esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing Data for Digital Soil Mapping in French Research--A Review
## 7 Main Progresses, Perspectives, and Prospects  
The Increasing Availability of Remote Sensors and of Their Spatial, Spectral, and Temporal Resolution over Time  
Taking the first Landsat satellite (1972) as a starting point, the last fifty years have been characterized by the launch of an increasing number of Earth observation satellites with different spatial coverages and resolutions, spectral bands, and revisiting times. Meanwhile, a large number of airborne spectral sensors have been tested on smaller spatial coverages in order to explore or simulate the potential of new sensors, to obtain faster images and shorten revisit times. These technological advances have improved the DSM in several ways:  
The increase in spectral range and spectral band resolution has led to a drastic increase in the number of soil properties that can be predicted using RS (e.g., [43; 107; 341; 342; 343; 107]).  
The increase in spatial resolution allows the mapping of increasingly fine spatial distributions of soil properties and their controlling factors (e.g., [325; 343; 346]).  
Shorter revisit periods allow for the better detection of changes in soil properties over shorter time periods and better capture of soil management effects (e.g., [347; 348; 349; 350]).  
Increasing the number of RS products over time allows both the design of composite products and a shift from static mapping to monitoring certain changes (e.g., [128; 351; 326]).  
Merging data from RS products generally provides better predictions than using a single RS product (e.g., using a time series to increase both the prediction accuracy and map coverage [352], using synergistic information to retrieve soil information [353] or fusing products to increase both spatial and temporal resolution (e.g., [354; 355; 356])).  
Several missions with high signal-to-noise ratios, high spatial resolution, but limited coverage per day have been launched recently: the Italian PRISMA (PRecursore Iper-Septtrale della Missione Applicativa, launch 22 March 2019, 30 m; [358; 359]), the German EnMAP (Environmental Mapping and Analysis Program, launch 1 April 2022, 30 m, [360]), and the Japanese HISUI (Hyperspectral Imager Suite, launch 5 December 2019, 30 m; [361]). The Italian-Israeli SHALOM (Spaceborne Hyperspectral Applicative Land and Ocean Mission, launch 2024, 10 m [362]) is also expected to be launched soon. In addition, some global mapping missions are planned or under study for the coming years, with variable spatial coverage and different ground sampling distances, such as the former HyspIRI mission [363; 364] and the Sentinel-10/CHIME (Copernicus Hyperspectral Imaging Mission for the Environment) satellite proposed as an ESA candidate mission [365]. This wide range of satellites will represent a major step toward multi-resolution and multi-precisions soil mapping. Virtual satellite constellations (LANDSAT/S2, PLANET, EarthDaily... ) are promising tools for the future. One of the challenges is to harmonize the data because the sensors, acquisition conditions, and even the processing chains are different.
## 3 Results
### Study Characteristics  
#### 3.3.1 PA on Weekdays and Weekends  
A total of 30 studies compared PA and/or sedentary behavior (SB) across weekdays and weekend days in preschool children. Among these, a substantial proportion reported higher PA or lower SB during weekdays. These differences were often attributed to the structured nature of school routines, which encourage regular activity through scheduled recesses and reduced opportunities for prolonged inactivity. For instance, [PERSON] and [PERSON] [35] found that sedentary behavior accounted for 91.9% of total time on weekdays, increasing to 96.9% on weekends, while moderate-to-vigorous PA (MVPA) dropped from 6.3% to 2.0% between the two periods. [PERSON] et al. [20; 46] observed consistent weekday increases in step counts, with school-time activity levels notably surpassing those recorded during after-school hours and weekends.  
In contrast, a smaller subset of studies documented higher PA or lower SB during weekends. These patterns were typically associated with greater parental involvement and opportunities for unstructured or outdoor play. For example, [PERSON] et al. [32] found that participation in organized sports and parental perceptions of traffic safety were positively associated with weekend MVPA and total PA. Similarly, [PERSON] et al. [54] reported that walking or cycling with a parent during weekend leisure was positively correlated with both children's and mothers' MVPA.  
Nevertheless, the majority of studies did not report consistent differences between weekdays and weekends. Many observed either no significant variation or mixed results, depending on individual factors.
# Acceleration of Service Life Testing by Using Weibull Distribution on Fiber Optical Connectors
## 6 Conclusions  
1. A vibration ALT was developed using the probabilistic Weibull distribution and acceleration coefficients to estimate the reliability of the fiber optic connectors. According to the samples tested, the reliability R(t) was 0.970.
2. The reliability was determined by the fiber optical connector failure times obtained from the insertion loss measurements performed during testing. The measurements were performed using Agilent N7745A equipment with an 8-channel optical multiport power meter, with a speed measurement data acquisition and transfer of 5 kHz per channel and with event detection in a wavelength range from 1310 to 1550 nm, as shown in Figure 1.
3. This investigation can be applied to fiber optical connectors used in the telecommunications industry to assist in real and practical reliability analysis. An experiment by using the service life testing program from GR-326 was performed and its reliability R(t) = 0.694 was determined.
4. By using the relation of the scale Weibull parameter data from each experiment, (1) service life testing program, and (2) vibration ALT, the percentage of the product's service life consumed was obtained.
5. Finally, to determine the reliability and the remaining life of a product, we recommend, based on the statistical Weibull analysis conducted, performing the service life tests, because each test of the service life consumes a percentage of the product's life.  
Conceptualization, [PERSON]-C., M.R.P.-M. and M.M.H.-R.; methodology, J.M.B.-C., M.R.P.-M. and O.M.-Q. data analysis, [PERSON]-C. and S.R.-L; writing--original draft preparation, [PERSON]-C. and M.R.P.-M.; writing--review and editing, [PERSON]-C., [PERSON]-M. and S.R.-L.; supervision, [PERSON]-M.; funding acquisition, [PERSON]-C., M.M.H.-R. and O.M.-Q. All authors have read and agreed to the published version of the manuscript.  
The research received no external funding.
## 3 System Design and the Proposed SEAC-Based Resource Allocation Methodology
### Multi-Agent MDP Model
The actor and critic losses are extended to include the off-policy experiences as follows:  
The actor loss with experience sharing is  
\[al=log\pi(a_{t}^{i}\Big{|}o_{t}^{i},\varphi_{i})(r_{t}^{i}+\beta Q^{\prime}(s_ {t+1},\ a^{\prime},\ \theta^{Q^{\prime}})-Q\ (s,\ a,\ \theta^{Q})) \tag{15}\]  
\[L_{se}(\varphi_{i})=L(\varphi_{i})-\lambda\sum_{k,\
eq i}\frac{\pi\Big{(}a_{t }^{k}\Big{|}o_{t}^{k},\varphi_{k}\Big{)}}{\pi\Big{(}a_{t}^{k}\Big{|}o_{t}^{k},\varphi_{i}\Big{)}}\times al \tag{16}\]  
where  
* \(\pi(a_{t}^{i}\big{|}o_{t}^{i},\varphi_{i})\) represents the probability of taking action \(a_{t}^{i}\) given the observation \(o_{t}^{i}\) and the actor network parameters \(\varphi_{i}\);
* \(r_{t}^{i}\) is the immediate reward received by agent i at time t;
* \(\beta\) is the discount factor;
* \(Q^{\prime}\Big{(}s_{t+1},\ a^{\prime},\ \theta^{Q^{\prime}}\Big{)}\) is the target Q-value at the next state \(s_{t+1}\) using the next action \(a^{\prime}\) and the target network parameters \(\theta^{Q^{\prime}}\);
* \(L(\varphi_{i})\) is the standard actor loss for agent i without experience sharing;
* \(\frac{\pi(a_{t}^{k}\big{|}o_{t}^{i},\varphi_{k})}{\pi\Big{(}a_{t}^{k}\big{|}o _{t}^{i},\varphi_{i})}\) is the ratio of the policy probabilities of another agent k to that of agent i. This ratio adjusts how much influence the experiences of other agents should have on the current agent i's policy. The critic loss with experience sharing is  
\[L_{se}(\theta_{i})=L(\theta_{i})-\lambda\sum_{k\
eq i}\frac{\pi\Big{(}a_{t}^{ k}\big{|}o_{t}^{k},\varphi_{k}\Big{)}}{\pi\big{(}a_{t}^{k}\big{|}o_{t}^{k}, \varphi_{i}\Big{)}}\Big{|}\Big{|}Q\ \big{(}s,\ a,\ \theta^{Q}\big{)}-y_{t}\Big{|}\Big{|}^{2} \tag{17}\]  
where  
* \(L(\theta_{i})\) is the standard critic loss for agent i without experience sharing;
* \(||Q\ (s,\ a,\ \theta^{Q})-y_{t}||\) is the squared error between the predicted Q-value \(Q\ \big{(}s,\ a,\ \theta^{Q}\big{)}\) and the target value \(y_{t}\). This is a common loss function for training critic networks in reinforcement learning. The hyperparameter \(\lambda\) adjusts how much influence the shared experiences have. The loss functions for the actor and critic are modified to include contributions from other agents, which are controlled by the hyperparameter \(\lambda\). This approach ensures that the policy and value functions are updated not only based on the agent's own experience but also by considering the experiences of other agents in the network [24; 25]. By continuously updating policies and value functions based on real-time feedback, the SEAC ensures efficient and responsive resource allocation. The experience sharing mechanism allows agents to adjust their strategies, leading to better handling of traffic variability and ensuring that resources are allocated effectively without causing congestion or wastage.
## 3 Results and Discussions
### Trolox-Equivalent Antioxidant Capacity (TEAC)
It was observed that ultrasound treatments showed a tendency to increase TEAC significantly (day 0) from 1.13 \(\pm\) 0.01 to 1.54 \(\pm\) 0.01 (ABTS) and from 3.3 \(\pm\) 0.1 to 3.7 \(\pm\) 0.1 (DPPH); only 30 kHz 10 min treatment (measured as ABTS) presented a significant decrease in the control. In general, the increase in antioxidant capacity is possibly due to a more significant disruption of cell walls, facilitating the release of phenolic residues attached to traces of pectin, cellulose, hemicellulose, and lignin in the cell wall [47]. Similar results were reported in _Fragaria \(\times\) ananassa_ (strawberry) stored at 4 \({}^{\circ}\)C for 15 days, and _Actinidia  
Figure 1: Changes in Trolox-Equivalent Antioxidant Capacity (TEAC) measured as ABTS** (2,2\({}^{\prime}\)-azino-bis(3-ethylbenzothazoline-6-sulfonic acid) Cationic Radical Assay (**Top**) and DPPH- (2,2-diphenyl-1-picrilhydrazil) Radical Scavenging Assay (**Bottom**) during storage at 4 \({}^{\circ}\)C in _Passiflora edulis f. edulis_ Sims pulp (_gulupa_) subjected to low-frequency ultrasound treatments. Data expressed as mean \(\pm\) standard deviation of three repetitions. delicious_ (Kiwi) juice sonified with the US of 33 kHz for 10 min and 20 kHz for 16 min, respectively [26]. In both investigations, a positive effect of this property was noted on the sonicated samples. This increase in antioxidant capacity with ultrasound treatment can also be attributed to the hydroxylation of flavonoids or the generation of hydroxyl radicals that increase the hydroxylation of food materials [36]. Storage did not show a marked tendency to increase or decrease the antioxidant activity significantly. There were highs and lows in each of the treatments; this property of the sonicated juice could be directly attributed to the US's cavitation, which could have caused the changes observed over the days [31]. It should be noted that ultrasound treatments do not tend to decrease antioxidant activity. Results are opposite to those identified in _Passiflora caerulea_, where it was found that in fresh and pasteurized juice, this property decreased from day 4 of storage [48]; additionally, refrigerated fresh fruit juices (for 29 days) have been reported to lose antioxidant potential [49]. According to the experimental conditions, it is considered that around 16 days of treatment is the most recommended time for the consumption of antioxidants, in agreement with [PERSON] et al. 2014 [50]. On day 20, there was a decrease in TEAC measured as DPPH, possibly due to the decrease in some low-polarity antioxidant molecules, which react to a greater extent with this reagent.
## 6 Development Trends and Prospects
### Interpretability  
The \"black box\" nature of deep learning models has always been a barrier to industrial applications. In recent years, the demand for interpretability of models has been on the rise. Researchers are exploring more transparent detection models that enable the generation process of detection results to be understood and interpreted, which is crucial for user trust and fault localization. Researchers are gradually improving the transparency of intelligent fault detection by using attention mechanisms, interpretable feature maps, and other methods.
## 1 Introduction
The scarcity of data in languages such as Tamil is not merely a quantitative issue but also a qualitative one, reflecting a gap in cultural representation and linguistic diversity. This scarcity becomes particularly problematic in the context of neural network training, where the quantity and variety of data significantly influence the model's accuracy and versatility [2,8]. The linguistic complexity of Tamil, with its distinct phonetics, syntax, and semantics, further complicates the scenario. Unlike Indo-European languages, Tamil's agglutinative nature, where words are formed by joining morphemes, poses unique challenges in language processing. Moreover, the rich cultural backdrop of Tamil, with its vast literary and artistic traditions, adds layers of meaning that standard models might overlook. This cultural richness brings forth images and scenes steeped in local traditions, festivals, and everyday life that are replete with nuances and subtleties. Capturing these elements in captions requires a model that understands not just the visual but also the cultural and emotional context. The introduction of the Dynamic Context-Aware Transformer (DCAT) aims to bridge these gaps. By integrating cutting-edge techniques like Vision Transformers and Generative Pre-trained Transformers, DCAT is designed to capture both the visual fidelity and the contextual depth required for effective image captioning in Tamil. The model's architecture, incorporating dynamic attention mechanisms and a novel Context Embedding Layer, is tailored to navigate the intricate interplay between visual cues and linguistic expression inherent to Tamil. The significance of this research extends beyond technological innovation; it touches on the broader themes of cultural representation and language preservation. By focusing on a low-resource language, this work contributes to the democratization of AI technologies, ensuring that the benefits of advancements in image captioning are not limited to a handful of widely spoken languages but are accessible to a more diverse global audience. This endeavor is not just about building a more accurate or efficient system; it is about creating technology that understands and respects the linguistic and cultural diversity of its users. As we continue to explore the realms of artificial intelligence and machine learning, the need to integrate these technologies with humanistic considerations becomes increasingly paramount. DCAT represents a step in this direction, aiming to create a system that is not only technically proficient but also culturally aware and linguistically inclusive. This research, therefore, is not just a contribution to the field of computer vision or natural language processing; it is a testament to the potential of AI to bridge gaps between technology and the rich tapestry of human languages and cultures.
## 4 Discussion  
The point cloud dataset is extensive, and points representing discontinuities lack distinct characteristics, so it is usually necessary to extract certain features for the point cloud classification. [PERSON] et al. [43] utilized four features--coordinates, normal vector, curvature, and density of points--as the basis input of the Artificial Neural Network (ANN) model, successfully achieving efficient point cloud classification. However, this method involves the manual selection of a sufficient number of training points, a process prone to errors that can adversely affect subsequent point cloud classification. To eliminate such effects as much as possible, it is generally necessary to repeatedly select training sets until better classification results are obtained, making it a time-consuming and labor-intensive process. The proposed method classifies point clouds solely based on the normal vectors of points, simplifying the extraction of point cloud features, and also demonstrating outstanding performance in discontinuity sets extraction. Employing a method that revolves around generating training sets around selected training points reduces the necessity for manual point selection, and decreases the likelihood of choosing erroneous points, thereby enhancing the efficiency and accuracy of acquiring training sets.  
Within the three-dimensional point cloud acquired through scanning rock surfaces, the presence of noise points not associated with discontinuity, induced by factors like vegetation coverage, rock weathering, and human activities, adversely impacts subsequent calculations of discontinuity parameters. In the realm of prevailing methodologies for point cloud classification, mitigating the impact of noise points remains a formidable challenge. This study addresses the challenge by enhancing the Naive Bayes classifier through the incorporation of a logarithmic probability threshold selected by the knee point algorithm, enabling more effective filtration of noise points. To gauge the denoising efficacy of the improved Naive Bayes classifier, this paper compares it with the Fuzzy C-Means (FCM) clustering algorithm applied to extract discontinuity sets from the point cloud of case B. As shown in Figure 18, the results of extracting discontinuity sets using the two methods are the same, but there are still slight differences. The FCM algorithm, in its indiscriminate classification of all points, fails to discern and eliminate noise points effectively. Notably, in the upper-right section of the point cloud, artificial rock discontinuities are inaccurately assigned to discontinuity set 1. In contrast, the method proposed in this paper identifies noise points by calculating the logarithmic probability of points on each discontinuity set, as illustrated in Figure 18a where the artificial rock plane has been almost completely removed. Furthermore, upon comparing Figure 18b, it becomes apparent that Figure 18a exhibits some conspicuous blank \"voids\", which result from the removal of noise points situated in irregular locations, such as along the edges of the rock mass and in weathered or fragmented areas.
# Quantum Time-Memory-Data Tradeoff Attacks on the SNOW-V Stream Cipher
## 2 Brief Description of SNOW-V
The procedure of initialization can be described in the pseudocode as follows. ```
Procedure\(INITIALIZATION(K,IV)\) \((a_{15},\cdots,a_{8})\leftarrow(k_{7},\cdots,k_{0})\) \((a_{7},\cdots,a_{0})\leftarrow(iv_{7},\cdots,iv_{0})\) \((b_{15},\cdots,b_{8})\leftarrow(k_{15},\cdots,k_{8})\) \((b_{7},\cdots,b_{0})\leftarrow(0,\cdots,0)\) \(R1,R2,R3\gets 0,0,0\) For\(t\) from 1 to 16 do \(T1\leftarrow(b_{15}\cdots,b_{8})\) \(FSMupdate()\) \(LFSRupdate()\) \((a_{15},\cdots,a_{8})\leftarrow(a_{15},\cdots,a_{8})\oplus z\) If\(t=15\)then\(R1\gets R1\oplus(k_{7},\cdots,k_{0})\) If\(t=16\)then\(R1\gets R1\oplus(k_{15},\cdots,k_{8})\)
```  
**Algorithm 1** Initialization of SNOW-V.  
After the initialization, the SNOW-V stream cipher starts the keystream generation. The following Algorithm 2 gives a full description of SNOW-V in the pseudocode. ```
\(INITIALIZATION(K,IV)\)  While more keystream blocks needed do \(T1\leftarrow(b_{15},\cdots,b_{8})\) \(FSMupdate()\) \(LFSRupdate()\)  Output keystream block \(z\)
```  
**Algorithm 2** SNOW-V Algorithm.
## 2 Materials and Methods
### EO Image Processing  
The scope of this module is to analyze EO images and extract any available information included. The EO images are retrieved using a sentinel-2 parser responsible for downloading sentinel-2 images from the Copernicus Open Access Hub, according to an area of interest and a timestamp. The users select these input parameters when they select a topic of interest from the user interface presented in Section 3. This image is further processed for the detection of affected areas and changes in buildings that have occurred from a disaster. Two distinct sub-modules with different scopes have been included in the EO image processing module: the first one, called the \"building footprint extraction and damage detection sub-module\" aims to detect damages within an urban area using RGB images and is based on the buildings that exist within a certain area. The second one. Called the \"affected area detection\" sub-module, is independent of the type of area and can detect floods and burned areas based on sentinel-2 images.  
The outcomes of the processing are the processed EO images retrieved, accompanied by rough statistics on the impacted area (e.g., surface, type of land cover/land use affected). The numerical information, provided as output, is taken into consideration during the generation of the news article, giving an added value to the generated news article. On the other hand, the processed EO images are made available to the end-user through the User Interface, allowing them to select if they would like these images to be presented in the final article or not.  
The sub-modules included in this module are:
## 4 Experimental Results  
To verify the noise reduction performance of the control system, noise control was performed on two different types of noise signals: a narrowband noise signal and a noise signal whose energy is concentrated in a specific band. In this study, TMS320C6748 of TI Company was used as the DSP control platform. The signal sampling rate was 8 kHz and the data width is 16 bit. In addition, this study detects the sound field distribution characteristics before and after noise reduction to verify the noise reduction performance of the system in an open space.