esa-sceva/satcom-chunk-collection huggingface dataset:
# Revisiting the Design of Parallel Stream Joins on Trusted Execution Environments
## 3 Challenges of Parallelizing Stream Joins on TEEs
### Motivating Experiments  
In this section, we present our motivating experiments.  
**Cache Effects on TEEs.** Our micro-benchmarks show that exceeding the available EPC size is not the sole performance bottleneck of Intel SGX. Rather, exceeding the L3 cache also causes non-negligible performance detrimments as it requires cache evictions which trigger security checks and cryptographic operations. We utilize the parallel memory bandwidth benchmark tool (pmbw) [45] to monitor the performance of 64-bit read and write operations using variable-length arrays on our Azure server (Described in Section 5.3). We then port it on Graphene-SGX [46] to compare the throughput from within an enclave. We notice that the SGX performance of the instructions almost perfectly matches the native runs, until we reach the last level cache (LLC); after which, a clear performance gap is noticed when the output of 20 runs is averaged as illustrated in Figure 2.  
**Memory Allocation Overhead on TEEs.** Next, we emphasize the importance of meticulous job scheduling on SGX by evaluating the overhead of allocating enclave memory during the initialization phase. We notice a significant \"jump\" in latency when the EPC size is reached in terms of encrypted memory demand, after which the slope depicting the change in latency rate increases from 1.5 ms/Mib to 4.7 ms/Mib as shown by the average of 30 runs illustrated in Figure 3. Note that the numbers depicted are for the sole memory allocation. Active usage and swapping of the memory pages in a complex application would entail an exponential increase of such overhead as shown in our profiling study in Section 5.  
**4. Design Aspects of Parallelizing Stream Joins on Multicore Processors with TEEs**  
In the following, we introduce four design aspects of parallelizing stream joins on TEEs: (i) execution approaches in Section 4.1, (ii) join methods in Section 4.2, (iii) partitioning schemes in Section 4.3, and (iv) distributed scheduling strategies in Section 4.4. We summarize them in Table 2.  
Figure 3: Memory allocation overhead on SGX with increased Encrypted Memory Demand.  
Figure 2: Caching Effects when executing read and write instructions, natively and on SGX for different array sizes.
## 5 Discussion  
The approach applied for this study integrates in situ observations with remote sensing data, as in situ measurements provide air temperature at high temporal but low spatial  
Figure 14: Data images related to the 8-day data 20/07/2022–28/07/2022. (**a**) From the combination of the four 8-day MODIS LST images (Aqua day, Aqua night, Terra day and Terra night) with their respective coefficients—deduced from the linear regression—we obtained (**b**) the estimated air temperature.  
Figure 13: Comparison between MODIS-derived (dotted line) and ARPA-measured (continuous line) Tair at the coordinates of Fortunago Monitoring Station (ID 8007), while the gray line depicts their difference (\(\Delta\)T).  
resolution, while satellite-derived data offer surface temperature estimated with higher spatial but lower temporal resolution. In this context, linear regression allows calibration of Land Surface Temperatures from MODIS satellites against air temperature data collected by ARPA, so that the predicted temperature reflects real conditions as closely as possible. Despite the simplicity of the statistical model used as an initial hypothesis for this calibration, we chose to apply linear regression because, as a preliminary method, it aligns with established approaches in the literature for temperature data calibration [29].  
We could achieve high accuracy, as the dataset used to calibrate the data has an \(r^{2}\) higher than 0.981 and RMSE of 1.11 \({}^{\circ}\)C, while the comparison of estimated Tair and the Tair measured by the monitoring station excluded from the calibration (ID 8007) shows a \(r^{2}\) of 0.979 and a RMSE of 1.03 \({}^{\circ}\)C  
The monitoring station with the lowest performance, located in Canevino--ID 9019--see Figure 17 in Appendix A, has an \(r^{2}\) of 0.976 and a RMSE of 1.21 \({}^{\circ}\)C probably due to particular features in the station's surroundings, as it is quite isolated from the urban fabric. The average annual uncertainty, considering all monitoring stations in the study area, has a range between 0.78 and 0.97 \({}^{\circ}\)C.  
The result shows the strong physical relation between surface and air temperature, enhancing the accuracy of the MODIS LST products [30]. The higher influence of LST Night temperature on the \(\hat{T}\)air can be explained by the lack of solar radiation and its effects on the thermal infrared signal [31].  
From the analysis of \(\Delta\)T (\(\hat{T}\)air-Tair), see Figure 13, we could detect a pattern that shows an overall overestimation of the temperature in summer and an underestimation in winter. This occurs for all monitoring stations, with no significant relation detected between elevation and errors [30]. Further work is needed to thoroughly evaluate the patterns detected, applying more complex regression methods such as global non-linear models [32] and Geographically Weighted Regression (GWR) [33].
## 4 Results
### Assessment of the Measurement Model  
For a reflectively measured model as in our case, it is required to assess the internal consistency reliability (ICR), convergent validity and discriminant validity [51]. The values for [PERSON]'s \(\alpha\) and composite reliability should be between 0.7 and 0.95 for research that builds upon accepted models. [PERSON]'s \(\alpha\) is seen as a lower bound and results of the composite reliability are seen as an upper bound in the assessment. The values for [PERSON]'s \(\alpha\) and the composite reliability are all within the suggested range of 0.7 up to 0.95 (cf. Appendix B, Table A2).  
Convergent validity (based on the assessment of outer loadings and the AVE) is given since all loadings are higher than 0.7, except for RP2 with a value of 0.692. However, the AVE of this construct is above the suggested threshold of 0.5. Thus, we kept this item. The AVE values of the other constructs are also well above 0.5, demonstrating convergent validity. Discriminant validity is also given since all outer loadings of our analyzed constructs are larger than their cross-loadings with other constructs and the square roots of the AVEs of the single construct are larger than the correlation with the respective other constructs (Fornell-Larcker criterion) (Appendix B, Table A3).  
Our data was gathered with a self-reported survey at one point in time in one questionnaire. Thus, a test for the common method bias (CMB) is required. We performed an unrotated principal component factor analysis to conduct the [PERSON]'s single-factor test [53]. It indicates that eight factors have eigenvalues larger than 1 which account for 70.14% of the total variance. The first factor explains 25.12% of the total variance. These values indicate that the CMB is not an issue in our data.
## 4 Experimental Results and Discussion  
### Performance Evaluation Metrics  
#### 4.1.1 ROC (Receiver Operating Characteristic)  
The receiver operating characteristic (ROC) curve is frequently used for evaluating the performance of binary classification algorithms. It provides a graphical representation of a classifier's performance rather than a single value like most other metrics. The closer the ROC is to 1, the more effective that detection model is. This algorithm's ROC is equal to or lower than 0.5, which means that the inspection model has no value for use.
# Real-Time Adjustment Method for Metro Systems with Train Delays Based on Improved Q-Learning
## 3 Model Construction
### Model Notations and Assumptions
Interaction zones define the distance between the train and the entrance of the block when the next train approaches, guaranteeing smooth and safe train operations. Generally, the interaction zones are set to one block (red rectangle) for stopping and two blocks (yellow rectangle) for deceleration [29]. Specifically, the positional relationship between the following train service \(k\) and preceding train service \(k-1\) significantly impacts the train's speed. In Case 1 (depicted in Figure 4), if the preceding train \(k-1\) is located outside the interaction zone block, the subsequent train \(k\) will maintain its maximum design speed, ensuring a normal operation. However, in Case 2, when the train \(k-1\) is within the interaction zone blocks, the subsequent train \(k\) will be advised to maintain a lower speed to ensure a safer distance. Figure 4: Schematic diagram of train interaction zone under emergency situations. Figure 3: Diagram of bi-directional metro line. This operation adjustment ensures the overall stability and safe operation of the entire metro line. Based on the actual operation and operational requirement of metro systems, the following assumptions are made:  
1. Due to the limited tracks at stations in metro lines, overtaking strategies for trains are not allowed. This means that there are no overtaking situations in the model. 2. Trains follow a strategy of stopping at each station, and there are no instances where a train bypasses a station without stopping. 3. The running time in each block is predetermined based on the ATO (automatic train operation) system, which means block running time levels are determined according to the ATO system. 4. To ensure the efficient operation of the passenger service, it is preferred that trains are not allowed to stop within the blocks. Even if a train does stop, the doors are not allowed to open within the blocks. Once the train receives block clearance ahead, it immediately departs without any stopping time. 5. In the case of a long delay, the metro staff may decide to let trains return to the depot. The situation after this operation can be regarded as a scenario of short-term delay.
# A Scheduling Algorithm for Multi-Workshop Production Based on BOM and Process Route
## 5 Implementation of Scheduling Algorithm for Multi-Workshop Production
### Dynamic Inertia Weight  
The inertia weight \(\omega\) reflects the impact of the particle's previous velocity on this generation. In the standard particle swarm algorithm, \(\omega\) is linearly decreasing. Usually, the value of \(\omega\) is larger in the early stage of algorithm optimization, which is beneficialto the global optimization. At the end of the iteration, the value of \(\omega\) is smaller, which is beneficial to local optimization. In this article, the following improvements are made to \(\omega\).  
\[\omega(t)=r_{\omega}(\omega_{\max}-\omega_{\min})\exp(-\frac{t}{T}(\omega_{\max} -\omega_{\min})^{2}) \tag{22}\]  
In Equation (22), \(r_{\omega}\) is a weight coefficient with a value less than 1. Different from the linear decreasing model, this equation represents a dynamic inertia weight. The decreasing speed is more suitable for the particle search process and better balances the global search ability and local search ability of the algorithm.
# Mechanistic Insights into Fish Spoilage and Integrated Preservation Technologies
## 2 Mechanisms of Spoilage
### Microbial Spoilage
#### 2.3.2 Microbial Metabolism and Metabolite Production  
After colonization, specific spoilage organisms (SSOs) proliferate by exploiting muscle nutrients via distinct biochemical pathways. Proteolytic and deamination reactions--catalyzed by bacterial proteases and deaminases--break down proteins into peptides and free amino acids, releasing ammonia and biogenic amines, such as putrescine, cadaverine, and histamine [45; 46; 47]. Concurrently, marine SSOs (e.g., Shewanella putrefaciens, Photobacterium phosphoreum) enzymatically reduce trimethylamine oxide (TMAO) to trimethylamine (TMA), generating strong ammonia-like off-odors [47]. Sulfur-reducing bacteria further convert sulfur-containing amino acids into hydrogen sulfide (H\({}_{2}\)S) and other volatile sulfides, producing characteristic rotten-egg aromas [48].
## 3 Radar Micro-Doppler Analysis of Vibrating Structures
### Radar Micro-Doppler from Vehicle Starts  
Radar micro-Doppler has the potential to detect a vehicle being started by capturing the distinct vibrations associated with the engine's startup process, including the activation of the starter motor, the engine coming to life, and the changes in mechanical components as the car stabilizes at idle. When a vehicle starts, the above changes in the vehicle's behavior produce detectable vibration signatures that can be captured by radar, even if the vehicle is stationary.  
Upon starting, an engine typically idles at an elevated RPM as the vehicle drivetrain and exhaust components warm up to steady state conditions. These startup transients cause a distinct transient vibration profile that radar micro-Doppler can pick up. The transition from engine-off to engine-on can be detected by changes in the intensity and frequency of micro-vibrations from the engine block, exhaust system, and engine mounts.  
If the vehicle is started while in gear (e.g., in Drive or Reverse), the radar might also detect the slight vibrations as the transmission system engages and begins to transfer power. However, this is more noticeable in vehicles with manual transmissions or in automatic transmissions when the vehicle is under load. The engagement of the torque converter or clutch (in manual transmissions) as the vehicle starts could also create detectable micro-Doppler signatures.  
Starting the vehicle can also lead to small movements or shifts in the vehicle, especially if the vehicle is in gear or on a slope. The suspension system can compress slightly when the vehicle starts, which may also be detected by radar. This is especially true in the case of vehicles with automatic transmissions that might produce minor vibrations when the vehicle is in Drive or Reverse.  
Even in the case of idling or non-moving vehicles, the act of starting the vehicle can cause a sudden change in the vibration profile. Before starting, the vehicle is in a stationary state with minimal engine-induced vibrations (engine off). After starting, the vehicle produces more mechanical vibrations from the engine, creating an easily detectable shift in the radar's micro-Doppler signature.  
The radar will detect the initial activation of the engine (starter motor) and the transition to normal engine operation. The Doppler effect from the engine's turning parts (e.g., pistons, crankshaft, fan belt) will produce distinct shifts in the radar's frequency.  
The effectiveness of radar micro-Doppler can depend on the vehicle type. For example, a hybrid vehicle or an electric vehicle (EV) may produce different vibration profiles compared to a traditional internal combustion engine (ICE) vehicle. The radar might struggle to detect engine start in EVs, which have no traditional engine start process, as they rely on electric motors that may not produce the same vibrations during startup. Gross vehicle motion from release of parking brakes is likely to be applicable to both ICE and electric vehicles.
# Optically Transparent Honeycomb Mesh Antenna Integrated into OLED Light Source
## 3 Antenna Configurations
### Non-Transparent Antennas  
A reference non-transparent patch antenna was first designed using an entirely copper film. The geometry of the non-transparent antenna is shown in Figure 6. This antenna consists of a radiating element on one side of the glass substrate (thickness of \(h_{s}=1.1\,\mathrm{mm}\)) and a ground plane on the other side.  
The width (\(W_{f}=1.77\,\mathrm{mm}\)) and the length (\(L_{f}=5.8\,\mathrm{mm}\)) of the antennas' feedline were calculated to match the characteristic impedance of 50 \(\Omega\). Both antennas occupy a volume of \(25\times 25\times 1.1\,\mathrm{mm}^{3}\). A copper film (conductivity of \(\sigma=5.8\times 10^{7}\) S/m, thickness of \(t=370\,\mathrm{nm}\) and sheet resistance of \(R_{s}=0.05\)\(\Omega/sq\)) was used to design the two antennas.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **ITO Film** & **Copper Film** & **Mesh Structure** \\ \hline Transparency (\%) & 65.1 & opaque & 75.4 \\ Sheet resistance & 4.51 & 0.047 & 0.2 \\ \((\Omega/sq)\) & 370 & 370 & 370 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons between the ITO film and mesh structure.  
Figure 5: Diagram of the discrimination of two points observed with the bare eye [38].  
The dimensions were calculated using the equations in [39], so that the antenna operates in the 5.15 GHz WLAN band.
## 2 Materials and Methods
### NMR Data Analysis  
Resulting NMR spectra were aligned to the TSP signal (0 ppm) and input variables for statistical analyses were generated via variable size binning (green coffee beans spectra divided into 384 buckets, and roasted coffee beans spectra divided into 419 buckets). Each spectrum was segmented into buckets of 0.02 ppm in the range between 0.4 and 10 ppm, except the resonance regions of caffeine (3.2, 3.4, 7.75 ppm) and chlorogenic acid (6.2, 7.0, 7.50 ppm) because of the significant chemical shift changes observable due to their interaction in aqueous solution [19]. Therefore, the buckets of these regions were merged to have the protons of the corresponding molecule into the same bucket window (merged in green buckets: 3.14-3.28, 3.34-3.44, 6.02-6.5, 6.58-7.2, 7.44-7.68, 7.7-7.9; merged roasted buckets: 3.22-3.28, 3.36-3.48, 6.22-6.44, 6.72-7.2, 7.44-7.66, 7.7-7.88; Supplementary Materials Figure S1). Moreover, the region of residual water (4.5 ppm-5.24 ppm) was excluded. Buckets were then normalized to the measured weight of crushed beans, and thereafter, Probabilistic Quotient Normalization (PQN) was applied. The resulting dataset was used to perform multivariate statistical analysis.  
A total of 20 metabolites were identified in all the NMR spectra of green coffee, and 29 were identified in all the roasted one. Among them, 15 metabolites are present both in the NMR spectra of green and roasted aqueous extracts. Comprehensively, 34 different metabolites were assigned (Figure 1). Since most of them resonate in crowded regions of the spectrum, where the presence of other signals below certain peaks cannot be excluded, only 15 metabolites in green and 25 metabolites in roasted coffee, corresponding to well defined and resolved peaks in the spectra, were quantitated considering the area under the peaks. Signal identification was performed using a library of NMR spectra of pure organic compounds (AssureNMR 2.2 software, Bruker BioSpin, Karlsruhe, Germany), public databases (e.g., FooDB, n.d.; PhytoHub, n.d., Edmonton, Alberta) storing reference and literature data [7; 10; 12]. The resulting matrices were used to perform multivariate and univariate data analyses.
# Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images
## 4 Analysis and Discussion  
To test VO and the localization and mapping algorithms outlined above, we acquired a video sequence of a 1115 m path through a sparse ponderosa pine (_Pinus ponderosa_ Douglas ex Lawson) forest in Western Montana. The video was captured by walking a hand-held 12 cm baseline ZED stereo camera [62] through the forest. The camera was operated at 10 frames per second and VGA resolution (480 \(\times\) 640). Mounted on top of the camera field monitor was an antenna connected to a GlobalTop FGPMMOP4 GH GPS module [63] that was queried for a GPS coordinate reading and a PDOP value after each video frame capture. The GPS coordinates and video frames were stored on an embedded backpack computer. Figure 4(a) shows the GPS coordinate readings projected on the UTM coordinate system after translating the position track by subtracting the first GPS coordinate from all coordinates.  
We estimated camera egomotion using the direct VO algorithm presented in Section 2. We used a camera resolution of 240 \(\times\) 320 to estimate egomotion and a 25% gradient magnitude threshold during pixel selection. Frame-to-frame egomotion parameters were composed to construct an odometry track using Equation (28). Figure 4(b) shows the visual odometry position track after converting the translation components of the egomotion parameters to meters. Figure 6 shows the GPS track (black line) and the optimized VO track  
Figure 5: GPS position track projected on a UTM coordinate system (**a**). VO position track (**b**). The blue square denotes the starting position and the red triangle denotes the end position.  
after global alignment and local refinement (red line). The blue dashed line in Figure 6 shows the VO track after only applying the optimized global orientation parameter. As the figure suggests, the VO position track, although locally consistent, is subject to drift after approximately 300 m.
# Site-Specific Earthquake Ground Motions for Seismic Design of Port Facilities in Indonesia
## 4 Conclusions  
The SNI provides SAs for seismic design, but these have not been obtained with site-specific measurements. The amplification factors of the SAs were referenced from ASCE 7-16 [10], which uses EGM records and ground data obtained from sites other than Indonesia. We proposed site-specific EGMs for the seismic design of port facilities in Indonesia by performing a seismic response analysis of 144 cases using EGM records and ground data obtained in Indonesia. The main conclusions drawn from this study are as follows:  
1. The site-specific SAs showed very large variation, especially in the period range of a quay. The SAs became very large when the predominant frequency of the EGM incident in the bedrock agreed with the natural frequency of the ground. The site-specific EGMs must be evaluated using a seismic response analysis that considers the ground conditions at the site of interest. Moreover, the nonlinear soil characteristics must be properly considered.
2. We studied the site-specific EGMs for seismic design for cases where the ground data at the target sites were not obtained in detail. The proposed SA targeted the envelope of the average SAs obtained in the analysis. The comparison of the proposed SAs with those in the SNI revealed that the former is larger than the latter in the period range of 0.2-2.0 s for site class SD and in the period range of 1.0 s or longer for site class SE. The SNI underestimates the amplification factor of the EGMs for site class SD. For site class SE, the SNI falls short of considering the nonlinear characteristics of the soft sediment layers. For both site classes, conducting a reasonable seismic design of port facilities was made possible by using the proposed SAs.  
Conceptualization, C.B., A.N.R. and T.N.; methodology, T.N.; software, C.B. and A.N.R.; validation, C.B., A.N.R. and T.N.; formal analysis, C.B., A.N.R. and T.N.; investigation, C.B. and A.N.R.; resources, C.B. and A.N.R.; data curation, C.B. and A.N.R.; writing--original draft preparation, C.B. and A.N.R.; writing--review and editing, T.N.; visualization, C.B., A.N.R. and T.N.; supervision, T.N.; project administration, C.B. and A.N.R.; funding acquisition, C.B. and A.N.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by PT. Inti Teknui Solusi Cemerlang (ITSC), Surabaya, Indonesia, grant number: 036/ADM.ITSC/VIII/2019, and the APC was funded by PT. ITSC.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to express their gratitude to the PT Inti Teknik Solusi Cemerlang for providing soil investigation reports, [PERSON] from Narotama University and [PERSON] from Andalas University for providing EGM records.  
**Conflicts of Interest:** The authors declare no conflict of interest.