esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Experimental Area Description  
The experimental site is located in a Mixed Ombrophilous Forest (MOF), covering 32 hectares in Sao Joao do Triunfo, State of Parana, southern Brazil, within the Atlantic rainforest biome. The site is classified as a disturbed primary forest and represents a remnant of a much larger forest that once covered 182,000 km\({}^{2}\)[26]. The predominant species is Araucaria trees (_Araucaria angustifolia_), accounting for 42% to 48% of the sampled species at the site, with canopy heights ranging from 10 to 25 m [27]. The lower stratum of the forest includes a mixture of shrubs and herbaceous plants, typically reaching heights between 0.5 and 2 m, along with small trees such as yerba mate (_Ilex paraquariensis_), cinnamon (_Lauraceae_), and members of the _Mytraceae_ family, which generally range in height from 2 to 8 m [28]. Additionally, the site features fens, epiphytes, mosses, and lichens that cover the ground and tree trunks. The predominant soil type at the site is _Distrophic Red-Yellow Podzolic_.  
The climate classification of the experimental site is categorized as Cfa according to the Koppen classification system [29], which is defined as temperate humid with a hot summer. The mean annual air temperature is 17.4 \({}^{\circ}\)C, with the coldest month being July at 12.9 \({}^{\circ}\)C and the hottest month January at 21.2 \({}^{\circ}\)C. Precipitation is well distributed throughout the year, totaling 1667.7 mm yr\({}^{-1}\), without a well-defined dry season. The minimum monthly precipitation occurs in August with 86.2 mm month\({}^{-1}\) and the maximum in October with 186.1 mm month\({}^{-1}\), according to the Climatic Normals for 1991-2020 from the Brazilian National Meteorological Service (INMET), available at [[https://portal.inmet.gov.br/normais](https://portal.inmet.gov.br/normais)]([https://portal.inmet.gov.br/normais](https://portal.inmet.gov.br/normais)) (accessed on 22 April 2024).  
The leaf area index (LAI) at the experimental site was obtained from the MOD15A2 product of the MODIS (Moderate Resolution Imaging Spectroradiometer). The composite images of the MOD15A2 product offer a temporal resolution of eight days and a spatial resolution of 1 km, maintained within the range of 0.4 to 0.7 nm of the electromagnetic spectrum [30].
# Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data +
## 5 Experiments
### Impact of \(T\) in Model Performance  
The results related to predictions that cover a longer time frame (different values of \(T\)) are displayed in Table 5 for a fixed window size \(W=50\).  
The table reveals that the metrics exhibit improvement as the predictive horizon expands. This trend is expected since a larger value of \(T\) leads to more general predictions, with the focus being on detecting the presence of an attack in the upcoming \(T\) packets, rather than predicting the label of a particular packet. The most significant increase is observed between \(T=10\) and \(T=20\), where the F1 score improves by 6%, while precision and recall increase by 3% and 9%, respectively. As the value of \(T\) increases, the improvements in the metrics diminish, with only a 2% enhancement observed between the subsequent \(T\) values. We also display the label distributions for each of these cases. It is evident that with an increase in the value of \(T\), the model achieves greater performance, as one would anticipate due to the higher probability of an attack occurring within a larger window frame. Nevertheless, anticipating an attack when using a larger value for \(T\) yields reduced information and results in sparser identification of attacks.
# Data-Driven Construction of User Utility Functions from Radio Connection Traces in LTE
## 5 Performance Assessment  
In this section, the above-described method to estimate QoS thresholds on a service basis is tested with a set of radio connection traces taken from a live LTE network. For clarity, the analysis set-up is first explained and results are presented later. Finally, implementation issues are discussed.
# Cyber Resilience Limitations in Space Systems Design Process: Insights from Space Designers
## 1 Introduction
### Motivation of Present Study  
Space engineering faces unique difficulties due to its complex, interconnected nature, coupled with rapidly evolving cyber threats due to continuous innovation and limited situational awareness [33; 34]. Despite the growing significance of this topic, we found limited research conducted on the challenges faced by space system designers in incorporating cyber resilience decisions into their engineering processes.  
The results of this study highlight several key differentiators in the development of cyber resilience for space systems compared to other critical infrastructures. These unique challenges stem from the specific operational environment, engineering constraints, and strategic importance of space-based assets. Understanding these differences is essential for the development of effective cyber resilience frameworks tailored to the space sector. Therefore, this study aims to provide a comprehensive understanding of the challenges and limitations faced by space designers in engineering cyber resilience for space systems.  
We conducted interviews with industry professionals from Australia, the USA, and the UK. We aimed to focus on their understanding of cyber resilience concepts, the engineering frameworks they employ, the challenges they encounter, and their perspectives on a novelframework that could help incorporate cyber resilience into the design and development phases. The interview questions were intended to assess gaps in the current engineering processes and suggest a new novel framework for future study.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 4 ViMindXAI: A Scalable and Cognitive AI Platform for VBDA
### Deployment Considerations and Solutions
#### 4.4.1 Practical Deployment Challenges  
Deployment of scalable video analytics systems such as ViMindXAI in real-world environments faces multiple operational and architectural challenges. These span across edge-cloud orchestration, FL, infrastructure compatibility, and data platform migration.  
One of the most critical challenges is edge-cloud orchestration, where system responsiveness depends on minimizing inference latency while balancing computational load between edge devices and cloud servers. Application domains like surveillance, healthcare, and intelligent transportation operate under highly dynamic conditions, varying network bandwidth, heterogeneous device capabilities, and fluctuating workloads [108,117]. This necessitates the use of adaptive scheduling, resilient failover strategies, and real-time resource optimization mechanisms to maintain performance under variable constraints.  
A second major issue is synchronization in FL, while FL is a privacy-preserving paradigm that avoids centralized raw data sharing, it introduces significant communication overhead, especially in multi-node environments. Additionally, the presence of non-IID (non-independent and identically distributed) data across devices leads to biased local model updates and degraded convergence [107,113]. Effective deployment requires asynchronous aggregation, model pruning, and gradient compression techniques to reduce communication bottlenecks and ensure scalable training.  
Another challenge lies in resource and bandwidth limitations at the edge. Most edge devices are not equipped to execute large-scale transformer-based models or multimodal inference in real-time. Moreover, streaming high-resolution video data imposes heavy pressure on upstream and inter-device bandwidth [7,112]. Addressing this requires careful model optimization, such as using quantized or distilled versions of core models, efficient codec settings, and adaptive frame skipping strategies.  
Finally, the shift to modern data infrastructures introduces migration complexities toward Data Lakehouse architectures. Technologies like Delta Lake and Apache Iceberg require rethinking legacy ingestion pipelines, adopting new metadata strategies, and ensuring backward compatibility with existing BI and AI workflows. These changes, while beneficial in the long run, demand strong governance, incremental rollout, and organizational readiness for transformation.  
Together, these deployment challenges highlight the importance of robust system design that accommodates dynamic environments, privacy constraints, and evolving data infrastructure, while ensuring the scalability and resilience needed for mission-critical video analytics systems.
# Detecting Fake Reviews in Google Maps--A Case Study
## 3 Creating a Dataset of Google Maps Reviews
### Challenges Faced When Crawling Google Maps Data  
During the collection of the data from Google Maps, we faced several challenges. Some were caused by the fact that the scraping spanned several weeks since the crawler was continuously developed to handle various types of Google Maps places. Below, we present some of the major problems in detail.  
**Changes to Google Maps HTML markers.** Surprisingly, we noticed that during the period we were crawling the data, Google Maps HTML class names changed from simple names, such as _review-place-name_, to non-intuitive ones, such as _d4r55 YJx2d_. These names have changed a couple of times since, and each time they forced us to update the crawler code and restart scraping. At the end of the scraping process, 35 distinct classes were observed.  
**Lack of access to precise dates.** Every review on Google Maps is marked by its creation date. The problem lies in the presentation of this, as Google Maps shows only relative dates, with progressively worse accuracy. The older a review was, the less accurate was the time we could scrape. Because of that, newer reviews have much better accuracy than older ones. We decided to estimate the original date of review creation by subtracting the amount of time given by Google Maps from the date of scraping. However, we are aware that the date of older reviews are stored with large error margins, as displayed in Table 2.  
**Rebranding of reviewed places.** During the scraping, the line of supermarkets \"Tesco\" was bought by Salling Group S/A, and therefore, the corresponding Google Maps places were renamed to \"Netto\". We decided to merge the reviews from old Tesco stores with the reviews of the corresponding new Netto stores.  
**Changes in accessibility of accounts.** During our initial scraping, we gathered data on every account that we found. Among those data was information on whether the account was private or not--private accounts cannot be properly accessed, so we were only able to collect the account's name, id, and the fact that it was private. During our research and refinement of the database, we noticed that we could not access data from a lot of accounts that once were public. Interestingly, most of them were the same accounts that we labeled as fake. We realized that most probably Google banned those accounts. Fortunately, this change came late into our research and did not impact the collection of data; it only made it harder to validate such accounts.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Timestamp of Review** & **Error Margin** \\ \hline
1–59 min ago & 1 min \\
1–23 h ago & 1 h \\
1–6 days ago & 1 day \\
1–4 weeks ago & 1 week \\
1–11 months ago & 1 month \\
1–X years ago & 1 year \\ \hline \hline \end{tabular}
\end{table}
Table 2: Precision of date and time information in Google Maps.
# Advanced Optimization Techniques for Federated Learning on Non-IID Data
## 2 Background  
### Federated Learning  
Federated Learning (FL) is a decentralized machine learning approach that allows deep learning algorithms to be trained collaboratively across multiple devices or nodes without requiring data to be transferred to a central server. Instead, the training occurs locally on each device using its own data, which remains on the device, thus preserving privacy. The updates to the model parameters (such as weights) are then shared with a central server, which aggregates these updates to form a common, improved model that is sent back to the devices for further training [2].  
In this process, each participating device performs local training based on its own data and then exchanges only the updated model parameters, such as weights and biases, with a central server. The server synthesizes the updates from all devices to create an improved global model, which is then redistributed to the devices for another round of training. This iterative process continues until the model reaches the desired level of performance. The key advantage of FL is that it enables the training of a machine learning model without ever moving raw data from the devices, making it a crucial method in scenarios where data privacy is paramount.  
Federated Learning (FL) emerges as an innovative method that offers an advanced alternative to traditional centralized and distributed machine learning. Unlike centralized ML, where data are uploaded to a central server, exposing privacy risks, FL keeps data on devices, reducing the chances of data breaches. While distributed ML improves scalability by independently training models across participants, it still faces issues with synchronization and security during data transmission. FL enhances these models by maintaining data privacy and enabling collaborative model training, where only model updates are exchanged, ensuring more efficient communication and improved security [7].  
This method is particularly important in domains such as autonomous vehicles [8], traffic prediction and monitoring [9; 10], healthcare [11; 12; 13], telecommunications [14; 15], the Internet of Things (IoT), smart cities [16; 17; 18], industrial management [19; 20; 21], blockchain [22; 23; 24], and medical artificial intelligence. In these fields, data privacy is critical, and the ability to retain data on devices while still participating in collaborative learning provides a significant advantage [25; 26].
# Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array
## 4 DoA Estimation  
Five DoA estimation methods are presented in this section. MUSIC, PWD, WSF, and both deterministic (D) and stochastic (S) ML techniques were adapted in the analysis of signals using an SAA for the first time in this work. WSF is the large sample approximation of the SML technique, as mentioned earlier.  
There exist major differences between WSF, MUSIC, PWD, and ML techniques. Apart from the fact that PWD and MUSIC are not ML techniques, an advantage is that in WSF and ML, one can freely choose both the noise and signal model. In MUSIC, PWD, and other related techniques, we have to look for \(U\) maxima from a spatial spectrum of \(D\) size, while in maximum likelihood techniques, we only look for a single minimum (or equivalent maximum) from an estimation function of \(U\times D\) size. It then emerges that ML techniques suffer from the _dimensionality curse_ very quickly as \(U\) rises. An extensive search in this greater-dimensional space is computationally forbidden, but nonlinear optimization algorithms are often used to evaluate the minimum within a considerable period. The nonlinear optimization algorithms for WSF and ML techniques are presented in Section 5.1.  
Two scenarios are of interest: analysis of signals in a single frequency and wideband analysis of signals. In the case of wideband analysis, a time-domain smoothing algorithm is applied, as in [26; 35], which is briefly reviewed in this section. In narrowband analysis, the smoothing algorithm does not provide any advantages, because the signals are coherent. Hence, analysis is conducted for spherical harmonic coefficients in the frequency domain.
###### Abstract  
This paper addresses the problem of voltage restoration and reactive power sharing of inverter-based distributed generations (DGs) in an islanded microgrid subject to denial-of-service (DoS) attacks. Note that DoS attacks may block information exchange among DGs by jamming the communication network in the secondary control level of a microgrid. A two-layer distributed secondary control framework is presented, in which a state observer employing the multiagent system (MAS)-based ternary self-triggered control is implemented for discovering the average information of voltage and reactive power in a fully distributed manner while highly reducing communication burden than that the periodic communication way. The compensation for the reference signal to the primary control is acquired according to the average estimates to achieve voltage restoration while properly sharing reactive power among DGs. An improved ternary self-triggered control strategy integrating an acknowledgment (ACK)-based monitoring mechanism is established, where DoS attacks are modeled by repeated cycles of jamming and sleeping. A new triggering condition is developed to guarantee the successful information exchange between DGs when the sleep period of DoS attacks is detected. Using the Lyapunov approach, it is proved that the proposed algorithm allows agents to reach consensus regardless of the frequency of the DoS attacks, which maintains the accurate estimation of average information and the implementation of the secondary control objectives. The performance of the proposed control scheme is evaluated under simulation and experimental conditions. The results show that the proposed secondary control scheme can highly reduce the inter-agent communication as well as improve the robustness of the system to resist DoS attacks.  
distributed self-triggered control; voltage restoration; reactive power sharing; denial-of-service attacks; islanded microgrid +
Footnote †: journal: Applied Soft. 2020, 10, 3780
# Intelligent Fault Diagnosis of Unbalanced Samples Using Optimized Generative Adversarial Network
## 2 Methods
### Proposed Method
#### 2.2.2 Data Preprocessing  
To obtain rich features of mechanical fault conditions, the 2D feature images are produced from the 1D vibration signals with wavelet transform method [11]. Then, image recognition methods can be used to identify the fault data. Continuous wavelet transform can be expressed as follows:  
\[CWT(\gamma,\tau)=\frac{1}{\sqrt{\gamma}}\int f(t)\varphi(\frac{t-\tau}{\gamma}) \mathrm{d}t \tag{7}\]  
where \(f(t)\) is the raw signal, \(\gamma\) is scale factor, \(\tau\) is translation factor, and \(\varphi(\cdot)\) is the wavelet basis function.
## 3 Results and Discussion
### Raman Spectroscopy  
Figure 3 presents the Raman spectra of our SiCN ceramics without and with sulfur. All samples show characteristic D band and G band peaks of carbon at around 1345 cm\({}^{-1}\) and 1583 cm\({}^{-1}\), as well as a broad peak between 2500 cm\({}^{-1}\) and 3000 cm\({}^{-1}\), corresponding to the 2D and D + G band of carbon, respectively. The sulfur-free SiCN ceramics show decreased ratio of I (A\({}_{\rm D}\))/I (A\({}_{\rm G}\)) and increased crystallite size (L\({}_{\rm a}\)), with increasing pyrolysis temperature (Figure 3a and Table 1) [27]. The I (A\({}_{\rm D}\))/I (A\({}_{\rm G}\)) ratio of all samples indicate that the carbon inside the SiCN ceramics exhibits amorphous structure originating from disordered sp\({}^{2}\)-hybridized carbon. After introducing sulfur into the SiCN ceramics, characteristic bands of sulfur are also detected in the Raman spectra besides that of the D, G, 2D, and D + G bands of carbon (Figure 3b).
## 3 Results
### Effect of Different Additives on the Aerobic Stability of Whole-Plant Millet Silage  
During 7 days of aerobic exposure, there was no significant temperature increase in any of the experimental groups, with aerobic stability exceeding 168 h. However, post-aerobic stability testing revealed an increase in pH values across all groups, although all additive groups remained significantly lower than the CK group (\(p<0.05\)). Additionally, the LA and AA contents in all treatment groups, except the S group, decreased compared to their levels upon opening, with the most notable decrease observed in the LP group (\(p<0.01\)) (Table 6). \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Items** & **Additives** & **g/kg DM** & **NDF** & **ADF** & **CP** & **WSC** \\  & & & **(g/kg DM)** & **g/kg DM** & **(g/kg DM)** & **(g/kg DM)** \\ \hline Pre-ensiling & M & 388.9 \(\pm\) 9.75 & 621 \(\pm\) 12.8 & 516 \(\pm\) 9.20 & 68.8 \(\pm\) 1.11 & 21.3 \(\pm\) 1.98 \\ \hline \multirow{4}{*}{At silo opening} & CK & 373 \(\pm\) 0.06 b** & 586 \(\pm\) 6.35 & 503 \(\pm\) 4.89 a & 70.2 \(\pm\) 1.07 * & 33.8 \(\pm\) 8.65 b \\  & LP & 376 \(\pm\) 0.70 ab & 576 \(\pm\) 2.73 & 496 \(\pm\) 5.25 ab & 70.0 \(\pm\) 0.70 & 46.3 \(\pm\) 2.34 b** \\  & S & 385 \(\pm\) 0.01 ** & 536 \(\pm\) 19.4 & 458 \(\pm\) 15.3 b & 69.8 \(\pm\) 1.28 & 43.6 \(\pm\) 3.88 b* \\  & MIX & 383 \(\pm\) 0.80 ab** & 550 \(\pm\) 25.6 & 480 \(\pm\) 20.8 ab & 69.4 \(\pm\) 0.58 & 69.4 \(\pm\) 5.80 a** \\ \hline \multirow{4}{*}{After 7 d aerobic stability test} & CK & 442 \(\pm\) 0.10 & 582 \(\pm\) 8.20 A & 497 \(\pm\) 9.20 A & 66.4 \(\pm\) 0.47 & 25.0 \(\pm\) 4.07 B \\  & LP & 439 \(\pm\) 0.22 & 531 \(\pm\) 21.9 AB & 456 \(\pm\) 18.8 AB & 68.4 \(\pm\) 0.49 & 25.8 \(\pm\) 2.68 B \\ \cline{1-1}  & S & 446 \(\pm\) 0.08 & 524 \(\pm\) 24.1 B & 453 \(\pm\) 19.3 AB & 66.9 \(\pm\) 2.18 & 27.5 \(\pm\) 1.41 B \\ \cline{1-1}  & MIX & 449 \(\pm\) 0.01 & 513 \(\pm\) 5.15 B & 435 \(\pm\) 10.3 B & 61.5 \(\pm\) 4.38 & 37.1 \(\pm\) 1.31 A \\ \hline \hline \end{tabular} Note: CK, LP, S, and MIX represent control, _Lactiplantiobacillus plantarum_, sucrose, combination of _Lactiplantiobacillus plantarum_ and sucrose addition treatments, respectively; M represents raw materials. Different lowercase letters indicate significant differences (\(p<0.05\)) between different treatments at silo opening; different uppercase letters indicate significant differences (\(p<0.05\)) between different treatments after the aerobic stability test. * Indicates significant differences (\(p<0.05\)) between different treatments after the aerobic stability test. * Indicates significant difference between unscaling and aerobic stability experiments in the same treatment (\(p<0.05\)), ** indicates that the difference between the at silo opening and after aerobic stability test in the same treatment. * is extremely significant (\(p<0.01\)). DM: dry matter; CP: crude protein; WSC: water soluble carbohydrate; NDEF: neutral detergent fiber; ADF: acid detergent fiber.  
\end{table} 
Table 4: Effects of different additives on nutrients at silo opening and after aerobic stability test of whole-plant millet silage.
## 4 System Implementation  
This study established a WSN-based monitoring system for military storage facilities to real-time monitor the storage environment parameters of high-value weapons to ensure item safety, with the system implementation architecture being as shown in Figure 10. We set up a military warehouse WSN monitoring system server in the National Defense University Research Building (located in Taoyuan City), and the remote monitoring area was mainly built in specific mountain weapons warehouses at the National Zhongshan Institute of Science and Technology. This area is relatively remote and sparsely populated, so communication network signals cannot be fully covered. High-precision weapons in the warehouse are installed in well-sealed oil and gas tanks to ensure safe storage. We use ZigBee transmission technology to build a wireless sensing network. Sensor 1 is placed at the entrance of the warehouse to collect indoor environmental parameters; Sensors 2-5 are placed in the oil and gas storage tank, and can collect the temperature, humidity, pressure and power of the sensor battery of the storage tanks, transmitting the information to the monitoring server via GSM or Ethernet by gateway to ensure the safety of the sensitive weapons.  
Figure 9: The ’receive’ function of UART communication.