esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Validation with Field Test
### Data Analysis and Discussion
#### 4.3.3 Discussion on Transfer Reconstruction with Time-Series Prediction Method  
In prior studies [34; 35; 36; 37; 38; 39], response reconstruction has been treated as a time-series forecasting issue, leading to the application of time-series prediction techniques. This section further discusses the feasibility of transfer reconstruction using time-series prediction methods from existing research and compares their accuracy with that of the proposed ResNet algorithm. A typical time-series prediction method, LSTM, was utilized to simulate the transfer matrix. The LSTM and the other time-series prediction methods [10; 11; 12; 13; 14; 15; 16] have been validated to be effective for training a network when both the input and output data are known information.  
A LSTM network was initially trained on high-frequency responses recorded at 04-S02 and 03-S02, with the target output at 03-S02. The architecture of the LSTM network closely mirrored that of the ResNet. Subsequently, this trained LSTM network was employed to reconstruct low-frequency strain, utilizing low-frequency responses at the same cross-sections. The comparison between the low-frequency strain reconstructed with the LSTM and the actual measurements is depicted in Figure 23.  
In Figure 23, discrepancies are evident between the peak amplitudes of the reconstructed and measured bridge strains. Compared with the precise alignment achieved with the ResNet, the LSTM exhibited less satisfactory performance in the transfer-reconstruction process. Notably, the LSTM-reconstructed peak strain was characterized by an unusual \"upper limit\" around 50 \(\mu\varepsilon\), as indicated by the solid lines in Figure 23. This limitation is presumed to be a carryover from the high-frequency training data for 03-S02. As shown in Figure 24, the maximum high-frequency strain at this location was approximately 50 \(\mu\varepsilon\). It is hypothesized that the LSTM, in its attempt to reconstruct low-frequency responses, was constrained by the memory of the exact amplitude values from the high-frequency responses. Once the predicted low-frequency strain surpassed the maximum amplitude present in the high-frequency dataset, the LSTM enforced the upper limit, resulting in a distorted reconstruction. Drawing from the author's experience, this discrepancy can typically be mitigated in LSTM applications by leveraging partial knowledge of the target responses and implementing normalization to align the upper limits across all datasets. i.e., overfitting of unreal time-related factors. However, in this study with the ResNet, no such prior knowledge of the low-frequency strain was assumed, and the overfitting was therefore avoided. Based on these observations, it is advisable to prefer time-irrelevant methods such as the ResNet over time-series prediction methods like LSTM for transfer-reconstruction  
Figure 23: Comparison between the low\(-\)frequency strain reconstructed with LSTM and the measured strain at 03\(-\)S02.  
tasks, particularly when dealing with scenarios where limited prior knowledge of the target variable is present.
# An Adaptive Data Rate Algorithm for Power-Constrained End Devices in Long Range Networks
## 4 LoRa Network Performance Evaluation
### ED Power Consumption Model
#### 4.2.3 Analysis of ED Power Consumption in Different Operating States
\[E_{avg}=\sum_{i=1}^{m}\frac{n_{i}}{n}\cdot E_{avg\_i} \tag{34}\]For a specific LoRa ED, the power consumption is mainly related to the working current and duration in each operating state. Using a LoRa ED equipped with an SX1262 chip and connected to an inclination sensor, the current and duration in each operating state are recorded, as shown in Table 5.  
The common transmission power range of LoRa EDs equipped with the SX1262 chip is from 2 dBm to 20 dBm. The working current in the LoRa transmission state varies with different transmission power levels. To determine the relationship between the two, the working current of the ED in the transmission state at different transmission power levels was measured, as shown in Figure 6:0  
Figure 7 shows the impact of different data rates and transmission power levels on the transmission power consumption of the ED. As shown in the figure, BW has a relatively large effect on the power consumption of the ED. When the SF, transmission power, and coding rate are fixed, EDs using larger BWs consume less energy to successfully send one data frame. Therefore, when the coding rate is fixed, optimizing the transmission power consumption of EDs requires paying attention to the allocation of transmission power and BW.
# A Scalable Framework for Sensor Data Ingestion and Real-Time Processing in Cloud Manufacturing
## 4 Materials and Methods
### Case Study  
The core layers of the proposed framework, namely the data ingestion, data processing and analytics, and application architecture were validated using real-world sensor data from the \"Istanbul Stock Exchange Real-Time Data for Temperature and Humidity Sensors\". This dataset was sourced from the \"Appliances EnergyPrediction Data Set\" on the UCI Machine Learning Repository [43]. It includes temperature and humidity readings from various rooms in a low-energy residential building, with a sample of 100 consecutive readings, as shown in Figure 3.  
Figure 3: Sample of 100 sequential temperature and humidity readings from real-time sensors.  
The data acquisition and preparation process encompassed the following steps (bypassing the framework data collection and communication layers, owing to direct data availability):  
1. Retrieved the \"Appliances Energy Prediction Data Set\" from the UCI Machine Learning Repository.
2. Downloaded the dataset in.csv format.
3. Extracted relevant columns, such as T1 (temperature in the living room) and RH_1 (relative humidity in the living room), along with data from other monitored spaces.
4. Stored the dataset locally in a directory titled Article_dataset for experimental use.  
The framework processed these data streams, where each sensor generated readings every second, including the timestamp, temperature (in degrees Celsius \({}^{\circ}\)C), and humidity (percentage %). This setup enables real-time ingestion, processing, and analysis across several experimental scenarios to compare different messaging queues, data processing frameworks, and application architectures.  
As shown in Figure 4, three experimental campaigns were conducted: the first (left) evaluation of Kafka and RabbitMQ for data ingestion, the second (middle) comparison of Spark Streaming with Apache Hadoop for data processing and analytics, and the third (right) assessment of microservice versus monolithic application architectures.
The study examines the impact of a 30 MW battery on frequency regulation, emphasizing the importance of battery energy storage equipment in frequency regulation. Reference [17] examines the technical application of flywheel energy storage systems in isolated power systems with high penetration of renewable energy. It discusses the advantages of using flywheel energy storage, different control schemes, and their impact on frequency regulation. In another study [18], an approach named DaSOF is introduced, aimed at enhancing frequency response and regulation services in grids with high renewable energy. This method provides a unified frequency control framework for distributed and energy-constrained energy storage systems, complementing the frequency control of existing generators. Reference [19] delves into the primary challenges of achieving 100% renewable energy penetration in low-inertia, isolated grids, including frequency control and stability. The study focuses on small island systems and proposes a solution using pumped hydro storage to enhance frequency regulation. This paper is based on the E-dReg0.5 specification, an extension of the frequency regulation reserve auxiliary service dReg0.5. Taking into account the charging state of energy storage systems [20; 21; 22], a sophisticated dynamic frequency reserve controller has been designed. Leveraging the rapid charging and discharging capabilities of energy storage systems [23; 24], the controller manages the charge and discharge operations through controlled actions on the energy storage system within the power grid. This controller not only aids in maintaining the frequency drift caused by load fluctuations in the power system but also incorporates energy transfer functionality in addition to frequency regulation reserve. This ensures that the controller can operate with high performance quality, contributing to the stable and balanced operation of the power system. By considering the charging state of the energy storage system, this controller achieves controlled rapid charge and discharge, further ensuring stable performance quality. The present dissertation comprises five chapters in total, structured as follows: Section 2 elucidates the pertinent regulations concerning the auxiliary service products planned by the Taiwan Power Company, with a specific focus on elucidating and introducing E-dReg. Section 3 introduces the hardware equipment and programming languages utilized in developing the enhanced dynamic frequency regulation controller, along with providing an exposition on the control strategies proposed for E-dReg in this paper. Section 4 presents and discusses the results of the controller's testing. Section 5 summarizes the research findings of this dissertation. Finally, the references used are provided.
# First Retrieval of Sea Surface Currents Using L-Band SAR in Satellite Formation
## 3 LuTan-1 Interferometric Experiment  
### Satellite SAR Data and Test Area  
LuTan-1 [28] is a high-resolution interferometric SAR mission that consists of twin satellites (A and B) carrying two SAR instruments operating at the L-band (f = 1.26 GHz) and orbiting in a double-helix formation [1]. The LuTan-1 data used here were acquired in the ascending pass, strip map, single transmission, and dual reception modes with the antenna looking to the right of the flight direction. Our dataset was acquired using HH polarization. The scene size for each acquisition was approximately 59 km in the azimuth direction and 55 km in the range direction, corresponding to an incidence angle varying between 27.86\({}^{\circ}\) and 32.28\({}^{\circ}\). The pixel sizes in the azimuth and range directions were 1.40 m and 1.67 m, respectively. The projection of the hybrid baseline into the along-track direction was approximately 575 m (the effective base length was 287.5 m), corresponding to a delay time of approximately 39.49 ms. Considering the average wind speed of 4.6 m/s shown in Figure 5a, the coherence time [29] owing to the time-varying characteristics of the local sea surface was computed as 242.12 ms, which exceeded the delay time of LuTan-1. The main acquisition parameters for the investigated images are summarized in Table 2.  
Figure 2: Schematic of across-track interferometry geometry.  
The interferometric images used in this study were obtained from the Yantai marine region of Shandong Peninsula, China, on 18 July 2022. The area covered by the satellite acquisition is shown in the red box in Figure 3. The Yantai Maritime Area consists of two main parts, the Bohai Sea and the northern Yellow Sea, connected by the Bohai Strait. The Bohai Sea region off Yantai, southeast of Bohai Bay, has an average water depth of 13 m and encompasses Laizhou Bay, Longkou Bay, and the mouths of seven major rivers. Intensity images of the primary (reference, serving as the benchmark) and secondary (aligned to the primary image for registration processing) images corresponding to the study area are shown in Figure 4.
# A Personalized Ontology Recommendation System to Effectively Support Ontology Development by Reuse
## 6 Evaluation
### Results and Discussion
The overall results of the precision evaluation are shown in Table 4. To calculate the precision up to any position \(k\) for any recommendation list, we considered the fraction of items in the system's recommendation list of size \(k\) that also exists in the user's recommendation list of the same size, regardless of their positions. For example, if the system's recommendation list of size 3 for a particular user query contains ontologies \(A\), \(B\), and \(C\), and the users re-ranked ontology \(A\) to be in the 4 th position (so it is not in the list of size 3), then the user's recommendation list of size 3 contains the ontologies \(B\), \(C\), and \(D\), and the P@3 is 0.66. Table 4 shows the \(P@k\) scores up to the 10 th position for all recommendation lists of the overall users' iterations. Our recommender exhibits a lower average precision of about 0.7 in the up-to-5-items recommendation lists, while the average precision value increases up to 0.93 in the up-to-10-items recommendation lists. This is an indicator that users re-ranked items in the first positions in the recommendation lists and rarely re-ranked items in the lower positions in the list. Figure 8 shows the \(P@k\) and \(AP@k\) scores for the size \(k\) recommendation lists up to 10 items, and the overall \(MAP\) score is 0.81. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Iteration No.** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** & **16** \\
**1/rank1** & 1 & 1 & 1 & 1/4 & 1/2 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1/3 & 1 & 1/2 \\ \hline
**Iteration No.** & **17** & **18** & **19** & **20** & **21** & **22** & **23** & **24** & **25** & **26** & **27** & **28** & **29** & **30** & **31** & **32** \\
**1/rank1** & 1/3 & 1 & 1/2 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline
**Iteration No.** & **33** & **34** & **35** & **36** & **37** & **38** & **39** & **40** & **41** & **42** & **43** & **44** & **45** & **46** & **MRR** \\
**1/rank1** & 1 & 1 & 1/2 & 1 & 1/3 & 1 & 1 & 1 & 1 & 1 & 1 & 1/2 & 1 & 1 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The reciprocal rank for each iteration and the \(MRR\) score value. Figure 8: \(P@k\) and \(AP@k\) scores for size k recommendation lists. To calculate the Cumulative Gain, we define the Gain for an item as an indicator of the relevancy of this item. It is a binary value indicating whether the user agreed with the item's rank (\({}_{Gi}\) = 1) or re-ranked it and put it in a different position (\({}_{Gi}\) = 0). We calculated the value of the \(NDCG\) for the top seven recommendations (as the smallest number of items in the recommendation lists is seven), which equals 0.96. A high \(NDCG\) value indicates that our system's recommendations were, in most cases, relevant to the user and need not be re-ranked by the user, thus indicating a good ranking quality.
# Effect of Thermal Treatment on Nano- and Micro-Copper Particles for Jewelry Making
## 2 Materials and Methods
### Preparation of Nano- and Micro-Copper Clay  
The synthesized nano- and micro-sized copper particles were mixed with the same types and weight ratio of organic binders and water to produce nano- and micro-copper clays. Sodium dodecyl sulfate (SDS) and methylcellulose (MC) were used as the clay's organic binder. Nano- and micro-copper clays were prepared using the same weight ratio of copper powder, SDS, MC, and water at 9.6: 0.1: 0.3: 2, respectively, to yield a clay-like material. Regarding our knowledge of the production process of nano-silver clay [4], type and weight percentage of binders in both copper clays were designed to obtain nano- and micro-copper clays. The unfired clay specimens were wrapped in Parafilm to prevent evaporation and oxidation from the atmosphere. Then, the synthesized nano- and micro-copper clays were molded into a square (10 \(\times\) 10 \(\times\) 2 mm) form to obtain the unfired clay specimens of both materials. After being dried at room temperature for 24 h, the heating experiments were conducted. To verify optimal firing conditions and to study the effects of copper particle size, heating conditions were controlled with temperatures ranging from 200 to 1000 \({}^{\circ}\)C, with a heating rate of 10 to 60 \({}^{\circ}\)C/min and holding time from 0 to 60 min.
# Residual Echo Suppression Considering Harmonic Distortion and Temporal Correlation
## 3 Harmonic Distortion Residual Echo Suppression  
HDRES [28] estimates the magnitude of the residual echo as a linear function of the linear echo estimates in the frequency bins that can affect the current frequency bin with a harmonic distortion, i.e., the frequencies that are quotients of the current frequency and integers. With the \(\delta(\cdot)\), which is 1 only for the frequency bins harmonically related to the current frequency and a few nearby bins, the estimate of the magnitude of the residual echo becomes:  
\[|\hat{D}_{r}(m,f)| =\sum_{i=1}^{M}\sum_{j=1}^{H}\sum_{k=-K}^{K}\delta(i,j,k,f)W_{H}(i,j,k)|X^{\prime}(m,i)|, \tag{5}\] \[\delta(i,j,k,f) =\begin{cases}1,&\text{if }i\times j+k=f\\ 0&\text{otherwise}\end{cases}, \tag{6}\]  
where \(M\) is the number of frequencies, \(H\) is the number of harmonics considered, \(K\) is the harmonic search window to accommodate nearby frequency bins to deal with the insufficient frequency resolution of the STFT, \(W_{H}(i,j,k)\)'s are weights of the linear combination, and \(X^{\prime}(m,f)\) is the linear echo estimate defined by [28]:  
\[|X^{\prime}(m,f)|=\sum_{t=0}^{S-1}L(t,f)|X(m-t,f)|, \tag{7}\]  
where the normalized weighting factor \(L(t,f)\) is:  
\[L(t,f)=\frac{|W_{L}(t,f)|}{\sum_{j=0}^{S-1}|W_{L}(j,f)|}, \tag{8}\]  
Figure 1: General block diagram of the acoustic echo cancellation system with residual echo suppression.  
in which \(W_{L}\) is the weight of the frequency domain linear adaptive AEC filter [8,28] considering \(S\) frames. The function \(\delta\) in (6) is constructed to deal with the harmonic distortion as the frequency contents falling in the \(i\)-th frequency bin affects the bins of which the frequencies are integer multiples of that frequency. The affected bins are centered on the bin \(i\times j\) but may also include nearby \(2K\) frequency bins, as the frequency resolution of the STFT is limited. The parameters of the residual echo suppression, \(W_{H}(i,j,k)\)'s, are estimated by a NLMS algorithm.
## 5 Implication in Catalysis  
In this section the implications of shape engineering in catalysis are presented on the basis of ceria-based transition metals. In particular, the effect of ceria nanoparticles geometry on the catalytic behavior of TMs/CeO\({}_{2}\) oxides is demonstrated on the grounds of indicative oxidation/reduction reactions, such as CO oxidation, nitrous oxide (N\({}_{2}\)O) decomposition and CO\({}_{2}\) hydrogenation to value-added products. The main purpose of this section is to demonstrate the pivotal role of support morphology on catalysis based on our recent findings in the field [33; 34; 35; 38; 39; 75], giving also particular emphasis to the establishment of reliable structure-performance relationships.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**BET Analysis**} & \multicolumn{2}{c}{**XRD Analysis**} & \multicolumn{2}{c}{**TPR Analysis**} \\ \cline{2-5}  & **BET Surface Area** & **Pore Volume** & **Average Crystallite Diameter, D\({}_{\text{XRD (nm)}}\)** & \multirow{2}{*}{**OSC (mmol O\({}_{2}\)/g)**} \\  & **(m\({}^{2}\)/g)** & **(cm\({}^{3}\)/g)** & **CeO\({}_{2}\)** & **CuO/Co\({}_{3}\)O\({}_{4}\)/Fe\({}_{2}\)O\({}_{3}\)/NiO** & \\ \hline Cu/CeO\({}_{2}\)-NC & 34.3 & 0.29 & 19.2 & 52 & 0.75 \\ Cu/CeO\({}_{2}\)-NR & 75.4 & 0.40 & 11.6 & 43 & 0.90 \\ Cu/CeO\({}_{2}\)-NP & 90.7 & 0.29 & 9.6 & 31 & 0.83 \\ \hline Co/CeO\({}_{2}\)-NC & 27.9 & 0.15 & 24 & 19 & 1.03 \\ Co/CeO\({}_{2}\)-NR & 71.8 & 0.31 & 14 & 16 & 1.19 \\ Co/CeO\({}_{2}\)-NP & 70.5 & 0.17 & 11 & 15 & 1.20 \\ \hline Fe/CeO\({}_{2}\)-NC & 32.2 & 0.19 & 16.8 & 52.3 & 0.67 \\ Fe/CeO\({}_{2}\)-NR & 68.6 & 0.19 & 9.7 & 7.2 & 0.75 \\ Fe/CeO\({}_{2}\)-NP & 64.2 & 0.12 & 8.5 & 16.5 & 0.70 \\ \hline Ni/CeO\({}_{2}\)-NC & 31.8 & 0.21 & 22 & 16.5 & 0.78 \\ Ni/CeO\({}_{2}\)-NR & 72.0 & 0.38 & 14 & 23 & 0.92 \\ Ni/CeO\({}_{2}\)-NP & 73.0 & 0.27 & 12 & 23 & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Textural, structural and redox characteristics of ceria-supported transition metal catalysts.
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 3 Vulnerabilities
### Key Extraction Attacks  
The confidentiality of secret information is an essential feature of the cryptographic algorithms involved in LoRaWAN. For instance, the **AppKey** should be stored in a secure manner in order to avoid any key-extraction attempts. However, in the reference implementations of LoRaWAN data transmission with the LoRaMAC libraries [61], the **AppKey** is directly defined in the program code. Hence, via physical access, an attacker might be able to extract **AppKey** with a firmware dump or via a microcontroller debug interface [37]. While secure key storage can be implemented with off-the-shelf microcontrollers, information leakage via side channels might be more difficult to avoid, as discussed below.  
In side-channel attacks, an attacker aims to indirectly extract sensitive information from a target device by measuring a physical quantity, which is affected by computation of, e.g., an encryption algorithm. A popular side-channel-measurement method involves the capturing of current-consumption waveforms out of which the information, e.g., secret keys, are extracted. A typical victim device, to this end, is a light-weight microcontroller with a relatively low clock rate (of a few MHz). Hence, as most of the LoRaWAN end devices embed such CPUs, side-channel attacks pose a realistic threat to confidentiality.  
The state-of-the-art literature reports a few cases of successful key extraction from a LoRaWAN device. The authors in [50] targeted the AES-CTR algorithm of the original Semtech template LoRaWAN-software implementation [61], to reveal **AppSKey** and **NwkSKey** out of recordings of electromagnetic emanations. The assumption here is that the plaintext payload is available to the attacker, e.g., a digitized sensor value. By applying the correlation power analysis technique, as given in [62], the authors could fully recover **AppSKey** and, partly, also **NwkSKey**. Additionally, the application of digital filtering for noise removal reduced the number of necessary trace captures to ca. 26%. In order to further reduce the number of necessary side-channel-trace captures, the authors of [51] proposed a deep-learning-based classifier to extract the secret keys. According to the experimental results, with a pretrained convolutional neural network model, a full recovery of **AppSKey** in ABP-activation mode becomes feasible with under 100 captured traces.