly add or change the table, variable, and mapping in the memory database, i.e., the operation information, and generates the corresponding XML configuration files. It avoids the trouble of manually changing configuration files, reduces the errors caused by manual changes, and improves the stability of the system.  
1. Table configuration  
The Table table stores data in memory, each table has multiple fields, each field has its type, and a single field can be accessed when accessing. We use a four-level index structure to avoid the problem that the same table name cannot be identified between systems. Each table added is equivalent to adding a section of code in the configuration file.  
2. Variable configuration  
In the configuration tool, the variable configuration is added after the table is established. After the variable information is added, click save, and the configuration tool will write the added information into an XML file named Table. In the XML file, the first pertains to the information of the table, such as the name of the table, Then it is item information, including item name, mode, data type, data length, array number, FIFO default of 1, and default value.  
\(<\)System name=\" \">  
\(<\)Table name=\" \">  
\(<\)Item name=\" \" mode=\" \" type=\" \" array=\" \" fifo=\" \" default=\" \" size=\" \"/\(>\)  
\(<\)/Table\(>\)  
\(<\)/System\(>\)  
3. Mapping configuration  
The Map table is not used to store data, but to access table data. There is a certain mapping relationship between the items in the table and the fields in the mapping. In this way, the item values in the table can be obtained through the mapping relationship. The mapping configuration is designed for ICD data. When there are variables in the variable table, the existing variables can be added to the mapping, then set the field information, and finally click save. The corresponding relationship between the ICD data field and the variables in the memory database is recorded in an XML file named Map. In the XML file, the initial information is Map, including the name, type, and description of Map, and then the field information, which is the field name, data type, starting byte, ending byte, starting bit, ending bit, and array number, and the variable is named after the name of the associated table.  
\(<\)Map name=\" \">  
\(<\)Field name=\" \" type=\" \" frombyte=\" \" tobyte=\" \" frombit=\" \" tobit=\" \" occurence=\" \" variable=\" \" /\(>\)  
\(<\)/Map\(>\)  
Figure 8: Design of the kernel data structure.  
4. Host configuration  
The host configuration is designed to configure the scheduling framework of the platform, the main idea is to referred to in Section 3.3. When configuring the host, the host information is added first, followed by the component information, after the addition is finished, it will be saved in an XML file named Host.
# Perceptual Similarities between Artificial Reverberation Algorithms and Real Reverberation
## 5 Discussion  
Our experimental results indicate that the Schroeder algorithm is the better choice if perceptual performance is not particularly important and computational efficiency is required. If computational efficiency is not particularly important and perceptual performance is a concern, the HMS reverberation algorithm might be preferable. Alternatively, a combination of the [PERSON] and Gardner reverberation algorithms or the HMS and Gardner reverberation algorithms, with Schroeder and HMS for short and medium reverberation times and the Gardner reverberation algorithm for long reverberations, might be beneficial.  
It is interesting to note that of the four audio samples, the scores for the male speech and the drumbeat are generally lower than those for female singing and cello piece. The scores for the drumbeat are particularly low. Therefore, the results suggest that the artificial reverberation algorithms tested in this experiment are less successful in simulating percusive sounds to make their perceptual effects similar to real reverberation. [PERSON] et al. [63] showed that although stimulus type has no effect on the ability to discriminate reverberation, this does not mean that the absolute amount of perceived reverberation is the same for different types of stimuli. Their study found that speech stimuli led to higher estimates of reverberation than singing or drums. This was particularly true for relatively long reverberation times (i.e., \(>\)1.8 s). Moreover, the absolute amount of perceived reverberation for drums was significantly different from that for vocal stimuli (include speech and singing). Thus, sound stimulus can have an effect.  
This is due to the fact that remote experiments conducted under COVID-19 restrictions could affect the listener's perception of the stimuli; thus, it is important to confirm the findings with in-person testing in further studies. It should be noted that this experiment is a static binaural reverberation parameter test rather than a dynamic one. However, the static case provides a valuable foundation before attempting fully head-tracked AR scenario conditions.