esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experiments and Methods
### Data Collection and Variable Selection  
Daily climatic data of four selected meteorological sites for period 2011-2017 were retrieved from Environment Canada historical database. Initially, nine different variables, namely heat degree days, hourly mean air temperature, minimum air temperature, maximum air temperature, relative humidity, dew point temperature, wind speed, atmospheric pressure, and daily mean air temperature, were selected. To capture the variation of climatic variables from different parts of the island, a new dataset (PEI dataset) was formed by averaging the variables of all four sites. The 2557 daily records of nine different variables used in this study were collected between the years 2011-2017 for the respective months of the period 2011-2017.  
Regression subset analysis was conducted with varied input combinations to select the appropriate inputs for artificial intelligence models. By selecting ETo as a response variable, the nine selected variables were regressed using Minitab (Version 18). Minitab is a statistical package developed by researcher [PERSON] from Pennsylvania State University. The best performing variables based on the highest coefficient of determination (R\({}^{2}\)) were selected as input variables for RNNs (Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Recurrent Neural Networks** &
\begin{tabular}{c} **Components of Recurrent Neural** \\ **Networks** \\ \end{tabular} & **Used Components after Trials** \\ \hline \multirow{6}{*}{Long short-term memory neural networks} & Hidden layers & 1 \\  & Forward layers & 1 \\  & Backward layers & 0 \\  & Neurons & 100 \\  & Learning rate & 0.01 \\  & Optimizer & Adam \\  & Batch size & 128 \\ \hline \multirow{6}{*}{Bidirectional long short-term memory neural networks} & Hidden layers & 2 \\  & Forward layers & 1 \\ \cline{1-1}  & Backward layers & 1 \\ \cline{1-1}  & Neurons & 100 \\ \cline{1-1}  & Learning rate & 0.001 \\ \cline{1-1}  & Optimizer & Adam \\ \cline{1-1}  & Batch size & 256 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The structures of recurrent neural networks for analysis of this study finalized after several trials.  
Figure 1: Locations of the four selected meteorological stations across Prince Edward Island, Canada.
# Numerical Simulation and Analysis of Fish-Like Robots Swarm
## 3 Results and Discussion
### The Rectangle Swarm  
The thrust and drag at different spacing in the rectangle swarm are shown in Figure 8b. It is verified by simulations that the net force of each fish is about zero. Therefore, the fishes were able to maintain the rectangle formation, and the Froude efficiency \(\eta\) is useful.  
We calculated the non-dimensional thrust, lateral power loss coefficients and the Froude efficiency of each fish. The results are shown in Figure 18. In the rectangle swarm, the thrust coefficients of no.1 fish and no.3 fish, no.2 fish and no.4 fish in the symmetrical position were basically equal. None of the fish had an obvious advantage for the lateral power loss and the Froude efficiency. Nevertheless, the performance of fish behind was better than that of fish in front.  
In the rectangle swarm, at close spacing, the fish was affected by the pressure of the adjacent fish swing and resulting in low efficiency; at large spacing, the wake was small and weak, which also leads to low efficiency. Therefore, the moderate spacing \((1.5L)\) made the rectangle swarm the most efficient. The wake is shown in Figure 19, and the pressure field is shown in Figure 20.  
Figure 16: The wake of the diamond swarm: (**a**) spacing = \(1.75L\), (**b**) spacing = \(1.25L\).  
Figure 17: The contours in pressure field of the diamond swarm: (**a**) spacing = \(0.75L\), (**b**) spacing = \(1.25L\).  
Figure 18: Results of the rectangle swarm: (**a**) non-dimensional thrust coefficients \(C_{l}\), (**b**) non-dimensional lateral power loss coefficients \(C_{p}\), (**c**) the Froude efficiency \(\eta\).
## 1 Introduction
### Applications of Computer Vision Technology
The integration of machine learning and computer vision in tunneling and underground construction was explored in [8], along with their unique applications and challenges compared to above-ground construction, and an overview of emerging technologies in the architecture, engineering, and construction industry was provided. Specific applications, such as real-time monitoring, geological prediction, and automated equipment control, are enabled by these technologies, while challenges like limited visibility, complex subsurface conditions, and data scarcity are faced, distinguishing them from above-ground contexts. Emerging technologies, including Building Information Modelling (BIM), digital twins, and advanced robotics, are highlighted as transformative tools, with their potential to enhance efficiency, safety, and sustainability across the industry being emphasized. The role of computer vision (CV) in enhancing e-waste management by automating and streamlining waste processing was examined in [9]. Computer vision is expected to reduce human intervention, processing time, and cost, thus increasing the efficiency and sustainability of e-waste management. In India, where e-waste management is a major environmental and social challenge, CV is a must. In intelligent transportation systems (ITS), CV is utilized to boost efficiency, intelligence, and safety [10]. Hand gesture recognition techniques using CV are reviewed for applications in communication for the deaf-mute, robot control, human-computer interaction, home automation, and medical fields [11]. Automated fruit and vegetable sorting and grading with CV are assessed, highlighting improved consistency, productivity, and economic growth by evaluating appearance for market value and quality [12]. Civil infrastructure condition assessment employs CV with remote cameras and UAVs for non-contact monitoring, addressing automation challenges, and ongoing research [13]. In agriculture, CV's potential and challenges in monitoring pig behavior for animal welfare in commercial production systems are described, noting dataset creation issues [14]. Offsite construction benefits from CV, with its applications, algorithms, and performance evaluated in a scoping review [15]. Safety in construction is improved through CV and deep learning frameworks, focusing on identifying unsafe conditions and behaviors despite implementation challenges [16]. Lastly, crime detection, prevention, and resolution are enhanced by CV and machine learning, with examples demonstrating effectiveness and statistical improvements in law enforcement [17]. Across these sectors, CV drives innovation while facing unique technical and practical challenges.
## 3 Simulation Results
### Impact of Pole Head Radius on Magnetic Flux Flow  
The magnetic flux flow is considerably controlled by the pole head radius, which can impact the working area of an MRF triggered by shear stress. To achieve higher flux distribution, a greater core radius or a higher pole head radius must be built. A large  
Figure 8: Magnetic field lines on a pole head.  
Figure 7: Field distribution of MRB at 2 Amp.  
core radius lowers the available space for coil winding, which affects the magnetic field. So, a large pole radius efficiently covers a wider area and the flux concentration will be uniform across the surface within a specific region. From the results, we can observe that the proposed sector pole head design can ensure better control over the magnetic field distribution, as illustrated in Figure 9. A wider pole head radius shows more concentrated magnetic field lines around the immediate surrounding area of the pole head, as well as across a broader space. It is obvious that there is a larger magnetic flux density near the pole head, and that the flux flow diminishes as far as the region from the center of the MRB. The magnetic field analysis shows that the magnetic flux is penetrating orthogonally through the MRF and forming a closed loop with the adjacent pole head, as shown in Figure 10. It can be seen that the magnetic flux lines are uniformly distributed throughout the configuration, which is essentially the same as the magnetic circuit design.
# Spectrum Demand Forecasting for IoT Services
## 6 Study Case Results: Colombia
Since it is difficult to predict if 5G and other IoT networks will be progressively installed and to which extent in Colombia during the next decade; therefore, from now on, all results are calculated for an intermediate case where both the spectral efficiency and teledensities are increased linearly in 10 years, but just to half of the values suggested by ITU for IMT-2020 requirements detailed in [50]. Regarding all different coverage areas in Figure 5a, NCB present very low spectrum requirements except for Home/office where residential Gbit internet connections will take place and Street where security and traffic cameras will require most of the spectrum. Both results will occur in a mmWave band as can be seen in Figure 5b and require only local spectrum as is the case of both scenarios 3 and 10 as shown in Figure 3. The NCB add a total of 232 MHz, but 231 MHz of them are in the mmWave band. On the other side, CB will require around 10 MHz in every coverage area with a peak in health-related areas, where advanced healthcare services will increase in popularity as the population ages. Figure 4: Cellular band (CB) and non-cellular band (NCB) requirements per economic sector. Regarding the frequency bands, the mmWave band has the largest requirement, which should not be difficult to meet due to the large spectrum availability in mmW frequency bands. The other NCB should also be easy to allocate since all of them are below 1 MHz bandwidth requirement. In the cellular bands, there will be important amounts of bandwidth to allocate. The total amount is 55 MHz with almost 27 MHz in the urban, rural, and industrial coverage areas in the Sub-1 GHz bands. This spectrum might not be easily available, but not releasing it might strongly reduce IoT development in the country. Finally, Figure 6 summarizes the spectrum requirement for 2030. The largest portion in the mmWave band for Street and Home/Office coverage areas, important spectrum amounts are foreseen in CB across all frequencies and coverage areas and a minor role for other technologies in NCB. In our consideration, in the next 10 years, cellular operators will not use mmWave spectrum in Colombia, which will probably be the case in the mid-term future. Figure 5: Cellular and non-cellular spectrum requirement increase from 2021 to 2030: (**a**) per coverage area and (**b**) per frequency bands.
or private spaces/properties are recorded without their consent or awareness. In edge computing scenarios, where processing tasks are offloaded to local servers or cloudlets situated near the edge of the network, there is a risk that sensitive information (e.g., the face of a stranger) in the camera view may be exposed to unauthorized entities. This potential breach of privacy underscores the importance of implementing robust privacy protection mechanisms in edge computing environments.  
Intuitive privacy protection methods, such as data encryption, do not address the visual privacy problem because they only provide secure communication between drones and cloudlets without preventing cloudlets from accessing the data. Indeed, for the purpose of live video analytics, the cloudlet must access the visual data. Clearly, a good balance is needed for providing data utility while protecting visual privacy.  
In light of these considerations, in this work, we propose a privacy-preserving framework for drones centered around live video analytics via edge computing. The major contributions of this paper are as follows:  
* A novel framework for secure object detection and tracking utilizing a distributed deep learning architecture. We propose a method that preserves privacy while maintaining practical latency for real-time analytics through careful model selection on drones and the assistance of cloudlets.
* A lightweight method to prevent the leakage of sensitive information through feature maps, employing a combination of noise injection and a feature map selection strategy, with minimal compromise in accuracy.
* Comprehensive evaluations with real-world datasets, which demonstrate the effectiveness of visual privacy protection against reconstruction attacks as well as the effectiveness of video analytics in terms of accuracy and latency.  
The proposed framework is discussed within the context of object detection and tracking but can be extended to similar Computer Vision tasks relevant to drones without loss of generality.
## 4 Conclusions  
This paper proposed a novel deep learning model that combines CNNs and LSTM networks with an attention mechanism to predict electricity demand in NSW. The forecasting results indicated that the proposed model outperforms other models, achieving the lowest MAE and MAPE among two individual models (CNN and DNN), and four hybrid models (DNN and CNN, DNN and LSTM, CNN and LSTM with attention). This hybrid architecture offers a promising approach for electricity demand prediction, potentially leading to better electricity management and cost savings. This study is the first to utilize a CNN-LSTM model with an attention mechanism for forecasting electricity demand, leveraging HDD and CDD data specific to NSW, Australia, during the years 2020 through 2023.  
Future research could incorporate additional weather variables, expand the model's application to diverse geographical regions, and evaluate its performance on multi-yeardata to gain a deeper understanding of seasonal trends and long-term fluctuations in electricity demand. We also plan to explore emerging technologies, including probabilistic forecasting and advanced Transformer-based models like the TFT or Informer, which have become increasingly influential in time series processing. Moreover, future studies could examine optimization strategies for this model, such as implementing distributed training, to reduce its computational burden while preserving its prediction capabilities.  
Conceptualization, L.A.; methodology, L.A.; software, L.A.; validation, L.A.; formal analysis, L.A.; investigation, L.A.; resources, L.A.; data curation, L.A.; writing--original draft preparation, L.A.; writing--review and editing, L.A., G.L. and L.L.; visualization, L.A.; supervision, G.L. and L.L.; project administration, G.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# Evaluation of a New-Concept Secure File Server Solution
## 2 Background
### State-of-the-Art Malware Prevention Techniques  
There are many complex protection solutions on the market today, many of which are also antivirus solutions. They aim to protect both the server environment and the various endpoint clients from ransomware attacks. There are also several solutions that focus more on a proper backup strategy and try to minimize the damage caused in the event of a successful attack by using multiple, automated, often cloud-based backups. Currently, available high-end solutions include the following key features: signature-based ransomware detection and prevention, behavior-based threat prevention, depth knowledge-based AI capabilities, real-time memory protection, critical component filtering, real-time file filtering, plant decoy files to detect ransomware, automated server and workstation backup, IoT discovery and control, cloud security, and local and remote file encryption.  
In addition to the above, some large enterprise solutions (e.g., IBM Security QRadar XDR [14], Bitdefender GravityZone Business Security Premium [15], Sophos Intercept X with XDR [16], Trend Micro Vision One [17]) monitor network traffic (C2, outbound traffic, DNS queries, etc.), include detailed logging and alerting services (SIEM), collect and automatically correlate data across multiple security layers (email, endpoint, server, cloud storage, network), and include features to enable data recovery.
# A Tile-Based Multi-Core Hardware Architecture for Lossless Image Compression and Decompression
## 2 Compression and Decompression Algorithms Based on Hybrid Strategies
### Strategies
#### 2.3.3 Non-Coding  
This study introduces a non-coding strategy to efficiently manage large prediction errors in image compression, ensuring that the encoding of pixel components remains concise and does not exceed 10 bits, even under the most challenging conditions. In predictive coding methods, the occurrence of large prediction errors can potentially inflate the bit count significantly, especially when the error magnitude surpasses 127. Conventionally, this would result in a bit sequence extending beyond 9 bits, plus an additional flag bit, cumulatively exceeding the 10-bit threshold. This inefficiency escalates with the magnitude of the prediction error, rendering the process less optimal. To mitigate this issue, the non-coding mode is introduced as a fallback mechanism. This mode is triggered when large prediction errors are detected, specifically when the error value causes the Golomb-Rice coding to exceed 9 bits. Instead of proceeding with the standard predictive coding that would yield a verbose bit sequence, the algorithm opts for the non-coding mode. It outputs  
Figure 4: Example of run-length coding.  
the corresponding flag bit '01B', followed directly by the original, uncompressed value of the current pixel component. This ensures that the encoding for any single pixel component is neatly capped at 10 bits.  
During the decompression phase, the algorithm actively monitors for the '01B' flag. Upon its detection, the following 8 bits in the sequence are interpreted as the actual value of the pixel component, without the need for further decoding. This direct method ensures a swift and efficient decompression output. This strategic approach to managing large prediction errors preserves the succinctness and efficiency of the compression algorithm. It provides a robust solution to circumvent the potential expansion of the bit stream, thereby ensuring that the overall compression remains effective even when faced with substantial prediction errors.
# Protein Kinases in Mediating Phage-Bacteria Interactions
## 1 Introduction  
Bacterial survival in dynamic environments relies on sophisticated signalling systems, including particularly histidine kinases (HKs) and serine/threonine kinases (STKs) (for a more exhaustive classification and discussion of bacterial kinases refer to [1]), which govern adaptive responses to stressors ranging from nutrient scarcity to antimicrobial threats. HKs, central to two-component systems (TCSs) (e.g., _Escherichia coli_ EnvZ/OmpR), transduce extracellular signals via histidine phosphorylation, driving stress adaptation, antibiotic tolerance, resistance [2] and virulence [3]. STKs, resembling eukaryotic kinases, regulate intracellular processes such as _Staphylococcus aureus_ PknB-dependent efflux pump activation [4] and _E. coli_ HipA-induced persister cell formation [5, 6], linking kinase activity to antibiotic tolerance and resistance (refer to [7] for further clarifications of their differences).  
While these kinases are well-documented in stress resilience and therapeutic evasion [1, 2, 8], their roles in phage-bacteria interactions remain poorly understood. However, investigating these roles is critical given the therapeutic promise of phages as potential alternatives to antibiotics. For instance, _Marinomonas mediterranean_ TCS BarA/UvrY modulates CRISPR-Cas defenses [9], and phages like T7 deploy kinase mimics (Gp0.7) to subvert host signalling [10]. Moreover, most studies of phage-bacteria interactions fail to account for environmental stress conditions, which dynamically reshape bacterial physiology and may concurrently influence phage infectivity and antibiotic susceptibility. This knowledge gap remains unresolved, even if multidrug-resistant pathogens escalate into a global health crisis and phage therapy emerges as a promising therapeutic alternative.  
By synthesizing recent advances, this article highlights the dual significance of HKs/STKs in stress-driven antibiotic resistance and more their nascent, yet critical, intersections with phage defense--a triad of interactions essential for deciphering bacterial adaptability and guiding next-generation antimicrobial strategies.
# Mechanical Fault Prognosis through Spectral Analysis of Vibration Signals
###### Abstract  
Vibration signal analysis is the most common technique used for mechanical vibration monitoring. By using vibration sensors, the fault prognosis of rotating machinery provides a way to detect possible machine damage at an early stage and prevent property losses by taking appropriate measures. We first propose a digital integrator in frequency domain by combining fast Fourier transform with digital filtering. The velocity and displacement signals are, respectively, obtained from an acceleration signal by means of two digital integrators. We then propose a fast method for the calculation of the envelope spectra and instantaneous frequency by using the spectral properties of the signals. Cepstrum is also introduced in order to detect the unidentifiable periodic signal in the power spectrum. Further, a fault prognosis algorithm is presented by exploiting these spectral analyses. Finally, we design and implement a visualized real-time vibration analyzer on a Raspberry Pi embedded system, where our fault prognosis algorithm is the core algorithm. The real-time signals of acceleration, velocity, displacement of vibration, as well as their corresponding spectra and statistics, are visualized. The developed fault prognosis system has been successfully deployed in a water company.  
digital integrator; envelope spectrum; cepstrum; fault prognosis; Raspberry Pi 1594