esa-sceva/satcom-chunk-collection huggingface dataset:
# Cloud Computing Based on Computational Characteristics for Disaster Monitoring
## 2 Related Work  
Remote sensing is widely used in meteorology, land, ocean, agriculture, geology, and military fields. Remote-sensing monitoring is a technology that monitors and identifies environmental quality conditions by collecting electromagnetic wave information from the environment, such as via aviation or satellite. It can quickly and comprehensively acquire large-area synchronous and dynamic environmental information. Therefore, it is being used widely. Using remote-sensing technology, people effectively and dynamically acquire disaster information in large-scale disaster-stricken areas in real time. With the development of remote-sensing sensors, data obtained by different sensors can be used to calculate various parameters or indicators that directly or indirectly reflect disaster conditions. Numerous means exist of building disaster-monitoring models.  
Disaster-monitoring models are based on remote-sensing data as a data source, with a variety of auxiliary data as the driving data. The complex diversity of disaster-monitoring models is reflected in three aspects: data source, the model's function, and implementation algorithm. There are three main reasons for this complex diversity: (1) Currently, due to the rapid development of Earth observation technology, geoscience data acquisition methods are characterized by multi-sensor, multi-spatial resolution, multi-time resolution, and multi-temporal features. The data source types of the model are diverse, and different disaster-monitoring models often have different data requirements. For the collaborative processing of geoscience data from multiple sources and with different types, a new processing model is required for implementation. (2) Ground object information contains characteristics relating to time, space, spectrum, and geoscience parameters, and the disaster-monitoring model often treats these features in a targeted manner. (3) When targeting different levels and disciplines of users, the same type of disaster-monitoring model is implemented using different algorithms. Although the functions are similar, the algorithms are different.  
Macroscopically, disaster-monitoring models are often not isolated but aggregate multiple processing services to accomplish a task. Although the disaster-monitoring model has certain flexibility in aggregation, it nonetheless operated under certain rules. A disaster-monitoring model often follows several steps: data acquisition, preprocessing, information extraction, application analysis, and data product visualization. Partial and overall relationships and conditional dependencies between services exist in each step.
## 4 Discussion
### Carbon and Nitrogen Losses  
TN losses can be attributed mainly to NH\({}_{3}\) volatilization [50]. In this research, the NH\({}_{3}\)-N loss accounted for more than 60% of TN loss, meanwhile, only about 2% of TN loss was lost by N\({}_{2}\)O emission. The result was similar with [4]. The NH\({}_{3}\)-N loss of the three treatments accounted for 13.06-17.26% of the initial TN, which was higher than the range of 2.56-9.73% as reported by [PERSON] et al. [39] during composting with acidic additives, and lower than 29.17-60.55% during aerobic composting with sewage sludge as reported by [PERSON] et al. [3]. Therefore, NH\({}_{3}\) emission may be influenced by the different materials, additives, and aeration condition. Among the three treatments, continuous aerated treatment (C1) had the highest nitrogen loss (23.21%) due to its highest NH\({}_{3}\) volatilization (17.26%).  
CO\({}_{2}\) emission was the principal pathway for the TOC loss in this study, which comprised over 65% of TOC loss. While the CH\({}_{4}\)-C loss accounted for only about 0.3% ofTOC loss. The result was comparable with [PERSON] et al. [51] and [PERSON] et al. [42] in pig manure composting. C1 showed the largest TOC loss (51.26%) due to the highest CO\({}_{2}\) accumulation, as sufficient oxygen enhanced microbial activity thus produced more CO\({}_{2}\), so the intermittent aeration modes could reduce the TOC loss. However, this result was the opposite of [PERSON] et al. [18], who reported the intermittent aeration could result in higher TOC loss than continuous aeration strategies. Although the CH\({}_{4}\) loss of I2 treatment was the highest among the three treatments, its proportion in TOC loss was very small. Meanwhile, the CO\({}_{2}\) emission of C1 treatment was the highest, so its TOC loss was the highest.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### NIST SP 800-22 Test Suite  
The NIST SP 800-22 test suite consists of a set of statistical tests developed by the National Institute of Standards and Technology (NIST) [1]. It is used by researchers to evaluate the randomness characteristics and strength properties of a sequence to determine if they are sufficiently random. The test suite consists of 15 statistical tests, each of which generates a \(p\)-value in the range of (0, 1), where a \(p\)-value of 0 indicates that the sequence is not random at all, and a \(p\)-value of 1 indicates that the sequence is completely random. In image encryption, the test suite is used to evaluate the randomness of the produced cipher image. For conducting these NIST tests, the image must be first transformed into a bit stream, after which the test suite can process the data and produce results for each test. If the \(p\)-value of a test is greater than a significance level of \(\alpha=0.01\), then the null hypothesis is accepted; that is, the sequence is considered random and passes the test. If the computed \(p\)-value is less than 0.01, then the null hypothesis is rejected; that is, the sequence appears to be non-random.
## 5 Evaluation of STAR COSMIC-2 Wet Profile Retrievals through Comparison with CrIS and ATMS
### Comparison of STAR COSMIC-2 WetPrf with SNPP ATMS Measurements Using CRTM
#### 5.2.2 Comparison Results
The inter-COSMIC-2 RO sensor bias between E1 of COSMIC-2 and the other RO sensors (E2 to E6 of COSMIC-2) can then be calculated with the double-difference method:  
\[\Delta\text{BT}_{\text{STAR/C2(E1)-C2(En)}}=\Delta\text{BT}_{\text{STAR/C2(E1)-ATMS }}-\Delta\text{BT}_{\text{STAR/C2(En)-ATMS}}, \tag{7}\]  
for \(n=2\), 3, 4, 5, 6. In our double-difference method-based comparison, both day and nighttime collocations are simulated and compared. Figure 19 shows the relative BT biases (\(\Delta\)BT\({}_{\text{STAR/C2(E1)-C2(En)}}\)) between COSMIC-2 E1 and E2 to E6 for selected ATMS channels in a zoom-in view of \(\Delta\)BT and \(\Delta\)BT with error bar, respectively. In general, the STAR WetPrf-derived BT differences between five COSMIC-2 (E2-E6) sensors and COSMIC-2 E1 sensor are within \(\pm\)0.1 K for ATMS temperature-sounding channels CH07 to CH11 (peak sounding height from 8.06 to 20.89 km) and water vapor sounding channel CH19-20 (peak sounding height from 3.18 to 4.27 km). The uncertainties of the difference calculation for these two channels are more significant for ATMS CH21 and CH22, with peak sounding heights at 5.58 and 6.66 km. It is also interesting to see the consistency between the two COSMIC-2 RO precision evaluations using CrIS (Figure 16) and ATMS (Figure 19a) as the reference sensor. For example, both evaluations of using CrIS and ATMS as the reference sensors show that COSMIC-2 E4 appears to have the most significant positive BT differences compared to other sensors of COSMIC-2. This can be seen from the inter-RO bias \(\Delta\)BT\({}_{\text{STAR/C2(E1)-C2(E4)}}\) comparisons for wavenumber below 2275 cm\({}^{-1}\) in Figure 16 and ATMS CH07-11 in Figure 19a. This analysis using ATMS as the reference sensor demonstrates the precision consistency among six COSMIC-2 RO sensors, which agrees with the analysis presented in Section 5.1 using CrIS as a reference sensor. Figure 19: Evaluation of precision of STAR COSMIC-2 wet profile retrievals through deriving BT differences of ATMS channels between COSMIC-2 E1 and five other COSMIC-2 sensors by using ATMS as the reference sensor in the double-difference method: (**a**) Zoom-in of BT differences, and (**b**) the BT differences with error bar.
## 5 Experimental Results and Discussion
### Visualization of Embedded Visual Vectors  
A high-dimensional vector is not easy to compare intuitively. In the case of the visual or semantic vectors experimented here, it is difficult to understand how the vector is embedded as a 200-dimensional vector. Therefore, by using the t-SNE dimension reduction methodology, the vectors of 200 dimensions are reduced to two dimensions while maintaining their relative distances and are visualized as shown in Figure 7.  
Figure 7 shows the visualization results using the CIFAR-100 test dataset (seen-classes) and CIFAR-10 test dataset (unseen-classes). For CIFAR-100, the color is expressed based on the 20 super-classes. For CIFAR-10, the color is expressed based on the classes. The left column of Figure 7 visualizes the raw pixel data of the images as it is, while the right column visualizes the embedded visual vectors through the proposed embedding model.  
In Figure 6(a),c which are visualization of raw images, it is difficult to find the relationship between visual vectors and each class. In contrast, Figure 6(b),d which are visualization of embedded vectors from proposed embedding model, shows that visual vectors are embedded more cohesively according to their classes. As shown in Figure 6(b), which is the visualization of the embedded visual vectors from CIFAR-100 test images, the embedding occurs in the meaningful manner per super-classes. This means that the embedding with proposed model performs relatively well on the seen-classes. In Figure 6(d), the visualization of embedded visual vectors from CIFAR-10 test dataset is relatively complicated compared to Figure 6(b), but still a little more meaningful than Figure 6(c), the raw images. Through this visualization, we can get some intuition for the zero-shot learning using the proposed model.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **hit@1** & **hit@2** & **hit@3** & **hit@4** & **hit@5** & **hit@6** & **hit@7** & **hit@8** & **hit@9** & **hit@10** \\ \hline HSL & 0.53 & 0.65 & 0.70 & 0.75 & 0.77 & 0.79 & 0.81 & 0.82 & 0.84 & 0.85 \\ (100 Sen Classes \& 10 Unseen Classes) & 0.44 & 0.53 & 0.59 & 0.63 & 0.66 & 0.69 & 0.72 & 0.74 & 0.76 & 0.78 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hit@k performance on visual-semantic cross-modal retrieval for seen class visual data in CIFAR-100.
## 5 Vulnerability Detection
### Detailed Comparative Analysis on Vulnerability Detection Methods
#### 5.2.4 Vulnerability Repair and Fraud Detection  
A key finding in vulnerability repair and fraud detection research is the importance of achieving high precision and recall. Approaches such as bytecode-based repair [82] focus on patching re-entrancy vulnerabilities at the Ethereum Virtual Machine (EVM) level, thereby bypassing the need for source code modifications. Fraud detection methods [85; 86] leverage heuristic or machine learning-based classification to identify suspicious transactions, thus mitigating financial risks in decentralized applications (DApps). Tools such as sGuard+ [83] automatically repair vulnerable contracts by modifying them to fix known logic errors (e.g., integer overflows) before redeployment. The scalability of these methods is a significant advantage, particularly in diverse smart contract environments, though their dependency on specific models emphasizes the need for continuous improvement and adaptation.
## 2 Materials and Methods
### Key Performance Metrics  
The following key performance metrics were monitored and recorded during the experiments:  
1. Quantum Bit Error Rate (QBER): The QBER measures the errors in the quantum channel. It was calculated by comparing the bits [PERSON] sent and [PERSON] received.
2. Secret key rate: The secret key rate is the rate at which secure keys are generated and distributed between the two nodes. It was calculated by analyzing the bits received by [PERSON] and comparing them with the bits sent by [PERSON].
3. Link visibility: The link visibility measures the signal-to-noise ratio in the quantum channel. It was calculated by analyzing the signal and noise levels in the quantum channel.
# An Outdoor Navigation System for Blind Pedestrians Using GPS and Tactile-Foot Feedback
## 4 Evaluation
### Experiment I: Direction Recognition
#### 4.1.1 Study Participants and Experimental Procedure  
A total of 20 undergraduate students (5 women and 15 men) at Panamericana University (Mexico) took part in the experiment. Their ages varied between 18 and 24 years with an average of 20.5. All participants were normally sighted. None of them had any known tactile sensory or cognitive deficit. All provided their informed consent before participating according to the university ethics guidelines.  
During the experiment, the subjects were comfortably seated wearing the user interface on the right foot. For hygiene, all subjects were wearing socks. Prior to the experiment, they were totally unaware of the details and purpose of the test. General instructions about the task were comprehensively given. A short familiarization time with the user interface was granted. During this time, the participants tested different actuator vibration frequencies and had the opportunity to choose the preferred one. All 20 subjects chose 55 Hz, the actuators' maximum vibration frequency.  
A laboratory assistant operated the remote computer and sent commands to the user interface. The smartphone, cloud server, and navigation software were not required for this experiment.
# A Fuzzy Path Selection Strategy for Aircraft Landing on a Carrier
###### Abstract  
Landing is one of the most dangerous tasks in all the operations on an aircraft carrier, and the landing safety is very important to the pilot and the flight deck operation. Nowadays, the landing safety of carrier aircraft is improved by designing an automatic landing controller and by training the pilot to increase his/her control ability. However, the importance of choosing the landing path has not been investigated thus far. In this paper, the problem of landing path selection for an aircraft carrier is studied as there are several candidates corresponding to different situations. A fuzzy path selection strategy is proposed to solve the problem considering the fuzziness of environmental information and human judgment, and the goal is to provide the pilot with a more reasonable decision. The strategy is in view of the idea of Fuzzy Multi-attribute Group Decision Making (FMAGDM), which has been widely used in industry. Firstly, the background of the landing path selection is given. Then, the factors influencing the decision making are abstracted to build the conceptual model. A TOPSIS-based group decision-making method is developed to denote the preference of each decision maker for each alternative route, and the optimal landing path under the current environment is determined taking into account the knowledge and the weight of both the pilot and the landing console operator (LCO). Experimental studies under different setups, i.e., different environments, are carried out. The results demonstrate that the proposed path selection strategy is validated in different environments, and the optimal landing paths corresponding to different environments can be determined.  
landing; aircraft carrier; landing path; fuzziness; fuzzy multi-attribute group decision making
# Low-Complexity Multi-Size Circular-Shift Network for 5G New Radio LDPC Decoders
## 2 Multi-Size Circular-Shift Network for 5G NR LDPC Decoder  
### 5G NR LDPC Characteristics  
QC-LDPC codes play a crucial role in 5G communications. In the 3 GPP standard meeting, QC-LDPC codes were accepted as the channel coding scheme for 5G eMBB data channel. The brief structure of NR LDPC base graph is illustrated in [8]. In particular, the columns of the base graph are partitioned into three parts: information columns, core parity columns, and extension parity columns. The rows of the base graph are divided into two parts: core check rows, and extension check rows. Specifically, the base matrix consists of five submatrices : (1) submatrix A is reserved for systematic bits; (2) submatrix B, a square matrix with dual-diagonal structure, corresponds to the first set of parity bits; (3) submatrix O is an all-zero matrix; (4) submatrix C corresponds to single parity-check (SPC) rows; and (5) an identity matrix I corresponds to the second set of parity bits. The combination of submatrix A and submatrix B is defined as the kernel, and three submatrices O, I and C are called extensions. This code structure is similar to the Raptor-like extension as described in [20].  
The 3 GPP has agreed to consider two rate-compatible base graphs, BG1 and BG2, which have a similar structure for the channel coding. While BG1 is designed for larger block lengths (\(500\leq K\leq 8448\)) and higher rates (\(1/3\leq R\leq 8/9\)), BG2 is targeted for smaller block lengths (\(40\leq K\leq 2560\)) and lower rates (\(1/5\leq R\leq 2/3\)). The exact usage of base graphs and the definition of the two matrixes can be found in the 5G NR standard specification [4].
## 2 Materials
### XBee Module  
The XBee 900 HP PRO S3 module is a platform that offers wireless connectivity for point-to-point communication or in mesh networks with up to 128 nodes. The device consists of transmitters with configurable power between 10 and 250 mW and an omnidirectional antenna with a gain of 2 dbi. It has a variable transmission rate between 10 and 200 kbps. Its receiver has a minimum sensitivity of \(-110\) dBm, which allows a maximum communication range between modules of up to 6.5 km [34].  
In order to obtain wireless channel measurements, we shipped the XBee device in the UAV and configured the following parameters: unicast communication with IEEE 802.15.4 protocol (ZigBee) [35], transmission power of 250 mW, and interval between sending packets of 300 ms. The UAV went up to the programmed height (8 or 80 m) depending on the specifications of the measurements. During the flight to a specific point without changing the height and with constant speed, the embedded device sends the packages to the BS that measures the received signal.  
The base station consists of a notebook with Intel (R) Core (TM) i5-7200U 2.70 GHz processor, 8 GHz RAM, and 64-bit Windows 10 Home Single Language operating system; a microcontroller; and the XBee module configured to receive the packets, as presented in Figure 1. When the signal arrives at the XBee module, the microcontroller is configured to capture the signal strength of the received packet and send the data to be saved in the computer.  
Figure 2a shows the UAV with the XBee module responsible for sending data. Figure 2b shows the base station composed of the XBee module, the microcontroller, and the computer to power the system and save the data.  
Figure 1: Scheme of reception and storage of the wireless signal measurement data.
## Data  
### Experimental Field Site Information  
The study site is a managed poplar plantation located in a flat area periodically subjected to flooding near the village of Zerbolo in northern Italy (45\({}^{\circ}\) 12' 03.14\" N, 9\({}^{\circ}\) 03' 39.74 E, 60 m a.s.l.). The climate of the site is classified as Humid Subtropical - Mid Mild Latitude (Cfa) - Koppen Climate Classification [26], with yearly average rainfall of 912 mm and mean temperature of 12.5\({}^{\circ}\)C. The site is characterized by high total nitrogen depositions of about 20 kgN ha-1 per year [27]. The soil texture is sandy-loam (60.4% sand, 30% silt and 9.6% clay). The nitrogen and carbon content of the soil, measured to a depth of 100 cm, are 1.36 kgN m-2 and 5.23 kgC m-2, respectively.  
A 27-m scaffold tower was erected in March 2002 in the homogeneous stand of about 46 ha and disassembled in 2005, immediately before logging. The EC flux tower, belonging to the Carboeurope-IP network (site ID: IT-PT1), measured continuously carbon, water and energy fluxes between ecosystems and atmosphere. The plantation was characterized by a spacing of 6 x 6 m and a tree density of 278 trees ha-1. Mean tree height, mean diameter at breast height (DBH) and the stem basal area, measured in 2005, were 26.3 (\(\pm\) 4.5) m (n = 8), 32.9 (\(\pm\) 5.7) cm (n = 266) and 20.45 m2 ha-1, respectively. The leaf area index (LAI) was measured during the growing season every two weeks with LAI-2000 PCA plant canopy analyzer (LI-COR Inc., Lincoln NE, USA) as described in [28]. The LAI showed a seasonal variability with a maximum value of about 2.0 m2m-2 reached generally in July. The mean specific leaf area (SLA) of poplar leaves was 12.3 (\(\pm\) 1.8) m2 kgC-1. SLA was estimated by extracting a known sub-area from the leaves collected during the growing season. Three leaves, at two different canopy levels (bottom and top), from three plants around the flux tower and from three plants located in the nearby stand were sampled. The leaves were collected every month from May to August for a total of five sampling dates.  
The maximum taproot length reached about 1.5-1.7 m [29], while the soil depth was limited by the position of the water table at about 2.0 m.
# Sigma Routing Metric for RPL Protocol
## 6 Results  
After validating the technical configurations in each scenario, the simulation performance were made. The purpose of this section was to analyze the results of the simulations. A special coding was made in the Cooja simulator to obtain the complete results of each scenario, which can be consulted in the information shown below.  
By creating a simulation with Cooja in Instant Contiki 3.0, a comparison was made between the newly proposed objective function SIGMA-ETX and these three functions: OF0, MRHOF, and PH-ETX. Several simulations were performed, between 20, 40, 50 and 100 client nodes and 1 root or receiver node.
## 3 Results
### Influence of Tissue Thickness Underneath the Measured Surface on Measured Spectrum
#### 3.3.3 Spectral Comparison after Wavelength Reduction  
Figure 10 shows the selected SNV normalized diffuse reflectance spectra after wavelength reduction. In comparison with Figure 7, indeed the spectra are more similar between the tissue slices dataset and the lumpectomy data. In the NIR (>1176 nm), the spectra of the slices and lumps are most similar with an SCM of 1.00, 1.00 and 0.99 for IC, connective and adipose tissue, respectively. The maximum difference between the spectra was 0.31, 0.11 and 0.31. In the VIS (<602 nm), spectra from the slices dataset and the lumpectomy dataset deviate more with an SCM of 0.92, 0.93 and 0.98 for IC, connective and adipose, respectively, and a maximum spectral difference of 1.04, 0.70 and 0.42. In addition, for IC around 1200 nm, the shape of the lumpectomy spectra is steeper which would indicate that these spectra contain more fat. This corresponds with the fat percentage in the H & E sections: 19%, 9%, and 6%.  
After wavelength reduction, the spectra of slices and lumpectomy data are more similar (Figure 7 vs. Figure 10). Therefore, in Section 3.4.1, hyperspectral analysis was only performed using wavelengths with a penetration depth up to 2.5 mm.  
Figure 10: Spectra of IC (**a**,**d**), connective (**b**,**e**) and adipose (**c**,**f**) tissue obtained on the slices and the lumpectomy specimens after wavelength reduction. The gray areas represent the excluded wavelengths. The first and second rows demonstrate the spectra before and after the SNV normalization, respectively. The SNV normalization was applied separately on the wavelengths obtained with the visual and wavelengths obtained with the near-infrared camera.  
Figure 9: The diffuse reflectance (**a**) and penetration depth (**b**) calculated using the estimated optical properties, shown in Table 3. Because the diffuse reflectance and penetration depth of light vary both with wavelength and tissue composition, there is a large standard deviation around the mean spectra.