esa-sceva/satcom-chunk-collection huggingface dataset:
# Design, Implementation, and Field Testing
## 5 Field Study
### Analysis of the Logged Data  
On Day 35 of the field study, we modified PACTS with the lease-based mechanism after learning the usability issues with our system. The raw data on the total registered duration per room per day in the four rooms are shown in Figure 8. The summary of the data collected is provided in Table 1. As can be seen, only about 888 min (or 13 h) of data are logged for all four rooms. The daily average of time monitored by PACTS is between 1 to 5 min. It is apparent that only a very small fraction of the work done by nursing assistants were captured by PACTS. Hence, we cannot draw longitudinal trend out of the data we have collected. Nevertheless, the mean daily registered time per room was increased by more than five fold after the enabling of the lease-based mechanism.
## 3 Stationary Localization Algorithms
### Distributed Localization Algorithms
#### 3.2.1 Node Discovery and Localization Protocol (NDLP)  
In [8,9], the authors proposed a Node Discovery and Localization Protocol (NDLP) to manage sub-sea localization. As shown in Figure 4, NDLP starts with one seed node (primary seed \(S_{1}\)) with known position. The primary seed node is capable of determining the relative positions of neighboring nodes. A second seed node, \(S_{2}\) is then chosen by \(S_{1}\). \(S_{2}\) is the most distant node within the communication range of \(S_{1}\). The advantage of choosing the farthest node as the second seed node is that a larger area can be covered more quickly. A third seed node \(S_{3}\) is chosen from those nodes that lie in both communication ranges of \(S_{1}\) and \(S_{2}\), and has the maximum summation distance from \(S_{1}\) and \(S_{2}\). Each node in the overlapping region ( the grey area in Figure 4) is able to calculate the relative location using a simple triangulation technique. The nodes in the cross-hatched region in Figure 4 can only obtain two distance measures from seed nodes. In order to localize the nodes in the cross-hatched region, a fourth seed node is selected based on four algorithms, Farthest/Farthest Algorithm, Farthest/Nearest Algorithm, Nearest/Farthest Algorithm and Nearest/Nearest Algorithm.  
NDLP is an anchor-free and GPS-less algorithm. Large scale of unknown nodes can be localized by continuously selecting seed nodes. However, NDLP has some serious problems. First, the node discovery phase needs much communication overhead. Each node participates in massage exchange to select seed nodes. Hence, energy consumption of node discovery is high. Second, the seed node's selection process takes long time, hence localization time is long. Moreover, unknown nodes' relative coordinates calculated based on the seed nodes' positions are not accurate. In some areas, if nodes are  
Figure 3: The Asymmetric Round-Trip Scheme.  
much sparsely deployed or become sparser due to some movements, then it is possible that very few or even no node can be selected as seed node. It is shown that NDLP is not suitable for sparse and mobile UWSNs. Furthermore, in mobile UWSNs, repeating the node discovery each time the topology changes is unaffordable.
## 5 Application of Sys-IMF for Decision Support in Modular Product Family Design
### Use Case  
In the application example, a product family of medium-voltage electric motors is modularized. The industry is characterized by a strong correlation between order volumes and oil and gas prices, which means that day-to-day business is very volatile. In times of low order density, highly individualized orders are therefore also accepted, which over the years has led to a historically grown product portfolio with very high internal variety. The number of units sold is around 2000 product variants per year and the company's strategy is equal parts Engineer-to-order (EtO) and Make-to-order (MtO).  
In order to reduce internal product variety, a building kit (Baukasten) for the product family of medium-voltage electric motors is being developed within the scope of a modularization project. The aim here is to generate the maximum possible variety of offers using a specific set of components and to keep the internal effort as low as possible by means of a predefined configuration logic (for an overview of the various modular product structuring strategies, see [4]). In the context of the application, for the sake of clarity, the bearing  
Figure 10: Illustration of the approach to support the evaluation of alternative modular product architecture concepts.  
assembly of the motors is considered, and three alternative modular product architecture concepts are developed (Figure 11). The pictures of the alternatives are for illustration purposes only, while the figures represent real values.  
As can be seen in the diagram, the number of variants is greatly reduced by using a building kit as a product structuring strategy in all three concepts through standardization measures. The concept alternatives developed differ primarily in the varying technical implementations for the type and number of bearing arrangements. Concept 1 has about twice as many variant components as the other two concepts, 2 and 3. The last two alternatives have only a marginal difference in terms of technical implementation and component variance. For the knowledge-based and case-specific evaluation of the alternative product architecture concepts, the presented 2-phase approach is applied using the extended IMF.
# An Innovative Information Hiding Scheme Based on Block-Wise Pixel Reordering
## 2 Background of the Work  
Our novel scheme employs the principal idea of a vector quantization (VQ) codeword index reordering scheme [4] as the basic technique to embed and extract data. This section describes the data embedding and data extraction using the codeword index reordering.  
When there is a codebook, it is sorted first before embedding secret data. The data is embedded through the new indices in a stego codebook. Figure 1 indicates the flow of the codeword index reordering scheme including data embedding, data extraction, and image recovery. The stego codebook with the new ordered indices is then sent to receivers to extract the data.
# Developing an Intelligent Cellular Structure Design for a UAV Wireless Communication Topology
###### Abstract  
In the current digital era, where Unmanned Aerial Vehicles (UAVs), Artificial intelligence (AI), and Internet of Everything (IoE) can be well integrated, more global connectivity and automated solutions can be witnessed. This paper aims to develop an intelligent cellular structure design for a UAV wireless communication topology using an AI framework. The proposed AI framework includes Self Organizing Maps (SOMs) and an NN fitting tool that can be simulated using the Graphical User Interface (GUI) toolbox in MATLAB. The proposed framework is validated in a proof-of-concept scenario, where various parameters of link budget and cellular structure design have been tuned to achieve an efficient and optimized automatic design. The obtained results show high levels of adaptable wireless communication predictions without human intervention, which is a noticeable shift from existing work in the literature.  
unmanned aerial vehicles (UAVs); artificial intelligence (AI); cellular structure design; automatic design; optimization algorithms; internet of everything (IoE); wireless communications; graphical user interface (GUI) +
Footnote †: journal: _axions_  
detection, the Internet of Medical Things (IoMT), and virtual reality. These applications are still growing to connect billions of people and devices to the grid network [8].  
The rest of this paper is organized as a related study in Section 2. Section 3 shows the proposed UAV topology and simulation, followed by a proof-of-concept and discussion in Section 4. Then, the paper concludes in Section 5.
# Fast Deployment of a UWB-Based IPS for Emergency Response Operations
## 4 Implementation
### Hardware  
Both the anchor and the tag of the proposed UWB-based IPS consist of the same hardware elements:  
* The **communication module** is a Qorvo DWM1001-DEV module development board [65], which integrates a DWM1001C UWB transceiver module, a Bluetooth antenna, all RF circuitry, a Nordic Semiconductor nRF52832 SoC, and a motion sensor.
* The **processing module** is an Espressif ESP-WROOM-32s development board [66], which includes a dual-core MCU and a WiFi antenna.
* The **power supply** is provided by a 2600 mAh Li-Ion battery. The communication module is connected to the processing module through a UART interface, so that each new computed ranging is timely sent to the processing module. As for the power supply, the battery is directly connected to the communication module, from which energy is delivered to the rest of components. The whole set is laid on a 3D-printed support structure that enables easy mounting on a tripod or on a helmet (see Figure 7).  
For its part, the GNSS-RTK module consists of the following elements:  
* **Ardusimple simpleRTK2B budget board**[67]: a standalone board that allows one to evaluate dual-band GNSS-RTK technology. It is based on a u-blox ZED-F9P module and is fully compatible with Arduino and STM32 Nucleo platforms as a shield.
* **u-blox ANN-MB-00 Li/L2 multi-band antenna**[68]: a high-precision RTK multiband external GNSS antenna with 5 m cable and an SMA connector.
* **Ardusimple 4G NTRIP master**[67]: a radio module including cellular connectivity and the necessary software to connect the simpleRTK2B board to a GNSS real-time correction service. To validate the presented solution without deploying additional GNSS-RTK infrastructure, the NTRIP protocol [69] was used to receive corrections from the EUREF-IP service [70].
* **Raspberry Pi 4**: preprocessing unit of the whole GNSS-RTK module. All the elements are connected to the Raspberry Pi, which initiates all the processes and computes the final GNSS-RTK-based position. In addition, the module incorporates  
Figure 7: Designed UWB device (used either as an anchor or as a tag). (**a**) Front view. (**b**) Back view. (**c**) Mounted on a helmet.  
an external LED with 4 colors that identifies all the stages of the anchor self-location system. This indicator eases the deployment of the GNSS-RTK self-location system performed by FRs in the field. Figure 8 shows the GNSS-RTK module, identifying all the aforementioned components.
# Unusual Animal Behavior as a Possible Candidate of Earthquake Prediction
## 4 Recent Work on Dairy Cows' Milk Yield Changes  
Dairy cows' milk yield change is one particular branch of unusual animal behavior. There have been early works published on dairy cows' milk yield changes as in the following papers, [48; 49; 50; 51], and therein.  
Recently, our group has made a lot of important contributions. [PERSON] et al. (2014) [52] thoroughly elaborated the study of changes of dairy cows' milk yields in relation to EQs. They surveyed the unusual animal behavior of pets using a questionnaire, and additionally they explored how the dairy cows' milk yield varied before the EQ. Their finding was that there was a significant depletion in cows' milk yields 3-6 days before the disastrous 2011 Tohoku EQ, as a case study. [PERSON] et al. (2017) [53] have then extended their case study to statistical evaluations, and their statistical result revealed that the dairy cows' milk yield indicated a significant depletion again, but approximately three weeks before an EQ, using long-term observation data, and suggested a link with ULF radio emissions from the lithosphere. Also, they have obtained a promising probability gain of 6.8 based on the confusion matrix. This value seems to be sufficiently high for short-term EQ prediction purposes, even when compared with those of some promising candidates of electromagnetic effects [6]. Further, [PERSON] et al. (2016) [54] have investigated the correlation of cows' milk yields with seismicity and ULF magnetic field variations, and their conclusion is that these cows' milk yields show a depletion about 17-18 days before an EQ, and it was also found that ULF lithospheric radiation happens, at least, during the period of low milk yields of dairy cows. Finally, we can say from our previous work that the cows' milk yield change is acceptable as an EQ predictor, but its lead time is very variable at approximately 2-3 weeks.  
A recent good review paper on unusual animal behavior has been published by [PERSON] et al. (2023) [55], who indicated the weakness and deficits of papers published so far because of the short period of data presentation before going into any definite conclusion of the sensory mechanism of animals; however, they have suggested the importance of conventional foreshocks from the seismological sense.
## 2 Materials and Methods
### Methods  
#### 2.2.1 Standard Proctor Compaction Test  
Optimum conditions of soil (maximum dry density (MDD) and optimum moisture content (OMC)) are key considerations for assessing both the quality assurance and quality control of in-situ soil. With this regard, a standard proctor compaction test was used to determine the MDD and OMC of both soils before and after treatment with POFA-based geopolymer according to BS 1377-1990: Part 4. The test was carried out for all treated specimens immediately after mixing the soil with the geopolymer.
# Context-Aware Wireless Sensor Networks for Smart Building Energy Management System
## 4 Proposed EMS for SEEB
### WSN Network Packet Delivery Evaluation
#### 4.5.1 Throughput Evaluation  
We measured the throughput from each sensor node to the gateway for one minute using the XCT-U software in two different topologies: Full mesh topology and a cluster tree topology. Each sensor node sends packets of 20 bytes and waits for the ACK back from the receiving node. The data packet contains three pieces of information (temperature, motion, and humidity). Table 5 presents the results in Kbps, which are close to the values reported on the datasheet. As the distance from the sensors increases, the throughput decreases.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Gateway Device** & **CPU** & **RAM** &
**XBee** &
\begin{tabular}{c} Freescale \\ i.MX28 \\ \end{tabular} & 68 Mb & Embedded Linux & ✓ & ✓ & ✓ \\ \hline
**Raspberry-Pi** & ARM & 2 Gb & Raspbian & ✓ & ✓ & ✓ \\ \hline
**Arduino** & AVR & 2 Kb & On-chip Boot & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 4: Gateways hardware configuration.  
Figure 5: (**A**) Energy consumption of different sensor nodes configurations, (**B**) Energy consumption of XBee RF module under different operating modes.
## 3 Normalization Algorithms
### Pseudo\(-\)Invariant Polygon Features\(-\)Based Normalization
#### 3.3.1 Pseudo\(-\)Invariant Polygon Features with the Single Band (Polygon\(-\)Single)  
To avoid the influence of noncorresponding scatter points, the mean reflectance of selected areas was substituted for pixel reflectance in regression analyses. When fitting a single band, a much more precise equation is generated than that obtained from point features. Correlation coefficients are all greater than 0.9, except for band 3 (Figure 5).
# Verdet Constant of Magneto-Active Materials Developed for High-Power Faraday Devices
## 2 Characterization of the Verdet Constant as a Function of Wavelength and Temperature
### VIS-NIR Region (\(400<\lambda<1100\) nm)
Among these, Tb\({}_{2}\)O\({}_{3}\) holds the record Verdet constant (\(\sim\)3.5 times higher than TGG), followed by the Dy\({}_{2}\)O\({}_{3}\) (\(\sim\)2 times higher (at 633 nm wavelength)) and Ho\({}_{2}\)O\({}_{3}\) (\(\sim\)1.4 times higher (at 1064 nm wavelength)). The manufacturing process of the RE\({}_{2}\)O\({}_{3}\)-based ceramic materials still needs to be optimized before the ceramics will be obtained in a high optical grade and in larger dimensions. Nevertheless, these structures represent one of the most promising options for the large-aperture VIS-NIR FDs. The fluorides mentioned in the UV region, i.e., LiREF\({}_{4}\)[(59)], PrF\({}_{3}\) and CeF\({}_{3}\)[(61)] could be used as Faraday rotators in the VIS region as well. The latter one, CeF\({}_{3}\), has been extensively studied over the VIS-NIR region (\(400<\lambda<1100\) nm), since it exhibits higher FOM than TGG crystal [(134; 46; 135; 46)]. There are many more reports on promising magneto-active materials for the VIS-NIR region, which offer properties superior to the TGG crystal, most of them targeting on the enhancement of the Verdet constant. From the vast number of these materials, we may mention, e.g., NaTb(WO\({}_{4}\))\({}_{2}\)[136], TSLAG [81, 137], Na\({}_{2}\)Tb\({}_{4}\)(MoO\({}_{4}\))\({}_{7}\)[138], LiCaTb\({}_{5}\)(BO\({}_{3}\))\({}_{6}\)[139], RE:TGG (RE= Ce [140], Pr [141], Ho [142], Tm [143] or Dy [144]), TbVO\({}_{4}\)[145], Nd:TbVO\({}_{4}\)[146], Na\({}_{2}\)Ce(MoO\({}_{4}\))\({}_{2}\)[147], TCZ [148, 149], Tb\({}_{2}\)Hf\({}_{2}\)O\({}_{7}\)[150] or CeAlO\({}_{3}\)[151].
## 4 Discussion
### Use of SIF as a Water Stress Monitoring Tool  
Because of its mechanistic link to the photosynthetic apparatus, \(\epsilon_{760}\) is reactive to ambient stress conditions, being a combination of the soil water availability, the VPD and the PAR. These conditions change during the day. At dawn, when PAR approximates 0, a plant grows in a light-limited regime, regardless of its soil water status. Assuming cloud-free conditions, PAR and VPD follow a bell-shaped pattern, and the plant will switch to a water-limited regime once the PAR and VPD reach a certain level. The point at which this happens depends on the plant water status, and, therefore, also on the soil moisture as well as on the plant isohydricity. Figures 5-7 have shown that more intense water limitation results in a more expressed decrease in \(\epsilon_{760}\), making it an indicator for the instantaneous stress conditions. By linking instantaneous stress to environmental factors, measurements of the degree to which a plant is in a water-limited regime (\(\epsilon_{760}\)) provide sufficient information to correct crop growth models for drought stress [24]. This makes \(\epsilon_{760}\) an especially interesting indicator in the light of the upcoming FLUorescence Explorer (FLEX), as it will provide global maps of \(\epsilon_{760}\) with a spatial resolution of 300 m. Global-scale SIF from the TROPOMI sensor has already proven its value to classify ecosystems as water- or light-limited [41]. The drought-induced decrease in \(\epsilon_{760}\) is expected to allow monitoring the intensity of the water limitation.
### Our New Approach: REDPACK  
In our previous studies, the rPassGAN series outperformed PassGAN. Nevertheless, compared with Probabilistic Context-Free Grammar (PCFG) and other Markov models, such as the OMEN [1], rPassGAN sometimes cracked fewer passwords. In this study, we propose a model to maximize the password cracking performance using a small number of guessing. With our model, we can selectively collect more realistic password candidates to improve the efficiency of password cracking. Then, we can make an effective cracking dictionary with these collected candidates. If password candidate dictionaries from various models are available, prior knowledge of the password's properties and structure is not required for selecting a more realistic password candidate. The relativistic average standard GAN [13]'s discriminator operates as an estimator, which evaluates how realistic input passwords are from the various pre-generated candidates. Although a GAN generator is typically used, our model introduces a novel way of using the GAN's discriminator in password guessing. We refer to this model as a relativistic discriminator for password candidates effective pack(REDPACK). We demonstrate that our model can compete favorably with the Hashcat transformation rules (rockyou-30000, dive), PCFG [14], and OMEN [2]. Our new approach's contributions are as follows:  
* GAN is a generative deep learning model. Generally, the generator of a GAN is used for producing fake samples. However, the trained discriminator of the GAN is not utilized for the inference. The discriminator is only used in the training phase for training the generator. Our first contribution is that we propose how to utilize the discriminator of the GAN not for training the generator but for enhancing password cracking performance. To the best of our knowledge, our approach is the first model that has been proposed in the field of password cracking. The discriminator of the GAN estimates how realistic password candidates are. Then, we use these estimates as criteria to select the best candidates among the multiple dictionaries.
* If there are multiple pre-generated password candidate dictionaries, REDPACK enables us to make a more effective and efficient password candidate dictionary without any background knowledge about the pre-generated dictionaries. If we provide this efficient and effective password candidate dictionary to a password strength estimator like Zxcvbn, we are able to make the criteria of the password strength estimator stricter [7; 8; 12].
* Our last contribution is the building of a custom ruleset for REDPACK. This custom ruleset helps the password candidates from REDPACK maximize its cracking performance.
## 4 Discussions
### Evolution, Future Trends, and Open Research Areas in Blockchain-Based Data Storage
#### 4.4.5 On-Chain AI and Blockchain Integration
4.6 The Road Ahead for Blockchain Storage: Emerging Paradigms in Edge Computing and the Internet of Everything Era  
The evolution of data storage methods within blockchain systems is increasingly influenced by the convergence of the Internet of Everything (IoE) and edge computing technologies. As the volume of connected devices expands in sectors such as smart cities, healthcare, autonomous systems, and industrial automation, the demand for real-time data processing, decentralized storage, and secure device-to-device communication is expected to intensify [182; 183]. Edge computing enables data to be processed and temporarily stored closer to its source, reducing latency and alleviating the burden on centralized servers. When integrated with blockchain technology, edge computing not only strengthens data security and integrity through decentralized consensus but also facilitates localized decision making [184]. This hybrid approach is particularly critical for IoE ecosystems, where billions of devices simultaneously generate and process sensitive data. Future solutions are anticipated to adopt Edge-integrated Hybrid Storage Models, combining the following:  
* On-chain metadata verification for ensuring immutability;
* Off-chain distributed storage systems (e.g., IPFS) for scalable data management;
* Edge storage and processing to enhance performance and minimize response times. Recent studies suggest that this three-layered hybrid model will become the dominant architecture for data storage and security in future IoE environments. Such systems can offer low-latency responses, decentralized control, and improved data privacy, particularly in resource-constrained edge settings. However, interoperability challenges, device-level vulnerabilities, and data synchronization issues remain key obstacles requiring further research [182; 183; 184]. The ongoing convergence of blockchain and edge computing is expected to follow a structured development path:  
* 2024-2025+: Increased adoption of blockchain-integrated edge computing solutions, especially in smart city infrastructures, industrial automation, and healthcare data systems [194];
* 2026-2028+: Standardization of hybrid storage architectures, where on-chain metadata verification is combined with off-chain and edge computing storage models for enhanced security and scalability [182];
* 2028-2030+: Full deployment of autonomous blockchain-based edge systems, enabling decentralized AI-driven decision making in IoE environments, reducing dependency on centralized cloud infrastructures [195]. Despite these advancements, scalability constraints, device-level security vulnerabilities, and interoperability challenges remain critical hurdles that require further research and industry collaboration [182; 183; 184; 194; 195].