esa-sceva/satcom-chunk-collection huggingface dataset:
# RCM: A Remote Cache Management Framework for Spark
## 5 Experimental Results and Analysis
### Performance Result  
#### 5.2.1 CREP Performance Improvement  
In order to test the effectiveness of CREP, this section sets up three groups of benchmark jobs to observe the acceleration effect. The execution time of benchmark jobs, PageRank, K-means and WordCount, are shown in Figure 6a-c, respectively. Advanced cache replacement algorithms are set, including GD-Wheel [15], WCSRP [20], LWR [21], WACR [22] and ERAC [23].  
In Figure 6a, the X-axis represents the type of datasets and the Y-axis represents the execution time. For Web-BerkStan and Web-Google, the execution time of GD-Wheel has the longest and CREP has the shortest. As the size of the dataset increases, it is obvious that the execution time of CREP is shorter than that of the other algorithms. For Cit-Patents, the execution time of WACR has the longest and CREP has the shortest. CREP reduces the job execution time compared with GD-Wheel, WCSRP, LWR, WACR and ERAC by an average of 28.9%, 24.2%, 25.8%, 26.1%, and 22.5%, respectively. That is because the CREP considers not only the response time of the query database, the number of queries, and the data size but also the time loss of weight during job running, which accelerates the job. In Figure 6b, the best performance of CREP is further verified. In Figure 6c, WordCount, a computation-intensive job hardly has a better optimization than PageRank and K-means. Combined with source code to analyze this phenomenon, we can conclude that WordCount  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Configuration** \\ \hline CPU & Intel (R) Xeon (R) Platinum 8160 CPU @ 2.10 GHz \\ RAM & 64G \\ Hard Disk & 1 TB \\ OS & CentOS 7.0 \\ Spark & Cloudera Spark 2.4.0-cdh6.2.1 \\ Cloudera Manager & CDH 6.2.1 \\ Yarn & Yarn-3.0.0 \\ Redis & Redis 5.0 \\ JDK & JDK 8.0 \\ HDFS & HDFS 2.0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Resource configuration information.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Job Type** & **Iteration Rounds** \\ \hline PageRank & 3000 \\ K-means & 5000 \\ WordCount & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Benchmark workloads.  
has less interaction with cache resources than PageRank and K-means. So, it is insensitive to cache pressure. From what has been discussed above, it is revealed that CREP has a perfect optimization for the jobs which are not computation-intensive.
# An Interactive Method for Detection of Process Activity Executions from IoT Data +
## 2 Background and Motivation
### BPM Meets CPS and IoT
#### 2.1.2 IoT-Awareness of Process Data  
Given an event log of the process (activity) executions from a BPM system [3], we define _IoT-awareness_ of process data as the amount of information related to the sensor events of the CPS that is contained in the process event log (cf. Figure 1), also known as _IoT-enriched event log_[14]).  
Figure 1: UML class diagram of BPM–CPS concepts serving as basis for our work (adapted from [5]).  
_Full IoT-awareness_ is given if each process or activity execution-related event in the event log is linked to a set of _relevant_ actuator and sensor events influencing or influenced by the execution of the corresponding activity. _No IoT-awareness_ is given if there is no relation to IoT data contained in the event log.  
Note that sensor events may also be associated with more than one event in the event log or with no event (e.g., context data on the level of an entire trace [12; 14]).  
Once we were successful in reaching full process-awareness of IoT data as discussed in the previous section, we can automatically generate an _IoT-enriched_ event log based on the _SensorStream_ format [14] that has full IoT-awareness and that can then be exploited for improved process monitoring and mining.
## 3 Results and Discussions
### Structure
Therefore, it may be assumed that both Co and Al ions are incorporated in the ZnO lattice without changing the wurtzite structure. Closer examination of XRD patterns reveals a shift in the peaks' position toward higher diffraction angles in doped films compared to pure ZnO. The most pronounced changes are for CAZO films, where the (002) and (101) peaks shift by 0.08\({}^{\circ}\) and 0.06\({}^{\circ}\), respectively. Decreases in cell volume and lattice spacing  
Figure 3: XRD patterns of pure ZnO films and films doped with 5% Al (AZO), 5% Co (CZO) and co-doped with 5% of Al and Co (CAZO). due to replacement of Zn ions in the lattice by the smaller Al and Co ions are possible reasons for the observed shift [10; 17; 27; 35]. In order to confirm the assumption of lattice contraction, we conducted TEM studies along with selected area electron diffraction (SAED) (Figure 4). From TEM pictures, it can be seen that all the films have a similar morphology. They consist of nanocrystallites with irregular shapes in the size range of 20-30 nm, which is in accordance with the crystallite size calculated from XRD spectra (Table 1). The SAED patterns of all studied films (for clarity, only the SAED pattern for undoped film is shown in Figure 4) show clearly distinguishable rings corresponding to different planes of hexagonal wurtzite ZnO structures. All SAED patterns were indexed, and \(d\)-spacing (also called interplanar distance) was calculated and compared to that for pure ZnO. For all samples, the calculated \(d\)-spacing was lower than for undoped films. The deviation varied from 1% to 2% depending on the type of the dopant and crystal orientation. The highest deviations were obtained for AZO (2.03% in the 102 plane), CZO (1.76% in the 102 plane) and CAZO (1.08% in the 002 plane). We should note here that electron diffraction is taken from a very small selected area of the sample, thus giving local information for the sample status. The local SAED results may deviate from global XRD results to some extent. Generally, both techniques clearly confirm that 5% doping leads to lattice contraction. This is a consequence of the replacement of Zn ions in the lattice with the smaller Al and Co ions. Our additional TEM and SAED studies have shown that when concentration of dopants increases to 10%, local areas can be found where lattice spacing increases compared to pure ZnO films. This means that at higher concentration, dopant ions have occupied interstitial sites between zinc and oxygen atoms. Similar results are obtained for AZO films deposited by spray pyrolysis [36]. Two incorporation processes of aluminum in the ZnO lattice were detected: (i) a substitution of Zn\({}^{2+}\) by Al\({}^{3+}\) for concentrations less than 2%, and (ii) occupation of interstitial sites beyond 2% [36].
# Material Composition Testing Related to Measurement Instrument Enclosure Design and Safety
## 3 Results
### Classification of Test Samples by Material Mixture
The results presented here underscore the complexity of predicting flammability performance based solely on base material properties, confirming earlier studies that suggested that additive interactions could considerably influence combustion behaviour. These observations highlight the ongoing challenge in predicting material performance from limited test parameters and support arguments for broader experimental evaluations. Future research directions should focus on predictive modelling to further reduce the need for repetitive testing. Developing accurate computational models or machine learning techniques to predict flammability categories based on specific material compositions and thickness variations could greatly enhance the efficiency of material selection and certification processes. Additionally, larger-scale tests and comparisons with real-world fire scenarios could validate the practical relevance of these standardized small-scale tests. The findings from this study offer valuable insights for manufacturers in material selection and product development phases. By identifying material and additive combinations that consistently meet defined flammability standards, manufacturers can optimize their choices strategically to meet customer demands effectively while minimizing additional fire hazard testing requirements. Several material groups demonstrated consistent flammability classification regardless of minor additive changes or the sample thickness. Specifically, Groups 3, 4, 7, 8, 9, 10, and 17 consistently met the HB classification across all tested compositions, while Groups 2 and 16 reliably achieved HB75 and Groups 5, 14 and 15 reliably achieved HB40. In such cases, it is reasonable to recommend a \"worst-case testing\" approach, where only the thinnest samples with the least favorable additive composition are tested. If these worst-case variants meet the desired classification, the same classification could be assumed for related compositions within the group, thereby reducing certification costs and avoiding redundant testing. On the other hand, Groups 1 and 11 exhibited variability between HB, HB40, and HB75 classifications depending on the additive type or thickness, indicating that full testing across variants remains necessary. Ultimately, this approach enhances both flexibility and cost efficiency in material utilization without compromising product safety, thereby strengthening manufacturers' competitiveness and compliance on global markets but not risking the safety of final product.
## 2 Materials and Methods
### Design
#### 2.1.3 Data Transmission and Processing  
The Cryologger ITB nominally records its position and sensor measurements on an hourly basis and stores them in memory until a transmission interval of 3 h is reached. Data are transmitted via the Iridium satellite network as a Short Burst Data (SBD) message, which has a maximum size of 340 bytes. Each data sample is stored as a 30-byte binary message (Table 2). This minimizes the total number of required transmissions and reduces the overall power consumption of the system by allowing more samples to be sent in a single message. Each message is attempted to be transmitted for up to 180 s, and if unsuccessful, the message is stored in a temporary buffer and reattempted at the next transmission interval. Both the sampling and transmission frequency of individual ITBs can be remotely modified by the end-user by transmitting an SBD message to its International Mobile Equipment Identity (IMEI), the unique identifier associated with each Iridium transceiver. Successfully transmitted SBD messages are received by an Iridium ground station and sent to Ground Control's server. These data are then forwarded to Amazon Web Services (AWS), where they are decoded using a Python script, stored in a database, and visualized on a website. Raw data collected from Cryologger ITBs can be viewed in near real time and is freely available for download at [[https://cryologger.org](https://cryologger.org)]([https://cryologger.org](https://cryologger.org)). It should be noted that there is a subscription cost to send data through the Iridium network that varies by provider. For hourly data (Table 2), the cost is on the order of 35 USD a month, not including activation fees.
## 3 Results and Discussion
### Influence of the Operating Strategy
To this purpose, the investment period was fixed to 20 years and the main assumptions for the economic investigation are summarised in Table 3. The cost of the integrated CHP system (ORC and HEX sub-systems) was evaluated equal to EUR 636,786 adopting the bare model cost [113]. Specifically, the initial investment for the selected supercritical ORC unit was evaluated equal to EUR 551,838, with a specific cost lower than 5500 \(\mathrm{\SIUnitSymbolCelsius}\)/\(\mathrm{kW_{el}}\), while the cost of the HEX sub-system corresponds to about EUR 85,000. The absolute and percentage costs of the different components are visible in Figure 9. The turbine revealed the highest rate (36.5% of the integrated system), whereas the share costs of the four heat exchangers were similar and ranged between 13.3% of the HEX unit and 18.0% of the condenser. The pumps costs correspond to about 4%. Figure 8: Comparison of energy balance on yearly basis for mixed, electric-driven and thermal-driven strategy. Figure 9: Components cost of the integrated combined energy system. The analysis reveals that the operating strategy has a noticeable impact on the economic performance of the integrated system when the same architecture is selected. To this purpose, the net present values (NPV) for the three operating modes are compared in Figure 10.  
The results highlight that the mixed strategy also guarantees better economic performance: the payback period is lower than five years (4.9 years) and the NPV at the end of the investment period is equal to EUR 1,617,456. A slight increase in the payback time (PBT) is registered when the electric-driven mode is adopted (6.1 years). On the other hand, the thermal-driven strategy is characterised by a significant PBT rise to 11.0 years, and the NPV reduces to less than EUR 430,000, owing to the noticeable electric integration from the grid (52.7%).
## 5 Experimental Evaluation
Our LBSN2 vec and PLBSN2 vec are able to significantly outperform the other network embedding methods. We think the superiority of our methods comes from the derived embedding space that better utilizes the information between user-user interactions and user-POI visiting records in location-based social networks, which is crucial in modeling the potential of future POI visiting. **Limitations. ** Here we would like to discuss the limitations of the proposed methods for effectively solving TPD and TCD tasks. First, the proposed embedding learning methods, LBSN2 vec and PLBSN2 vec, are based on historical check-in records, i.e., existing interactions between users and POIs. If users tend to visit some particular POIs, it would be possible that the recommended propagators and potential customers, with respect to one of these POIs, can to some extent be similar. In other words, the \"filter bubble\" effect could happen. We would leave the solution to filter bubble as future work because there is a critical challenge in striking a balance between having accurate predictions and fair recommendations. Second, the embedding learning-based approach via a neural network requires a rich set of user-POI interactions to generate effective representations. While it is common to have some popular POI types and popular POIs, it would be natural to have better performance for popular venues and types. Third, different POIs have a variety of behaviors of users. Some are frequently visited by similar groups of users, and some are visited by various types of users. No matter which types of POIs are given, our proposed method can recommend proper propagators and customers. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **P@10** & **R@10** & **NDCG@10** \\ \hline Node2 vec & 0.320 & 0.185 & 0.741 \\ LINE & 0.310 & 0.195 & 0.610 \\ GraphGAN & 0.240 & 0.169 & 0.675 \\ LBSN2 vec & 0.350 & 0.197 & **0.760** \\ PLBSN2 vec & **0.380** & **0.216** & 0.753 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results in P@10, R@10, and NDCG@10 for the TPD task. Figure 2: Results of Precision@N for the Targeted Propagator Discovery (TPD) task. Due to the requirement of rich data for better prediction performance, we think our method may be less suitable for venues visited by users with diverse behaviors.
## 2 Experiment and Results
The amplifier array was comprised of 4 tapered SOAs. A compact heat sink, with temperature control was designed to load the SOAs. Each diffractive order of the Dammann grating was finely adjusted by a pair of mirrors for coupling to the corresponding SOA in the amplifier array, as shown in Figure 2.0 Finally, a total CW output power of 2.4 W was achieved. A spatial beam combination was built to compress the total size of the 4 sub-beams from the amplifier array. Four mirrors were used to rearrange the sub-beams into a 2 \(\times\) 2 pattern, as shown in Figure 2.1 The propagation direction of all the sub-beams is highly uniform by finely adjusting the mirrors. The total combined beam size is 4.5 mm in diameter (defined by 90% of the total energy) and a 4.2-mm-in-diameter (defined by 90% of the total energy) focused beam spot is obtained by a 1-m-focal-length lens. The total beam quality can be calculated to be 4.5 mm \(\times\) 4.2 mm/4 = 4.7 mm.mrad (half beam width and half angle). Figure 6a shows the P-I curve of the MOPA system. Maximal optical power of 2.2 W was obtained. Figure 6b shows the WPE of the MOPA system. The highest WPE achieved was 20%. Mode-hop free tuning range of 49 pm at 2.2 W output power level was achieved by tuning the master oscillator, meanwhile only 0.03 W power change was observed, as shown in Figure 2.2 Figure 8 shows the screenshot of the oscilloscope in spectral linewidth measurement by a Fabry-Perot interferometer (THORLABS SA210-5B and SA201 controller) with resolution of 67 MHz and free spectral range (FSR) of 10 GHz. The linewidth can be calculated as 0.446 ms / 22.30 ms \(\times\) 10 GHz = 200 MHz (0.3 pm). In addition, the stability of the MOPA system was investigated during 2.5 hours of operation. We observed only 2 pm of wavelength change and 0.1 W of power change within this time. Figure 2.3 Optical power (**a**) and wall plug efficiency (**b**) of the MOPA system versus driving current. Figure 2.4 Power fluctuation of the MOPA system during wavelength scanning.
# A Fire Detection Algorithm Based on Tchebichef Moment Invariants and PSO-SVM
## 1 Introduction
Moreover, it continues to exhibit the best performance even if the size of the training sample set is small and the images are transformed by TRS, proving its effectiveness and feasibility. The remainder of this paper is structured as follows: Section 2 provides a brief introduction to Tchebichef moments. In Section 3, the translation, rotation, and scale invariants of Tchebichef moments are obtained, which contributes to form the feature vectors. Section 4 describes how to construct an SVM and introduces the basic principle of particle swarm optimization and the optimization procedure for SVM parameters. The process of fire image detection based on an PSO-SVM model is also presented. Section 5 describes the experiments conducted to demonstrate the performance of the proposed algorithm. Results of the experiments are also discussed in detail. Finally, the conclusions are stated in Section 6.
# Network Structure and Synergy Characteristics in the Guangdong-Hong Kong-Macao Greater Bay Area
## 4 Results Analysis
### The Macro State of City Clusters in the Greater Bay Area
#### 4.3.2 Regional Capital Flow Advantage Linkages
Hong Kong \(\rightarrow\) Shenzhen and Shenzhen \(\rightarrow\) Guangzhou are the most dominant logistics linkage directions, forming the core axes of the logistics network in the Greater Bay Area. Similarly, Shenzhen \(\rightarrow\) Guangzhou and Hong Kong \(\rightarrow\) Guangzhou represent the strongest capital flow directions, establishing the core driving pattern of the capital flow network. Both network structures are characterized by a \"core-core\" type of flow, with the linkage strength between core cities exceeding one-third. Additionally, the weaker linkages of sub-nodes in the region indicate that cities such as Zhaoqing and Jiangmen exhibit lower linkage direction and strength in both logistics and capital flow networks. This suggests that, at the current stage, the regional development of the Greater Bay Area is still predominantly influenced by the core cities.
# A Low-Overhead Countermeasure against Differential Power Analysis for AES Block Cipher
## 4 Implementation Results
### Experimental Setup  
We have implemented two designs (DESIGN-I (without countermeasure) and DESIGN-II (inclusion of countermeasure)) in Verilog HDL on Digilent ZYBO development board. It contains a Zynq 7020 FPGA device for logic implementation. To apply the SCA attack on the selected board, certain alterations are required, i.e., (i) removal of the decoupling capacitors (C179, C180, C224 and C225), and (ii) replacement of a shunt resistance, i.e., R265, with a value 0.1 \(\Omega\)[41]. This will sink the total current of the board through this resistance (0.1 \(\Omega\)). The purpose of using a 0.1 \(\Omega\) resistor is to reduce the static power, as Zynq SoC (with both FPGA logic and dual-core processor) has much larger static power consumption.  
_Implementation parameters._ We have implemented a 128-bit variant of AES block cipher. It means our designs contain a 16-byte key, having values (0 xA1, 0x78, 0x5B, 0x77, 0x2D, 0 xCD, 0 xD4, 0x1F, 0x9E, 0x55, 0 xA3, 0x45, 0x7C, 0x8B, 0x26, 0 xEC). All these values are in hexadecimal representation.  
_Capturing power traces._ As shown in Figure 3, the probes of the oscilloscope are connected across the shunt resistance that we replace to 0.1 \(\Omega\). We captured the power traces as the AES performed encryption over the data (so the attack will have the highest probability of success). An external trigger signal is generated from FPGA to inform the oscilloscope when it will start to capture the data and vice versa. To set and reset the external trigger, we used pin 8 of port 2. The trigger is activated during the computation of the **AddRoundKey** and **SubBytes** functions. These two functions are executed 10 times for each plaintext value and the average value of 1000 power traces is captured by the oscilloscope. Note that the AddRoundKey function is simply the exclusive-OR of each byte of data with the key. So, it does not consume too much power. The SubBytes results in a leakage of information, as the cryptographic device uses too much power (because Sbox is looking for the bytes obtained after the computation of an exclusive-OR operation). This enables us to attack and perform such power analysis. After capturing the power traces, we developed a MATLAB script to perform the DPA attack, using Pearson correlation. Recall again that we also introduced a custom correlation algorithm to speed up the attack process (see Section 3.1.2).
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 6 Conclusions  
In this paper, we proposed an on-board real-time ship detection scheme based on the CFAR algorithm and lightweight deep learning for HISEA-1 SAR imagery, and constructed a ground system to verify the feasibility of our scheme. The scheme uses CFAR to perform initial ship detection followed by the YOLOv4-tiny method to obtain the final results. Subsequently, the entire algorithm was ported into the Jetson TX1 GPU and, through a reasonable hardware acceleration strategy, our scheme achieved high accuracy and efficiency on the ground verification platform. The experiments showed that this scheme demonstrated good applicability for real-time on-board ship detection on lightweight SAR satellites. In our future work, we will continue to improve the CFAR algorithm while also improving the YOLOv4-tiny network to further maximize the computational efficiency. When more data from the HISEA-1 are obtained, we will focus on labelling the ship chips to improve the accuracy of target detection.  
[PERSON], [PERSON], [PERSON] and [PERSON] conceived of this study. [PERSON] performed the experiments and wrote the manuscript. [PERSON], [PERSON] and [PERSON] supervised the work and revised the manuscript. [PERSON], [PERSON], [PERSON] and [PERSON] helped to acquire the data of experiments, and contributed with field experience in HISEA-1 satellite and on-board processing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Academy for Multidisciplinary Studies, Capital Normal University and the National Natural Science Foundation of China under Grant 42071444.  
Not applicable.  
The authors declare no conflict of interest.