esa-sceva/satcom-chunk-collection huggingface dataset:
# Soil Erosion Estimates in Arid Region: A Case Study of the Koutine Catchment, Southeastern Tunisia
## 2 Study Site  
The study site (Wadi Koutine) is located in the Jeffara area, northeast of Medenine city, which is part of the southeastern Tunisia region (Figure 1). The study site covers an area of 279 km\({}^{2}\), drained by Wadi Koutine, which starts from the mountain range of Beni Khedache and reaches the city of Koutine. The approximate coordinates of the central point of the Koutine catchment are 33\({}^{\circ}\)16\({}^{\prime}\) N and 10\({}^{\circ}\)08\({}^{\prime}\) E. The highest elevation of the Koutine catchment is about 650 m that corresponds to the peak of mountain Jbel Moggar. The climate in the study site is arid. The coldest months are December-February. June-August is the warmest period of the year, during which the temperatures could reach as high as 48 \({}^{\circ}\)C. The temperature is affected by both the proximity to the sea and the altitude. The rainfall is characterized by low mean annual values, high spatiotemporal variability and torrential features [32]. Annually, the Koutine catchment receives mean rainfall varying between 234 and 162 mm in the mountains and eastern parts, respectively [33].  
Figure 1: The study site.  
The annual average runoff of Wadi Koutine has been estimated to be 8.74 Mm\({}^{3}\)/yr [34].  
Due to the low annual rainfall rates and their spatiotemporal variability, together with the relatively high elevation of the Koutine catchment, different surface water harvesting techniques were implemented (i.e., _Jessour_ and _Tabia_). The Jessour are mainly constructed in the mountainous areas of the catchment. The Jessour is an ancient water-harvesting technique that is constructed in the inter-mountain and hill watercourses to intercept runoff and sediments [35]. This hydraulic unit is composed of three main components: a dike in the form of a small earth embankment with a spillway made of stones, a terrace that represents the cropping area and an impluvium, which is the runoff catchment area [36]. Tabias are basically situated in the middle of the catchment on moderate slopes. A Tabia is formed by a principal embankment of 50 to 150 m long situated along the contour with lateral bunds of about 30 m long at the ends. The Tabia gains its water directly from its impluvium or by the diversion of Wadi runoff. Water is captured until it reaches a height of 20 to 30 cm, after which it is diverted, either by a spillway or at the upper ends of the lateral bunds [37].  
In the areas behind Tabia and Jessour, water-harvesting techniques have high effectiveness in combatting water erosion. However, in the rangelands, current conservation measures have low effectiveness, and the rate of land degradation is increasing [38]. The Koutine catchment is used for cropland, including the production of cereals and grazing in various types of rangeland. The land use is dominated by sparsely covered, degraded steppes. Cropped sites, mainly for growing olives, are found on terraces behind water-harvesting structures [35].
# Bluetooth Low Power Modes Applied to the Data Transportation Network in Home Automation Systems
## 3 Test Bench  
The goal of this research is to study the latency on multi-hop communications, including as many gateways as possible in a scatternet. Then, the first step towards the goal consisted of the implementation of the proprietary nodes with Bluetooth connectivity. Next, the wireless test bench was deployed so that the test bench consisted of a multi-hop wireless network of proprietary nodes based on Bluetooth technology.  
One of the implemented proprietary nodes can be seen in Figure 2. The nodes included the WT-11 chipset of Bluegiga [36], general-purpose input and output components, and some test pins. The chipset included a firmware developed by Cambridge Silicon Radio [37], named BlueCore4 [38], that eases the low power modes defined in the Bluetooth specifications and analyzed in this manuscript, i.e., park and sniff. In addition to the firmware, a proprietary middleware was designed using BlueLab [39], the software tool to develop embedded software given by the manufacturer of the chipset [37]. BlueLab eases the programming of the nodes at the HCI level, the lowest level that any proprietary middleware can access within the Bluetooth protocol stack. The middleware included the processing tasks, which were limited to the most basic operations to restrict their influence in the latency. Moreover, the middleware integrated processes to initialize the communications, present the operation status and route data to other nodes.  
This research considers the deployments of multi-hop networks based on Bluetooth communication standard inside homes and buildings. An example of such deployments can be seen in Figure 3. The grey circles represent the network access nodes deployed. Some sensors (smoke, temperature, etc.) would be attached to the nodes, and the network could be accessed by wireless devices, such as mobile phones. Even though the electricity supply is usually at hand in such indoor deployments, a battery is included in each node to overcome any failure in the electricity supply that might happen.  
Every pair of nodes in the test bench built a piconet. In addition, the interconnection of piconets built a multi-hop network with linear topology, which is one of the simplest topologies available. However, the linear topology is the most suitable one to study the performance of the desired amount of Bluetooth gateways with the least amount of nodes.  
Here, every intermediate node operates as gateway and relays the data gathered from other sensor nodes (not depicted in Figure 3) to the DS. The DS stores all the information available in the system and shares it with all the devices through the multi-hop network.  
On the other hand, there is a network administrator who accesses the data in two ways. They can gain admission to the network through a mobile phone with Bluetooth connectivity or through a computer connected to the node labeled as number one.
## 1 Introduction  
The recent advances in solid phase synthesis, catalysis or different types of separation have raised great interest in the area of cross-linked polymeric supports at both the academic and industrial level. Introduced fifty years ago, this type of polymers was initially produced in the form of particles [1] and is currently used for filling columns in processes that are carried out under continuous flow, as in different chromatography processes. Several applications of these supports involve polymers with low cross-linked degree (expandible or homogeneous polymers), which require a previous swelling process to became porous. When these polymers swell in a good solvent, they allow the access of all reagents to all material sites although their use becomes limited. On the other hand, the changes in the type of solvent used usually damage the properties of the material.  
Contrary to the fact that the homogeneous polymeric networks need to swell to reach a porous state, the macroporous polymers (heterogeneous network) has a porous structure formed during preparation and maintained in any solvent or in dry state. The inner structure consists of aggregates of microglobules of interconnected polymers forming pores and their rigidity results from their high degree of cross-linking.  
Generally, these materials are prepared from a suspension polymerization reaction in particle form [2], in which the polymerization mixture should always include a vinylic monomer as cross-linker (it must have at least two double bonds) and an inert solvent or a mix of inert solvents (called porogen, porogenic mixture or pore forming agent). The presence of the inert solvent is crucial for the preparation of macroporous polymers. Hence, different classes of solvents are used: those that solvate the polymeric chains in formation (good solvents) [3]; those that do not solvate the polymeric chains in formation (bad solvents) [3]; supercritic carbon dioxide [4,5]; lineal polymers [6]; or a mix of good/bad solvents [7].  
In the last years, macroporous monolithic materials in rod form have been introduced as a new and useful generation of macroporous polymers prepared in a more simple way respect to suspension, from a homogenous mixture formed by vinylic/divinylic monomers and the inert or a mix of inert solvents, into a mold. The main objective of this review is to report important contributions of several groups working in the preparation of these macroporous monoliths and their modification for specific purposes.
## 7 Conclusions  
Bearing fault detection is one of the important issues in the industrial field. In order to improve the reliability of the equipment, it is necessary to detect the status of the bearings in a timely manner. It is very important to choose the appropriate bearing detection technology for different scenarios. Vibration analysis, acoustic detection, thermal imaging, electromagnetic induction, and methods based on vision, machine learning, and depth can be used to detect bearing faults. These methods have their own characteristics and perform differently in terms of detection efficiency, cost, accuracy, real-time performance, and robustness. In summarizing these methods, it can be seen that research on rolling bearings is progressing towards intelligent detection, integration of advanced sensor technology and data processing technology, multimodal fusion, and real-time detection. In the future, new technologies such as quantum sensors, edge computing, Internet of Things technologies, multimodal large models, quantum computing, and cloud computing can also be explored.  
Conceptualization, B.S.; Writing--Original Draft Preparation, B.S., Z.S. and P.S.; Writing--Review and Editing, F.W.; Supervision, H.S. and X.S.; Project Administration, J.L.; Funding Acquisition, B.S. All authors have read and agreed to the published version of the manuscript.  
Funded by China Aero Engine Group Innovation Fund, funding number is No. CXPT-2023-039, 2024. Funded by Hei longjiang Province Youth Science and Technology Talent Support Project, funding number is No. 2024 QNTJ007. Funded by Technical basic scientific research project, funding number is No. JSZI2020204A002.  
Author [PERSON] and [PERSON] were employed by the company Aero Engine Corporation of China Harbin Bearing Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# A Nested U-Shaped Residual Codec Network for Strip Steel Defect Detection
## 3 Methodology  
In this section, we proposed a nested U-shaped residual codec network (NURCNet) for strip steel defect detection, which is composed of the following three submodules: the encoder network, decoder network, and refinement network. Figure 2 shows the framework of NURCNent, where the encoder is a fully convolutional residual network, to extract both the rich low-level spatial details and high-level contextual information, and an attention block followed each residual block makes the extract multiscale defect features, paying more attention to defective objects. Similar to [13], a U-shaped residual network is designed by composing a convolutional network with U-shaped residual blocks (URBs). Unlike [PERSON] et al. [13] of which both the encoder and the decoder use U-shaped networks to extract salient object features, we only use the U-structure in the decoding stage to reduce the model complexity. Furthermore, a U-shaped residual block is designed to extract defect object features and reduce the computational cost. Finally, a refinement network followed the decoder network to further optimize the coarse saliency map of the lack of boundary information output at the encoder-decoder stage.
## 2 Experimental Setup  
To investigate the cutting path effect on the spindle AE and vibration signals correlated to the tool wear condition, experiments were conducted on a micro milling research platform, as shown in Figure 1. The research platform was developed by the micro manufacturing research laboratory in NCHU, Taiwan. It includes an ITRI PC controller, an XY-axis motion table with a resolution of 1 \(\upmu\)m, a \(Z\)-axis motion table, and an NSK-EM30S6000 electric-motor chronic spindle with a rotation speed of up to 60,000 rpm. For the sensor installation, A Kistler 8152B121 AE sensor was installed on a fixture attached to the spindle housing, along with a 3-axis accelerometer (Endevco 65-100-R). The working frequency range is from 50 kHz to 400 kHz for the AE sensor and from 1.5 Hz to 6 kHz for the 3-axis accelerometer. In signal acquisition, the AE and vibration signals were collected by a PCI-6132 and PCI-6251 data acquisition card, with a sampling rate of 2 MHz and 30 kHz, respectively. Three types of cutting paths, including straight line cutting, square path cutting, and circle path cutting, were designed, as shown in Figure 2, to analyze the vibration and AE signals affected by changing the cutting direction. The implementation of the cutting pattern is illustrated in Figure 3. Three types of cutting paths were integrated together for each cutting pass. For the tool size and cutting condition setup, the size of the micro end mill used in the test is 700 \(\upmu\)m, and the cutting condition for the
## 3 Advanced Techniques in Fault Injection and Error Rate Estimation
### Automatic Configuration Memory Fault Injection Tool
#### 3.2.2 Function  
The ACMFI tool provides the capability to automatically conduct statistics-based fault injection testing and can analyze the results through three tabs.  
1. Test Scenario Setting * SLICE Region: Allows the user to define the area for fault injection (based on the SLICE region of the configuration memory). It calculates the total number of bits within the specified fault injection range and presents the result to the user. * Confidence Interval: Enables the selection of the number of test repetitions for statistical fault injection testing. * Number of Tests: Displays the final number of tests based on the fault injection range and chosen confidence interval. * Test Scenario: Automatically generates fault injection commands in accordance with the number of tests.
2. Automatic Testing * Target Board Communication Connection: Offers a COM port connection feature for connecting to the target board. * Message: A window to verify the status of the target board and whether reception was successful.  
Figure 7: Automatic Configuration Memory Fault Injection architecture.  
* Send Message: Provides a function for inputting single fault injection commands.
* Test Start/Finish Time: Indicates the start and end times for the statistical fault injection test.
* Test Start: Conducts the fault injection test and presents the results based on a predefined test scenario.
* Test Result Analysis
* SEU Setting: Configuration of single-event upset (SEU) parameters obtained from the literature research.
* AVF Result: Presentation of AVF results based on the automatic fault injection test outcomes.
* CM Size: Display of memory bit size corresponding to the fault injection test range.
* Test Result: Reports the final system SER and provides reliability and failure probability based on \(8\times 10^{9}\).
# Improvement of Recycled Aggregates Properties by Means of CO\({}_{2}\) [PERSON]
## 4 Results and Discussions
### Impact of Accelerated Carbonation on the Properties of RCAs
#### 4.2.1 Impact of Natural and Accelerated Carbonation on the Water Absorption Coefficient
Compared with RB 1-4, the CO\({}_{2}\) uptake of RB 1-4 C was doubled, and the increase in CaCO\({}_{3}\) was higher than for RCA-RB 1-4. One can conclude that the formation of CaCO\({}_{3}\) was correlated with the CO\({}_{2}\) uptake and the quantity of available cement hydrates. In fact, the quantity of portlandite was around 0.4% for NC-RB 1-4 C, compared with around 0% for NC-RB 1-4. Thus, the formation of CaCO\({}_{3}\) in RB 1-4 C was related to the consumption of portlandite, but for RB 1-4, the formation of CaCO\({}_{3}\) may have been due to the carbonation of C-S-H, which could not be determined by TGA analysis. Moreover, the natural carbonation of NC-RB 1-4 could affect its porosity, slowing the diffusion of CO\({}_{2}\) and limiting the formation of CaCO\({}_{3}\). However, the decrease in the WAC did not vary in a linear manner with the quantity of CaCO\({}_{3}\) formed, because for RB 1-4, the decrease in the WAC was around 31%, compared with 39% for RB 1-4 C. The formation of calcium carbonates by accelerated carbonation decreased the water absorption coefficient of the RCA by filling the pores, but the quantity of CaCO\({}_{3}\) formed did not have a simple correlation with the CO\({}_{2}\) uptake. 2.2 Effect of the CO\({}_{2}\) Concentration on the CO\({}_{2}\) Uptake and Water Absorption Coefficient  
Figure 17 also shows the effect of the CO\({}_{2}\) concentration on the CO\({}_{2}\) uptake. Tests were carried out on RB 1-4 C with CO\({}_{2}\) concentrations of 15% and 100% and the same water content (5.2%). The maximum CO\({}_{2}\) uptake increased by around 37% with the concentration between 15% and 100%. For RB 10-20, the difference in CO\({}_{2}\) uptake between the two concentrations was the same as this, as was shown in Section 4.1.2. Figure 16: Correlation between the WAC and the formation of CaCO\({}_{3}\) (one WAC measurement for RCAs after carbonation). The CO\({}_{2}\) uptake affected the WAC, as the pores in the RCA were clogged as a result of accelerated carbonation. For NC-RB 1-4 C, the WAC was about 5.2%. After accelerated carbonation, the WAC decreased by 31% and 39% at CO\({}_{2}\) concentrations of 15% and 100%, respectively. The difference between the decrease at 15% and 100% was around 11%. The WAC decreased at CO\({}_{2}\) concentrations of 20% for RCAs sourced from demolition concretes. The WAC decreased as the CO\({}_{2}\) concentration rose and due to the CO\({}_{2}\) uptake, but between CO\({}_{2}\) concentrations of 20% and 100%, the decrease in the WAC after carbonation became stable.
## 2 Materials and Methods
### Feature Encoding  
This study employs two techniques, word2 vec [23] and fasttext [24], for feature encoding of mRNA sequences. Word2 vec is a method used to learn word vector representations from large-scale text data, transforming words into vectors to capture semantic relationships between them in a high-dimensional space. Fasttext, on the other hand, learns word embeddings by considering character n-grams within words and uses these vector representations of n-grams to construct word vectors. This approach captures internal structures of words, thereby enhancing the model's generalization ability. The CSpredR model utilizes K-mer segmented fragments as features for the nodes in the de Bruijn graph. These K-mer fragments can be analogized to individual words, and directed edges naturally represent relationships between these \"words\". Thus, we view all mRNA sequences in the dataset as a corpus, where each mRNA sequence represents a sentence in this corpus. Furthermore, we consider all possible 4\({}^{k}\) K-mer fragments as the vocabulary for constructing these sentences. We then encode these K-mer fragments using word2 vec and fasttext techniques separately and fuse them as features for the nodes in the graph. **We chose weighted averaging as the fusion method for these two encoding approaches because it preserves their complementary characteristics while avoiding the computational complexity and overfitting issues associated with high-dimensional concatenation, thereby further optimizing feature representation and computational efficiency.** Experiments indicate that combining these two methods maximally enriches the semantic information of the de Bruijn graph.
# Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series
## 3 Data and Methods
### Method Used for the Simulation Study
#### 3.2.2 Adopted BRDF Model  
The kernel-driven RTLSR BRDF model, adopted for MODIS albedo products [27], was used in the frame of this study. This semi-empirical model, based on the formulation elaborated by [PERSON] et al. [28], consists of expressing the BRDF as a sum of three kernels representing basic scattering types: isotropic scattering, volumetric scattering and geometric-optical surface scattering. In this formalism, the BRDF is expressed as [28]:  
\[R(\vartheta,\ v,\ \varphi,\ \lambda)=f_{Iso}(\lambda)+f_{Vol}(\lambda)K_{ Vol}(\vartheta,\ v,\ \varphi,\ \lambda)+f_{Gev}(\lambda)K_{Gev}(\vartheta,\ v,\ \varphi,\ \lambda), \tag{4}\]  
where \(\vartheta,\ v,\ \varphi\) are the solar zenith, view zenith and relative azimuth angles, respectively. The terms \([K_{Vol}(\vartheta,\ v,\ \varphi,\ \lambda),K_{Gev}(\vartheta,\ v,\ \varphi,\ \lambda)]\) are the model kernels and the terms \([f_{Iso}(\lambda),\ f_{Vol}(\lambda),\ f_{Gevi}(\lambda)]\) are the spectrally dependent BRDF kernel weights, which are referred to as BRDF parameters in the remainder of the paper. The model kernels are computed following the assumptions and equations used for MODIS Albedo product [27]. In particular, the volumetric kernel, \(K_{Vol}(\vartheta,\ v,\ \varphi,\ \lambda)\), is the so-called Ross-Thick, which assumes a dense canopy layer of small leaves with a uniform leaf angle distribution, a Lambertian background and equal values of transmittance and reflectance based on a single-scattering approximation of the radiative transfer theory [29]. The geometric kernel, \(K_{Gev}(\vartheta,\ v,\ \varphi,\ \lambda)\),is the Li-Sparse kernel, assuming sparse three-dimensional objects casting shadows on a Lambertian background [30; 31].
# Comprehensive Review of Storage Optimization Techniques in Blockchain Systems
## 4 Methods
### Other Solutions
#### 4.7.2 State Channels  
The state channel [104] is a Layer 2 scaling solution that enables participants to perform multiple transactions and state updates off-chain, submitting only the final results to the main chain when the channel is closed. This approach substantially reduces the number of on-chain transactions and associated data storage requirements. By eliminating the need to record each transaction on the main chain, state channels effectively minimize blockchain storage redundancy. By offloading the majority of computations and transaction processing, state channels not only optimize storage resources but also improve system throughput and responsiveness. This makes them particularly well-suited for high-frequency transaction scenarios such as micropayments and decentralized exchange platforms. As a result, state channels significantly reduce the storage burden on blockchain systems, thereby enhancing scalability and overall efficiency.  
State channels share several technical similarities with IPFS. For example, the Lightning Network utilizes state channels and smart contract technologies to shift most transactions off-chain, recording only the final results on-chain after transaction completion, thereby reducing the blockchain's storage demands. Key mechanisms in the Lightning Network include the \"Recoverable Sequence Maturity Contract\" (RSMC) and the \"Hashed Timelock Contract\" (HTLC), which address challenges related to off-chain transaction confirmation and payment channels. Experimental results show that, compared to on-chain transactions, the Lightning Network substantially reduces block size requirements and alleviates storage redundancy. This version improves clarity, flow, and conciseness while maintaining technical accuracy and academic tone.