esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Transaction Scheduling to Enhance Concurrency in High-Contention Workloads
## 2 Related Work
### Concurrency Control
#### 2.2.2 Transaction Dependency Management  
Mitigating transaction contention and optimizing CC synchronization overhead are critical challenges in high-performance transaction processing systems. Effective management of inter-transaction dependency and intra-transaction dependency can significantly improve system throughput and reduce latency.  
* **Inter-transaction dependency:** [PERSON] [17] maintains dependency information between transaction groups, ensuring that dependent transactions only proceed after the completion of prerequisite transactions. [PERSON] et al. [18] propose a thread-aware policy to reorder transactions efficiently in a multi-threaded OLTP architecture based on OCC. AlNiCo [19] utilizes SmartNICs to route transaction requests to CPU cores, reducing contention with low latency intelligently.
* **Intra-transaction dependency:** Polyjuice [18] leverages RL model to explore a policy space of fine-grained actions for individual operations. [PERSON] [20] introduces a CC protocol that extends traditional 2 PL by dynamically assigning timestamps, allowing locks on hot data items to be retired early while preserving serializability. [PERSON] [21] modifies lock order to reduce lock hold times for hot records.
# Fast Prediction of Flow Field around Airfoils Based on Deep Convolutional Neural Network
## 3 Results and Discussion  
### Effect of Training Parameters  
To study the effect of hyperparameters on the convergence and accuracy of the proposed CNN-DCNN model during the training process, different sets of values are tested for the training parameters, namely the learning rate, the mini-batch size and the random deactivation rate (dropout). The parameters are studied by means of the control variable, so a large number of parameter combinations have to be tested. Results with parameter values around the finally chosen ones are reported in Figure 9, where lines in red color stand for the reference case, in which the learning rate, the mini-batch size, and the random deactivation rate are set to 0.005, 32, and 0.2, respectively, as the reference values. Throughout the training process, results are sampled every 200 epochs. It should be noted that the sampling frequency of 200 just denotes the frequency of recording model accuracy, which is set to save time merely and will not affect the training of model.  
Figure 8: The geometry of 30 airfoils sampled from the airfoil library (NACA0012 highlighted by the solid red line).  
Figure 9(a) shows the convergence curves with values of the learning rate (abbreviated as \"lr\" in the figure), for both the training and validation accuracy of the proposed model. Generally, the validation accuracy fluctuates closely around the training accuracy, and both curves converge normally with the a learning rate of 0.0005. Smaller learning rate of 0.0001 would slow down the convergence, although the same level of accuracy could be achieved with enough number of iterations. Larger learning rate of 0.0009 does accelerate the learning process in the early stage, however, it would be trapped by a local minimum which failed the training process.  
Impacts of mini-batch size and dropout are displayed respectively in panels (b) and (c) of Figure 9. Compared with the reference value of 32, the smaller mini-batch size turns out to have higher gradient oscillation between adjacent iterations, which is not conducive to the convergence of the model. A larger mini-batch size, however, would get caught by a local optimum, and thus could not reach the same level of accuracy as the other two setups. As for the choice of dropout, which is employed by the CNN-DCNN model to avoid overfitting, Figure 9(c) implies that the appropriate value (0.2 in the current case) could accelerate the convergence of the training process, and in turn benefit the generalization ability of the model handling unseen data during the testing stage. The hyperparameters adopted for training and prediction in this work are summarized in Table 3.
###### Abstract  
The city of Naples (Campanian region, Southern Italy) has been hit by the strongest earthquakes located inside the seismogenic areas of the Southern Apennines, as well as by the volcano-tectonic earthquakes of the surrounding areas of the Campi Flegrei, Ischia and Vesuvius volcanic districts. An analysis of the available seismic catalogues shows that in the last millennium, more than 100 earthquakes have struck Naples with intensities rating I to III on the Mercalli-Cancani-Sieberg (MCS) scale over the felt level. Ten of these events have exceeded the damage level, with a few of these possessing an intensity greater than VII MCS. The catastrophic earthquakes of 1456 (I\({}_{0}\) = XI MCS), 1688 (I\({}_{0}\) = XI MCS) and 1805 (I\({}_{0}\) = X MCS) occurred in the Campania-Molise Apennines chain, produced devastating effects on the urban heritage of the city of Naples, reaching levels of damage equal to VIII MCS. In the 20 th century, the city of Naples was hit by three strong earthquakes in 1930 (I\({}_{0}\) = X MCS), 1962 (I\({}_{0}\) = IX MCS) and 1980 (I\({}_{0}\) = X MCS), all with epicenters in the Campania and Basilicata regions. The last one is still deeply engraved in the collective memory, having led to the deaths of nearly 3000 individuals and resulted in the near-total destruction of some Apennine villages. Moreover, the city of Naples has also been hit by ancient historical earthquakes that originated in the Campanian volcanic districts of Campi Flegrei, Vesuvio and Ischia, with intensities up to VII-VIII MCS (highest in the Vesuvian area). Based on the intensity and frequency of its past earthquakes, the city of Naples is currently classified in the second seismic category, meaning that it is characterized by \"seismicity of medium energy\". In this paper, we determine the level of damage suffered by Naples and its monuments as a result of the strongest earthquakes that have hit the city throughout history, highlighting its repetitiveness in some areas. To this aim, we reconstructed the seismic history of some of the most representative urban monuments, using documentary and historical sources data related to the effects of strong earthquakes of the Southern Apennines on the city of Naples. The ultimate purpose of this study is to perform a seismic macro-zoning of the ancient center of city and reduce seismic risk. Our contribution represents an original elaboration on the existing literature by creating a damage-density map of the strongest earthquakes and highlighting, for the first time, the areas of the city of Naples that are most vulnerable to strong earthquakes in the future. These data could be of fundamental importance to the construction of detailed maps of seismic microzones. Our study contributes to the mitigation of seismic risk in the city of Naples, and provides useful advice that can be used to protect the historical heritage of Naples, whose historical center is a UNESCO World Heritage site.  
**Keywords:** Naples (Southern Italy), historical earthquakes; Southern Apennines; seismic hazard
## 4 Key Enabling Technologies
### System Deploy
#### 4.3.2 Software System
Additionally, Redis functions as a short-term caching layer for frequently accessed data, increasing query speed, while Kafka is integrated for real-time data streaming, ensuring efficient data transmission and persistence. The system's functional modules are structured as follows:  
The Weather Monitoring Module integrates a weather station data display interface, providing real-time meteorological information, including temperature, humidity, wind speed, and precipitation levels. This module ensures continuous environmental monitoring to support decision-making in agricultural operations. The Scheduling Management Module features a visualized scheduling interface (as shown in Figure 15), enabling real-time monitoring of agricultural machinery operations. It supports task allocation and path adjustments, displaying key information such as the working area, task status, and navigation paths of the machinery. By integrating an optimized scheduling algorithm, this module enhances multi-machine collaboration efficiency, minimizing energy consumption and idle time. Additionally, it leverages Apache Flink for real-time stream processing, ensuring precise scheduling decisions. Figure 14: Comprehensive environmental monitoring. The Autonomous Driving Control Module is designed with an intelligent control interface (as shown in Figure 16) for real-time monitoring of trajectories, path deviations, and control parameters of agricultural machinery. It supports remote debugging and parameter adjustments, allowing for flexible operation management. By incorporating the control optimization algorithm proposed in this study, the module enhances path-tracking accuracy and overall system stability, ensuring safe and efficient autonomous operations.
## 2 Materials and Methods
### Feature Extraction
#### 2.3.1 Radiomics (\(R\)) Feature  
The \(R\) feature group consisted of first-order (\(n=14\)), texture (\(n=40\)), and wavelet decomposition (WD) features (\(n=432\)). However, the WD features were calculated from the CT-only model, and the cold-spot volume (\(n=1\)) was added only to the DD. Cold spots were defined as 95% of the prescribed dose, according to the Radiation Therapy Oncology Group 1016 protocol [27]. Therefore, \(R\) values for CT and DD (CT-\(R\) and DD-\(R\)) were 486 and 55, respectively. Texture features were calculated from four categories: the gray-level cooccurrence matrix (GLCM), gray-level run length matrix (GLRLM), gray-level size zone matrix (GLSZM), and neighborhood gray-tone matrix (NGTDM [28; 29; 30; 31]). WD was performed by applying either a low- (\(L\)) or high-pass filter (\(H\)) in each direction of the three-dimensional volume using a Coiflet1 mother wavelet [32]. The 432 WD features were derived by calculating 14 first-order and 40 texture features for each of the eight wavelet decomposition images: LLL, LLH, LHL, LHH, HLL, HLH, HHL, and HHH.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Category** & **Data** & **Feature** & **No. of Features** \\ \hline \multirow{8}{*}{\(R\)} & \multirow{4}{*}{CT} & First-order features & 14 \\  & & Texture features (GLCM, GLRLM, GLSZM, and NGTDM) & 40 \\  & & Wavelet decomposition features & 432 \\ \cline{2-4}  & & \multirow{4}{*}{DD} & First-order features & 14 \\  & & Texture features (GLCM, GLRLM, GLSZM, and NGTDM) & 40 \\  & & Cold spot volume & 1 \\ \hline \multirow{8}{*}{\(L\)} & \multirow{4}{*}{CT} & First-order features & 14 \\  & & Texture features (GLCM, GLRLM, GLSZM, and NGTDM) & 40 \\ \cline{1-1}  & & \multirow{4}{*}{DD} & First-order features & 14 \\  & & Texture features (GLCM, GLRLM, GLSZM, and NGTDM) & 40 \\ \hline \multirow{8}{*}{\(T\)} & \multirow{4}{*}{CT} & Betti number (b0, b2, and b2/b0) & 303 \\  & & Area under the Betti number curve & 3 \\ \cline{1-1}  & & Maximum Betti number & 3 \\ \cline{1-1} \cline{2-4}  & & \multirow{4}{*}{DD} & Betti number (b0, b2, and b2/b0) & 63 \\ \cline{1-1}  & & Area under the Betti number curve & 3 \\ \cline{1-1}  & & Maximum Betti number & 3 \\ \hline \end{tabular}
\end{table}
Table 2: Extracted features.
# Study on the Anti-Interference Performance of Substrate-Free PEDOT:PSS ECG Electrodes
## 4 Conclusions  
This study systematically investigated factors affecting skin-electrode impedance and ECG signals of substrate-free PEDOT:PSS electrodes. The results show that skin-electrode contact status and electrode size significantly influence the skin-electrode impedance. Partial contact leads to poor performance in both impedance stability and ECG signal quality. Conversely, when electrodes form conformal contact with the skin, neither size nor shape significantly affects ECG signals, emphasizing the importance of impedance stability. Among dynamic factors, friction between fabric and electrode caused impedance changes and baseline interference, while breathing frequency did not significantly affect impedance and fast breathing resulted in baseline interference and ECG waveform disruption. The study demonstrates that ECG interference is not solely caused by skin-electrode interface impedance, but that it is also affected by chest and abdominal movements during breathing.  
The results of this study provide a theoretical basis for the further optimization of substrate-free electrodes, toward the realization of high-quality, long-term, and comfortable physiological signal monitoring. This research highlights the importance of skin-electrode impedance stability and the impact of dynamic factors like friction between fabric and electrodes and breathing on ECG signal quality. Ongoing improvements in the reliability and durability of substrate-free electrodes are necessary to meet the higher demands of future wearable health-monitoring applications. Future research should focus on correlations between impedance and physiological signals, impedance changes related to human activities, and developing improved interference filtering circuits.  
Conceptualization, Y.C.; methodology, C.L.; formal analysis, C.L.; investigation, C.L.; resources, Y.C.; data curation, Y.C. and C.L.; writing--original draft preparation, C.L.; writing--review and editing, Y.C., C.L., and K.X.; supervision, Y.C.; project administration, C.L.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 52005115).  
Not applicable.  
Figure 9: Experimental results for different breathing frequencies. (**a**) Bode plot of skin–electrode impedance; (**b**) Spectrogram of ECG; (**c**) Waveform of ECG.
## 2 Related Work on Diacritics Restoration
### Classical Approaches
#### 2.1.4 Character-Level Approaches
Even more comprehensive comparative experiments are performed in [31], and they cover 100 languages and several approaches, such as the lexicon lookup, the lexicon lookup with the bigram language model, several character-level methods with various window sizes, the hybrid of the lexicon lookup with the bigram language model (for words in the lexicon), and the character-level approach (for words that are not in the lexicon). With some exceptions, the hybrid approach performs the best for the majority of languages. A similar hybrid approach is also successfully applied to the Romanian language [32]. The candidates for each recognized undiacritized target word are generated based on mappings of the dictionary, and the appropriate candidates are selected with the Hidden Markov Model (HMM)-based language model. The diacritics for unknown words are restored with the character-level approach (described in [25]) using windows with up to eight characters. Another hybrid approach that is used for completely different purposes (to clarify/claim the output of the character-based method) is presented in [33] for the Turkish language. During the first stage, it performs the sequence classification with the CRF method, but next to current/neighboring character,s it also uses the current/neighboring tokens as features, i.e., five character-level and two word-level features. The output of the first stage is fed into the morphological analyzer-based language validator. The authors compared their hybrid approach with several others (rule-based, rule-based with the unigram language model, and character-based but without language validator stage) and proved it is the best model to use. In contrast to the previously described approaches, the sequence labeling problem can be solved, not on the character, but the syllable level, as in [34]. The authors solved the instance-based classification problem by treating each syllable as a separate independent classification instance and applying the SVM classifier on top. They used different types of features, such as the \(n\)-grams of syllables (surrounding the target with window sizes of 2 and 3); syllable types (uppercase, lowercase, number, other), characterizing surrounding syllables, and dictionary-based features (dictionary words that contain the target syllable). The method achieves a high accuracy on Vietnamese texts.
## 5 Evaluation  
This section elaborates on the scalability and performance overhead of our delegated policy evaluation for data-driven workflows. Our goal is to carry out a systematic scalability assessment using the Universal Scalability Law (USL) [25; 26]. The USL combines (a) the initial linear scalability of a system under increasing load, (b) the cost of sharing resources, (c) the diminishing returns due to contention and (d) the negative returns from incoherency into a model that defines the relative capacity \(C(N)\):  
\[C(N)=\frac{N}{1+\alpha(N-1)+\beta N(N-1)} \tag{1}\]  
Figure 14: Workflows of authorization tasks as a pre- or post-condition of an authorization policy.  
where \(N\) represents the scalability of the software system in terms of the number of concurrent requests, \(\alpha\) represents the contention penalty and \(\beta\) defines the coherency penalty, with \(0\leq\alpha,\beta<1\). To benchmark the scalability, \(N\) is incremented on a fixed configuration.  
The authorization policies used in our setup are similar in complexity to the ones we described earlier. The performance evaluation does not include the time required for subjects to authenticate. All subjects are authenticated in advance. For a scalability assessment of OpenAM itself, we refer to our previous work [27].  
In our experimental setup, we deploy all microservices in a Docker container on a Dell PowerEdge R620 server with 32 Intel Xeon E5-2650 CPU cores running at 2.00 GHz and 64 GB of memory connected to a 1 Gigabit network. We simulated an increasing number of concurrent access control requests from multiple users. We compare two performance criteria:  
* The latency overhead of delegating the evaluation of the policy to another microservice;
* The performance trade-offs of policy delegation vs. local policy evaluation.
## 2 Proposed Framework and Experimental Methodology
### Proposed Encryption Workflow with Hybrid CPU-GPU Strategies
#### 2.5.1 Data Shuffling  
In this method, data are partitioned into blocks, and a seeded random generator is used to assign each block to either the CPU or GPU, as shown in Algorithm 1. Random seed (\(R\)) is a uniformly distributed pseudo-random number between 0 and 1. If \(R<0.5\), the block is assigned to the CPU; otherwise, it is assigned to the GPU. This randomization ensures workload balance and helps avoid predictable encryption patterns, enhancing both performance and security. The CPU processes its assigned blocks sequentially, while the GPU performs parallel encryption on its assigned blocks, ensuring balanced workload distribution and enhanced security [22].  
Figure 5: Experimental flowchart for hybrid method evaluation. *The asterisk symbols indicate implementation details: “Python/CUDA/nvprof Profiling” refers to the software environment used for benchmarking setup, and “AES-128 Encryption in CTR” indicates the encryption scheme used during execution.
# A Thermal Discomfort Index for Demand Response Control in Residential Water Heaters
## 4 The Proposed Approach for Defining a Discomfort Index
### Data Preparation  
The data used in this article were provided by the Uruguayan National Electricity Company (UTE). Data are available in the \"Electric water heater consumption\" dataset, one of the three subsets included in EDC-UY [6], an effort to build a national database of energy consumption by gathering data from several households located in the main Uruguayan cities.  
In this article, only the electric water heater consumption records were used. These records have a sample period of one minute and cover a date range from 2 July 2019 to 26 October 2020. Customer records were filtered by the recording length, keeping only those with more than 5 months of recording (i.e., at least 216,000 records).  
The disaggregated electric water heater data have several gaps caused by different problems that arose during the data collection process (e.g., malfunctioning of the data transmission network, power failures, etc.). The gaps were filled using two techniques: resampling and refilling. The resampling technique normalizes the sample period to an exact minute. First, the records are grouped by customers to build one-minute record containers, starting from the date and time of the first record. Then, records whose date and time match with the date range of the container are assigned to it. In case one or more records match the same container, the minimum consumption value is set; otherwise, a null value is set (i.e., it corresponds to a missing record). The resulting data are taken as the input of the refilling technique. First, refilling detects the data gaps (i.e., consecutive missed records) and refills each one of them according to the following criteria. Starting from both extremes of the gap up to seven minutes forward/backwards, the missing data is recreated by a linear interpolation method. Finally, if missing values are still present at the gap (i.e., the gap is larger than 14 min), the null value is assigned to all missing values. The described process results in normalized time series of consumption values without gaps. For data preparation purposes, a Jupyter notebook was implemented, and the basis of the scripts was provided by the ECD-UY dataset. The Jupyter notebook uses Python (version 3) programming language and libraries Pandas and Numpy. The resulting notebook is available to download from [[https://bit.ly/3h133](https://bit.ly/3h133) qu]([https://bit.ly/3h133](https://bit.ly/3h133) qu) (accessed on 23 October 2021).  
An example of the effects of the refilling procedure on electric water heater activations is presented in Figure 2 (missing records) and Figure 3 (after processing). Both figures show the same water heater activations in the same date and time range. The first graphic shows the dataset before refilling the gaps and include the missing records. The second graphic was captured after the data were processed.
# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 1 Introduction  
With ever-improving and advancing technology, we are amid a communication revolution, a period of fundamental paradigm shift that arguably started decades ago, either with the invention of the Internet or the personal computer. From the ability to communicate electronically across a network of computers connected by cabled Internet, we moved to using wireless connections; and, with that, came the opportunity to create network connections relatively _ad hoc_. Customers in a coffee shop, for example, can establish a connection with the local router and access the Internet that way.  
The next step was the use of mobile resources, such as routers placed on other hardware, that, through the movement of their platform, moved into and out of positions that made them suitable nodes in Mobile Ad Hoc Networks (MANETs). Although such networks were originally comprised of nodes placed on other devices and systems, and thus subject to the connections created accidentally or as a byproduct of the operation of these devices and systems, the use of dedicated mobile platforms tasked primarily with the movement of nodes is finding increased attention in the literature.