esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges
## 2 Principles and Methods
### Remote Sensing of Terrestrial Ecosystems  
Vegetation growth in cold regions is limited by multiple environmental constraints including low temperatures, frozen sub-surface soils in permafrost affected terrain, low light levels in winter and shoulder seasons, water stress in summer, and nutrient limitations due to the very slow release of plant available nitrogen and phosphorus from seasonally frozen or inundated soils [196; 197]. Although ground-based measurements are needed to inform local-scale investigations, remote sensing is especially important for the cold regions because it provides spatially and temporally resolved data over broader scales, allowing for synoptic investigations of vegetation ranging from individual plots to regional and global extents.
# Instantaneous Frequency Representation Used for CPA Laser Simulation
## 3 Instantaneous Frequency Representation for a Realistic CPA Laser Simulation Step by Step
If we consider the effect nearby \(\omega_{0}\), \(\omega-\omega_{0}\ll\delta\omega_{1}\) and that \(\delta\omega_{1}\) is nearly constant over the bandwidth, the Nth-order distortion for the pre-pulse is produced by a combination of higher order terms of the stretcher dispersion:  
\[\varphi_{\text{M,prepulse}}(\omega_{0})=-\sum_{n=\text{M}+1}^{\infty}\frac{ \varphi_{n}}{(\text{M}-1)!(\text{n}-\text{M})!}(\delta\omega_{1})^{n-\text{M}} \tag{15}\]  
For pure third-order distortion, the chirp of the pre-pulse can be approximated to:  
\[\frac{\partial\delta\tau_{\delta\omega_{1}}(\omega)}{\partial\omega}\approx-[( \varphi_{3}\delta\omega_{1})]. \tag{16}\]  
For pure fourth order, the chirp is combined with a third order:  
\[\frac{\partial\delta\tau_{\delta\omega_{1}}(\omega)}{\partial\omega}\approx- \Big{[}\Big{(}\frac{\varphi_{4}}{2!}\delta\omega_{1}{}^{2}\Big{)}\Big{]}- \Big{[}\frac{\varphi_{4}}{2!}\delta\omega_{1}\Big{]}[(\omega-\omega_{0})]. \tag{17}\]  
These approximations confirm the effects seen in Figures 7 and 8. Non-intuitively, having a large third order may ease the constraints on the pulse contrast, as it decreases the peak power of the generated pre-pulse due to its residual chirp. Note, however, that this model lacks precision because it assumes that \(\delta\omega_{1}=\left(\omega_{1}-\omega_{2}\right)_{\tau}\) is constant. Figure 7: (**a**) Instantaneous frequency representation with third-order spectral phase distortion in stretching (\(\varphi^{(2)}=\frac{2}{\left(\pi\Delta f\right)^{2}}1.6\cdot 10^{6}\) and \(\varphi^{(3)}=-\frac{6}{\left(\pi\Delta f\right)^{2}}50\cdot 10^{3}\)), (**b**) its temporal intensity on log scale (dB), (**c**) zoom of temporal intensity of the pre-pulse on log scale (dB) and (**d**) normalized pre-pulse spectrum. This realistic example of a case that is currently limiting high-intensity laser performance on the target illustrates the main advantage of the presented IFR simulation method. It is compatible with huge time-bandwidth product pulses existing in most high-power CPA laser systems. The temporal domain can be stretched and recompressed without loss of information and without any modification on the frequency domain. It is also very efficient numerically, as all the operations presented above are purely along one dimension.
## 1 Introduction  
The success of endodontic therapy mainly depends on correct root canal shaping. This step is essential for the effectiveness of all further procedures, including disinfection and tridimensional filling of the teeth root canals [1; 2]. The objective of endodontic instrumentation is to produce a tapered continuous preparation, which preserves the root canal anatomy and maintains the apical foramen as small as possible, without any deviation from the original canal curvature [3,4]. Pressures generated during the canal instrumentation against the dentinal walls may lead to inappropriate canal transportation or microcracks [4]. Many studies show that canal transportation may result in inappropriate dentinal removal, adversely affecting the long-term tooth prognosis [1,2,4,5,6]. Moreover, microcracks may lead to vertical fracture, which is one of the first causes for tooth loss [7,8].  
Several studies reported the better shaping properties of nickel-titanium (NiTi) rotary files compared to previous stainless-steel instruments [5,9,10]. The super-elasticity of rotary NiTi instruments reduces the risk of root canal aberrations and allows for better maintenance of the original root canal shape, especially in canals with severe curvatures [11,12]. The ideal endodontic instrument should create a centrifugal shaping of the canal, have an efficient cutting capability and not generate excessive pressure against the dentinal walls [4,5]. Therefore, the mechanical design of the new NiTi endodontic instruments aims to improve the distribution of contact pressures against the root canal walls in order to enhance their shaping ability [13,14]. It is assumed that the pattern and the homogeneity of the pressure distribution that an instrument exerts against all portions of the canal walls are a valid indicator of its centering ability during shaping [4]. Nowadays, project management for endodontic instruments covers several phases: mechanical design, manufacturing of the prototypes, laboratory and clinical tests. The finite element analysis (FEA) may be divided into three phases: pre-processing, where the model is prepared; analysis, where the problem is solved; and post-processing, where the results are analyzed. Recently, the use of FEA has been proposed to analyze the mechanical behavior and the cyclic fatigue resistance of the endodontic instruments subjected to static and dynamic loads [15,16,17,18,19,15,19,20,21]. However, this technology has never been used to virtually predict the pressure distribution against canal walls of the endodontic instruments. Therefore, this study aims to analyze and predict, through FEA, the contact pressure distribution and, subsequently, the centering ability of two different NiTi rotary instruments against the canal walls during rotation. The chance to virtually predict the instruments' root canal centering ability through FEA during an early design phase and without the need of prototypes could dramatically influence the ability to produce even better performing NiTi instruments, reducing costs and developing times for manufacturers.
# Substring Position Search over Encrypted Cloud Data Supporting Efficient Multi-User Setup +
## 5 Substring Position Searchable Symmetric Encryption
### SSP-SSE Construction  
We now present the details of the proposed SSP-SSE scheme. The scheme consists of two phases, namely the setup phase and search phase. The setup phase is done once by the data owner to upload the set of encrypted documents and the searchable index to the cloud provider. In this phase, the data owner uses the KeyGen, BuildTree and Encrypt algorithms to encrypt the document collection, as well as to construct the searchable index. The search phase is performed every time by the cloud user when a query is submitted. In this phase, the cloud user invokes the ConstructQuery algorithm to generate the search query. The cloud provider executes the Search algorithm to output matching results. Finally,the cloud user invokes the Decrypt algorithm to decrypt the document collection to the original view. Our scheme is based on a set of important notations shown in Algorithm 1. We outline the details of setup phase in Section 5.3.1. We later show the search phase in Section 5.3.2.  
```
\(t=(t_{1},t_{2},\ldots,t_{n})\) - the text constructed from document collection \(D\). \(t_{i}\) is the letter in text \(t\) at position \(i\). \(\
u[i]\) - the node in \(\Lambda\) at index \(i\) (\(i\in[1;n]\)). \(V(\
u[i])\) - the position value of node \(\
u[i]\) in \(\Lambda\). \(pid(D_{j})=id(D_{j})||pos_{D_{j}}\) - concatenation of document identifier \(D_{j}\) (\(j\in[1;l]\)) with position \(i\) of character \(t_{i}\) in the document \(D_{j}\). \(L(\
u[i])\) - the path label of node \(\
u[i]\) in position heap tree \(\Lambda\). \(L_{parent}(\
u[i])\) - the path label of \(\
u[i]\)'s parent node. \(depth(\
u[i])\) - the depth of node \(\
u[i]\) in \(\Lambda\). \(\overline{\
u[i]}\) - the encrypted node in \(\bar{\Lambda}\) at index \(i\). \(\overline{V(\
u[i])}\) - the encrypted value of node \(\overline{\
u[i]}\) in \(\bar{\Lambda}\). \(\overline{L(\overline{\
u[i]})}\) - the encrypted path label of node \(\overline{\
u[i]}\). \(\overline{L_{parent}(\overline{\
u[i]})}\) - the encrypted path label \(\overline{\
u[i]}\)'s parent node. \(descendants(\overline{\
u[i]})\) - the set of descendant (child) nodes in the subtree rooted at node \(\overline{\
u[i]}\). If \(\overline{\
u[i]}\) is the leaf node, then \(descendants(\overline{\
u[i]})=0\). \(ancestors(\overline{\
u[i]})\) - the set of ancestor (parent) nodes at node \(\overline{\
u[i]}\). If \(\overline{\
u[i]}\) is root node, then \(ancestors(\overline{\
u[i]})=0\).
```  
**Algorithm 1**Notions.
# INSPIRE-SAT 7, a Second CubeSat to Measure the Earth's Energy Budget and to Probe the Ionosphere
## 1 Introduction
This device will be associated with the audio transponder (system that receive a signal at a given frequency and transmit it), which has already be validated onboard the UVSQ-SAT CubeSat. The last objective of the INSPIRE-SAT 7 mission is educational. It is to provide hands-on experience and training to students with a NewSpace approach (science objectives, mission analysis, design, construction, CubeSat integration and test, mission operations, management, cost and risks, mission and product assurance). This training of the new generation in the field of the NewSpace is important for a variety of future space missions for Earth constellation and for Astronomy and Astrophysics. The goal of the manuscript is to present an overview of the INSPIRE-SAT 7 mission. Section 2 outlines the scientific objectives of INSPIRE-SAT 7. Section 3 provides a description of the INSPIRE-SAT 7 CubeSat and its operational requirements. Section 4 presents a preliminary analysis of the expected results from combined measurements by INSPIRE-SAT 7 and UVSQ-SAT using a numerical model simulating ERB measurements by two CubeSats in orbit. This analysis will also demonstrate the interest in implementing a satellite constellation to improve the determination of the EEI.
# Smart Textiles: A Review and Bibliometric Mapping
###### Abstract  
According to ISO/TR 23383, smart textiles reversibly interact with their environment and respond or adapt to changes in the environment. The present review and bibliometric analysis was performed on 5810 documents (1989-2022) from the Scopus database, using VOSviewer and Bibliometrics/Biblioship for science mapping. The results show that the field of smart textiles is highly interdisciplinary and dynamic, with an average growth rate of 22% and exponential growth in the last 10 years. [PERSON], and Torah, R.N. have published the highest number of papers, while [PERSON] has the highest number of citations. The leading journals are _Sensors, ACS Applied Materials and Interfaces_, and _Textile Research Journal_, while _Advanced Materials_ has the highest number of citations. China is the country with the most publications and the most extensive cooperative relationships with other countries. Research on smart textiles is largely concerned with new materials and technologies, particularly in relation to electronic textiles. Recent research focuses on energy generation (tripoelectric nanogenerators, thermoelectrics, Joule heating), conductive materials (MXenes, liquid metal, silver nanoparticles), sensors (strain sensors, self-powered sensors, gait analysis), speciality products (artificial muscles, soft robotics, EMI shielding), and advanced properties of smart textiles (self-powered, self-cleaning, washable, sustainable smart textiles).  
smart textiles; bibliometric analysis; science mapping; research trends; hotspots +
Footnote †: journal: Applied Sciences
# An Adaptive Process Monitoring Method Based on an Improved ADALINE Neural Network
## 2 Theory and Methodology
### ADALINE Neural Network  
The ADALINE neural network is an artificial neural network algorithm, and its topology is shown in Figure 1.  
In Figure 1, \(n\) is the dimension, the vector \(X\) contains the input variables, and the vector \(W\) is the weight to be adjusted. The least mean square (LMS) is the learning algorithm that is used to adjust the weights, and \(T\) is a linear function. \(Y\) is the output, and \(r\) is the expected output. The learning method of ADALINE is supervised learning, using \(y\) to compare with \(r\) and sending the difference \(\varepsilon=\mathrm{r}-y\) into the LMS algorithm. In this way, the weight vector \(W\) will continuously be adjusted to make \(y\) as close to the target 0 as possible. Finally, when \(x\) is sufficiently small, \(W\) converges, and the needed weight value is obtained.  
The weight adjustment algorithm of the \(LMS\) learning rules is as follows:  
\[\Delta W=\eta(r-WX)X^{T}, \tag{2}\]  
\(\eta\) is the selected learning rate, which indicates the learning speed. \(T\) is a linear function; therefore, Equation (2) can be expressed as Equations (3) and (4).  
\[\Delta W=\eta(r-y)X^{T}=\eta\varepsilon X^{T}, \tag{3}\]  
\[\Delta\varepsilon=\Delta(r-WX)=\Delta r-\Delta WX \tag{4}\]  
Since \(r\) is a constant, \(\Delta r=0\) and \(\Delta\varepsilon=-\Delta\)WX. Through substitution into Equation (4), we obtain \(\Delta\varepsilon=-\eta\varepsilon X^{T}X=-\eta\varepsilon|X|^{2}\). Obviously, \(\Delta\varepsilon\) and \(-\varepsilon\) are in the same direction. Therefore, \(y\) and \(r\) are constantly close. When \(\varepsilon\) is sufficiently small, the weights of the adaptive neurons converge, that is, \(w\).
## 5 Performance Evaluation
### Velocity  
Another decisive parameter is the velocity of the MN. Target applications mandate supporting the physical speed of humans (for healthcare applications) averaging about 5 km/h or 1.34 m/s. For industrial applications, many machines such as forklifts are capable of speeds over 22 km/h but regulatory agencies such as Occupational Safety and Health Administration recommend a speed of 5 km/h for typical indoor environments [52]. We have evaluated the performance of the system with speeds up to 2.4 m/s.  
We have tested a group of scenarios in which a single MN roams around according to the TLW model with its speed averaging to the values between 0.6 m/s to 2.4 m/s. What stands out in Figure 17(a) is the steady rise in localization error with increasing velocity. In the previous section in Figure 17(b), we observed a general decreasing pattern of PDR with increasing velocity of the MN.  
The loss in packet delivery ratio with an increased physical speed of MN stems from both (i) lower accuracy of the filter and (ii) higher probability for delayed reception of the SET packet. It is possible to increase the supported maximum speed of the MN. At a higher velocity, it is more challenging to keep the accuracy of the filter unless by transmitting more frequent control beacons. A more frequent control beacon is not always desirable since it imposes a higher communication overhead. This higher overhead may avoid the boosted localization accuracy from improving the PDR. We consider dynamically changing this beaconing interval for future work.
## 1 Introduction
A QR code is a two-dimensional matrix composed of QR code symbols and has the advantages of a fast reading speed, large amount of information, low cost, and high reliability [20]. In the process of the AGV moving, the QR code camera at the bottom of the vehicle scans the QR code. It determines the position and attitude information of the AGV by identifying the current QR code. In this process, the angular error and positional error in the motion process are analyzed and calculated. By using various algorithms for correction and compensation, the positioning accuracy of the robot is improved to a certain extent. Based on the above problems, an improved AMCL particle localization algorithm is introduced into the SLAM fusion QR code, connecting the world coordinates of the QR code to enhance the number of particles in the AMCL algorithm, optimizing the particle sampling strategy, and improving the motion model to introduce a non-linear model to improve the localization accuracy. The AGV navigation and positioning system researched in this paper is characterized by a high accuracy, low latency, reduced maintenance cost and improved work efficiency. It can solve the navigation and positioning problems encountered by AGVs in practical work. According to the requirements of the actual production environment, this paper describes the improved methods and measures, and compares them with the unimproved methods and measures. Figure 1 shows the system framework diagram of AGV. The following are the main contributions made by this paper:  
1. Improve the way of generating and resampling the initial particle swarm of the AMCL algorithm, so that the particle performance is more stable, and can effectively match the set template library to improve the positioning accuracy. 2. In the navigation process, even if the QR code is damaged and stained, it still does not affect the navigation and positioning. Hybrid navigation can complement each other and complete the navigation task independently. 3. The improved AMCL algorithm fuses QR code navigation. The two types of data are fused using EKF in order to improve the positioning accuracy and at the same time reduce the navigation time and improve the navigation efficiency. 4. The improved AMCL algorithm can effectively reduce or avoid the occurrence of particle abduction events and increase the reliability of accurate AGV positioning. The superiority of the improved algorithm can be effectively proved through field experiments.
# SP-YOLOv8s: An Improved YOLOv8s Model for Remote Sensing Image Tiny Object Detection
## 3 Methodology
### SP-YOLOv8s  
For the recognition task of tiny objects, the original YOLOv8s algorithm has an insufficient ability to extract features of tiny objects because tiny objects occupy very tiny pixels of the image. This leads to a low accuracy rate when detecting tiny objects and is, therefore, not suitable for practical applications. To solve these problems, this paper proposes to use an efficient path aggregation network, SPANet, to replace the path aggregation network, PANet, in the YOLOv8s network. This aims to improve the network's ability to fuse feature maps at different scales while reducing the network parameters. In addition, except for the first downsampling, which retains the original CBS, all other downsampled CBS modules are replaced with SPD-Conv modules to make the network focus on the tiny object features and reduce the complex background interference. Specifically, the improvement lies in replacing the original downsampling modules in layers 3, 6, 9, and 12 of the YOLOv8s backbone network and layers 25 and 29 of the neck network with SPD-Conv modules, and replacing the original neck network with SPANet. It is true that the SP-YOLOv8s backbone network consists of layers 1-14, the neck network consists of layers 15-31, and the last layer is the decoupling detection head, and the above three parts constitute the complete SP-YOLOv8s. In short, this paper proposes the SP-YOLOv8s algorithm (YOLOv8s incorporating SPD-Conv and SPANet). The network structure of the proposed algorithm is shown in Figure 3. The SP-YOLOv8s algorithm is executed as follows:  
Figure 3: Network structure of the improved YOLOv8s: SP-YOLOv8s.  
Figure 2: Path aggregation network architecture; (**a**) PANet; (**b**) SPANet.  
Input: images of size 640 \(\times\) 640 \(\times\) 3.  
1. Image feature extraction using backbone network;
2. Feature fusion using neck network;
3. The feature maps of layers 23, 27, and 31 are input to the decoupling detection head. Output: feature maps of size 160 \(\times\) 160 \(\times\) 39, 80 \(\times\) 80 \(\times\) 39, 40 \(\times\) 40 \(\times\) 39.
# Distantly Supervised Named Entity Recognition with Self-Adaptive Label Correction
## 4 Experiment
### Comparison with State of the Arts  
The proposed method was compared with two groups of baselines, i.e., fully supervised NER models and distantly supervised NER models.  
The fully supervised NER methods are taken as the upper bound of these datasets. BiLSTM-CRF [35] effectively captures the sequential relationships among the input tokensand jointly predicts labels in the sentence. RoBERTa-base [36] is a robustly optimized BERT pretraining approach.  
Distantly supervised NER approaches explore tackling the noisy NER issue from different perspectives. AutoNER [15] adopts a revised fuzzy CRF layer to handle noisy labels with a new Tie or Break scheme. LRNT [23] is a name-tagging model solely based on weakly labeled data, which focuses on the effective portions of noisy data and utilizes Partial-CRFs to achieve global optimum. Self-training [22] is a self-training framework with marginal CRF to reannotate the missing labels. [PERSON] et al. [27] propose a calibration method to estimate the confidence of entity labels with prior noise ratio and integrate a self-training framework to boost performance (denoted as Confidence Estimation). Bond [7] also adapts self-training directly to noisy labels. SCDL [28] jointly trains two teacher-student networks in a mutually beneficial manner to iteratively perform noisy label refinery. BiLSTM-CRF [35] and RoBERTa-base [36], which are directly trained on noisy data, are also included, which can be viewed as the lower bound of these datasets. The base model architecture of Self-Training and Confidence Estimation is BiLSTM-CRF, while that of Bond and SCDL is RoBERTa.  
Table 1 presents the performance comparison of the proposed SALO against other methods. From the table, there are several observations as follows:  
* The proposed SALO outperforms existing distantly supervised approaches on all datasets, which demonstrates the superiority of the built model in noisy NER label learning. An in-depth data analysis reveals that SALO is able to detect accurate boundaries of various few-shot entity mentions such as [PERSON] and [PERSON] in the Webpage dataset compared with BiLSTM-CRF.  
BiLSTM-CRF architecture obtains 3.66% and 1.71% improvements on average \(F_{1}\) scores over Self-Training and Confidence Estimation, respectively. This improvement could be attributed to the adaptive label correction, which can successfully avoid the overconfidence issue of deep neural networks for noisy data and does not require to know/model the ratio of noise data in the training data like Confidence Estimation.
* Bond and SCDL which are two advanced baselines with the RoBERTa base model under the distantly supervised setting. The proposed SALO counterpart achieves 4.37% and 1.35% improvements on average \(F_{1}\) scores compared with Bond and SCDL, respectively.
* The models that explicitly handle the overconfidence issue (i.e., SALO and Confidence-Estimation) generally perform better than other distantly supervised baselines.