esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Sedimentation Rates  
Cesium-137 activity was found in five of the seven locations sampled; however only the riverine environments exhibited the archetypal \({}^{137}\)Cs peaks. Values of \({}^{137}\)Cs found within our study location are low when compared to published values; values from this study ranged from \(-\)0.02 to 0.30 pCi g\({}^{-1}\) in the disturbed basin and riverine, respectively. The reference basin and dwarf mangroves did not show any \({}^{137}\)Cs activity in the soil samples. For other locations, \({}^{137}\)Cs peaks were identified at 36 cm in the disturbed basin, 14 cm in the reference fringe, 18 cm in the disturbed fringe, and 16 cm at both riverine environments. Accordingly, sediment accretion rates calculated with \({}^{137}\)Cs were estimated to be 7.34 mm\(\cdot\) year\({}^{-1}\) in the disturbed basin mangroves, 2.85 mm\(\cdot\) year\({}^{-1}\) in the reference fringe mangroves, 3.26 mm\(\cdot\) year\({}^{-1}\) in the disturbed fringe mangroves, and 3.67 mm\(\cdot\) year\({}^{-1}\) in both riverine mangroves (Table 2).  
Sediment accretion rates utilizing the \({}^{210}\)Pb constant rate of supply method were lower than rates calculated with \({}^{137}\)Cs (Table 2; Figures 5 and 6). Sediment dating with \({}^{210}\)Pb was subsequently used to estimate carbon sequestration because of the lack of defined \({}^{137}\)Cs peaks at most of the sites (Figure 5) and \({}^{137}\)Cs mobility in marine systems. Sedimentation rates within our two tidal creeks were found to  
Figure 4: Soil carbon pools (mean \(\pm\) standard error) of different mangrove landscapes for hydrologically disturbed Hamilton Avenue Creek (HA) and reference Susan’s Creek (SC). Carbon pools have been calculated to a standard 20 cm depth.  
be 1.68 and 1.58 mm\(\cdot\) year\({}^{-1}\) in the basin mangroves, 2.28 and 2.23 mm\(\cdot\) year\({}^{-1}\) in the fringe mangroves, and 5.43 and 3.04 mm\(\cdot\) year\({}^{-1}\) in the riverine mangroves, for the reference (SC) and disturbed (HA) creeks, respectively. Sedimentation rates were 1.41 mm\(\cdot\) year\({}^{-1}\) in the dwarf mangroves that were found only in the reference creek.  
Figure 5: Cesium-137 activity (pCi g\({}^{-1}\)) graphs with depth for estimating sedimentation in mangroves adjacent to Naples Bay. Figures are shown for (**a**) riverine reference mangroves; (**b**) riverine disturbed mangroves; (**c**) fringe reference mangroves; (**d**) fringe disturbed mangroves; (**e**) basin reference mangroves; (**f**) basin disturbed mangroves; and (**g**) dwarf reference mangroves.
## 3 The Time Source Techniques
### Global Navigation Satellite Systems
#### 3.1.2 Accuracy  
The accuracy of GPS, or the potential error in its measurements, is influenced by various environmental factors [11]:  
* Satellite Orbit Error: Inaccuracies in a satellite's reported location can impact the GPS receiver's position and time calculations.
* Satellite Clock: Although GPS satellites use atomic clocks, these clocks are still subject to drift and noise. While some of this error is corrected within the signal message, residual errors may persist.
* Ionosphere and Troposphere Delay: GPS signals experience delays due to free electrons in the ionosphere, which vary based on the satellite's position relative to the horizon. Closer to Earth, signal delay is affected by temperature and humidity changes in the troposphere. While receivers partially compensate for these delays, some variation remains uncorrected.
* Multipath: GPS signals can reflect off buildings or other obstacles, leading to delays in the signal.
* Receiver Noise Error: Receiver noise can cause jitter in the signal, resulting in timing errors.  
Depending on environmental conditions and device sensitivity, GPS time accuracy can vary, though it is typically within 100 ns [43].
# Case Study on Secondary Building Materials
## 2 Materials and Methods  
### C&D Waste  
In the recycling processes of C&D, waste crushers and sieves come into operation. The resulting fine fraction, crushed sand, is often excluded from utilization as an aggregate for concrete production [9] and is instead used for lower-ranked applications such as sub-base layers, dams, and walls. The reasons for this include the residual attachments of the cement matrix, adverse grain shape, and comparatively higher content of hazardous substances (e.g., PAHs or sulfates) [23]. For the study presented in this article, two materials, from construction and demolition, were investigated: crushed sand (CD-1) and material from a C&D waste treatment facility in Berlin (CD-2).  
One material (CD-1) was delivered from a company in the C&D waste recycling sector in southern Germany. It was a contaminated crushed sand that could not be recycled economically and was a by-product of the crushing process--a recycled material with the EWC code 17 01 07 with app. 35% of the total material being contaminated. The contaminant content here was so high that landfilling was necessary. The material was processed in a pilot scale we treatment plant with a jig for density separation (Alljig P400/600 \(\times\) 800, Company Allmineral, Duisburg, Germany) [23]. The objective was to enrich the contaminants in a marginal fine fraction and to reach quality level RC-1 for the heavy fraction. Target impurities were sulfates and PAHs. leachates were prepared using column percolation according to DIN 19528:2009-01 up to a liquid to solid (L/S) ratio of 2 L/kg. Analyses of PAH and sulfate within the leachates and the solids was performed using GC-MS (Agilent 5973 with quadrupol GC/MS detector) and ion chromatography (Dionex Integration, Thermo Fisher Scientific, Dreieich, Germany).  
The second material (CD-2) was a typical urban construction and demolition waste. It was processed on a mobile pilot scale jig (Triple A, Company AGS, Schwentimental, Germany [24]), generating a light fraction and a heavy fraction. No chemical analyses were performed with the material. Manual sorting of the heavy fraction <32 mm into the categories mortar, stones, glass, bricks, ceramics, and metals was performed and evaluated.
## 2 Proposed Method  
A flowchart of the proposed model for RUL prediction is shown in Figure 1. The entire approach consists of three parts: data pre-processing with ELM model construction, adjusting factor gain, and transfer learning-based RUL prediction.  
1. Data pre-processing with ELM model construction: The degradation rate data are calculated from the original remaining capacity data. Then, the beginning and ending points are marked on the capacity data of the historical group. After that, a typical three-layer ELM is used to construct the relationship between the degradation rate obtained in data pre-processing and the remaining capacity of historical data;  
Figure 1: Flowchart of the proposed method.  
2. Adjusting factor gain: The ELM model constructed in step (1) is utilized on the known part of degradation rate data for the target group. The output remaining capacity is compared with the real remaining capacity data of the target battery, and the adjusting factor is calculated according to their differences;
3. Transfer learning-based RUL prediction: Several ELMs are used to construct the relationship between the remaining capacity and RUL of historical data. They share different weights according to their performance, which helps with the construction of the final ELM model. The final ELM is applied to the data of the target battery to predict the RUL. Then, the result is adjusted by the adjusting factor obtained in step (2) and then becomes the final prediction results of the RUL.
## 4 Angle Selection and Ray Categorization
### Underwater Regions Based on Ray Types
The first is identified by an angle that generates the furthest underwater differentiable LEP. The second ray is obtained by identifying the ray with the furthest non-differentiable LEP achievable within the SSP. Therefore, the permanent shadow zone is only created in two SSP conditions where the first requires the SSP between a node and the reflection surface to be on the average an increasing function and thus producing LEPs along the entire depth of the SSP column. The second condition is when the SS gradient increases in value and then shifts and becomes either constant or on the average a decreasing function indicating that differentiable LEPs will be possible at the depth at which the shift occurs. Figure 10: Demonstration of different regions generated in underwater environment where SSP of the water column dictates the boundary of each one. The second region is where the direct ray resides and is always bounded by the transmitter node's \(x\)-axis and \(z\)-axis as well as the contour \(\lambda_{LEP}\). For the third region, direct rays cannot reach while rays facing a differentiable LEP can. The final region is located between all three regions mentioned above where only surface reflected rays can reach this region. Moreover, reflected rays can be found in all regions except the permanent shadow zone. Finally, not all regions exist in each underwater environment where distinct SSPs may have fewer regions when comparing to others.
# Voting-Based Contour-Aware Framework for Medical Image Segmentation
## 2 Method
### Backbone Network  
Due to the outstanding performance of encoder-decoder network architecture in medical image segmentation, we adopted the nnU-Net and Segnet [42] as backbone networks in our framework. The detailed network configurations for the two datasets in our paper are shown in Tables 1 and 2. Because the nnU-Net can automatically configure its network architecture to adapt to different datasets, the nnUNet has seven resolution stages for the CHAOS dataset and four resolution stages for the hippocampus dataset. There are two convolution blocks for each resolution stage in the encoder and decoder. Each block contains a convolution layer followed by an instance normalization and Leaky nonlinearity. Strided convolutions are utilized to downsample the features in the contracting path. The upsampling uses transposed convolutions to enlarge the feature maps in the expanding path. In our experiment, the Segnet has five resolution stages for the CHAOS dataset and  
Figure 1: Illustration of two-stage segmentation framework. (**a**) Architecture of our contour-aware CNN in the first stage. (**b**) Detailed structure of the contour enhancement module. (**c**) Stage of voting strategy, where the voting committee filters the samples for the target network.  
four resolution stages for the hippocampus dataset. In the first two resolution stages, each resolution stage contains two convolution blocks for both the encoder and decoder, with each block containing a convolution layer followed by a batch normalization and a ReLU layer. The encoder and decoder each have three convolution blocks for other resolution stages. Max-pooling is used for downsampling to achieve translation invariance. The decoder uses the max-pooling indices to upsample the feature maps from the corresponding resolution encoder. The difference between the nnU-Net and Segnet is that the nnU-Net use the entire feature maps to concatenate them with the related decoder feature maps. At the same time, Segnet passes the pooling indices to the corresponding decoder.
# Community Detection Framework Using Deep Learning in Social Media Analysis
## 4 Experiments and Analysis
### Data Description  
There are many communication data on social networks, such as Twitter and Facebook. However, these data structures are scattered, difficult to seize, and contain much irrelevant content, lacking credibility. We comprehensively consider the data source, size, whether it is related to crime and other conditions, and finally choose to analyze the Enron email dataset. The Enron email dataset remains valuable for encapsulating communication patterns and relational dynamics similar to those observed in social networks. Research [45] has shown that interpersonal interactions within email communications can illuminate social structures, revealing underlying community dynamics and network connections. The Enron email dataset, characterized by its extensive collection of rich interpersonal exchanges [46], provides a suitable foundation for investigating community structures, thereby aligning with the fundamental principles of community detection that underlie our study. Such datasets offer an opportunity to analyze the formation and evolution of communities, facilitating a deeper understanding of communication networks similar to those found in contemporary social media environments.  
Enron was a global natural gas and energy trader and supplier corporation. In 2001, the company encountered various severe financial difficulties and filed for bankruptcy in a few weeks. Subsequent investigations revealed that company executives were engaged in financial crimes such as fraud, conspiracy, and falsifying financial records. As email communication was prevalent in Enron, analyzing the email dataset has become a crucial approach for investigation. Ultimately, 28 executives were found guilty, and CEO [PERSON] was sentenced to 120 years in prison. After the case was closed, the email dataset was released online.  
The email dataset is characterized by the presence of irregularities and redundancies, which necessitate a rigorous pre-processing regimen. Our pre-processing endeavors aim to structure the dataset comprehensively, culminating in fields such as message ID, date, sender, recipient, subject, CC information, and the actual content of the emails. Regarding the encoding of user IDs to serve as input data for the community detection framework, we have transformed the dataset by encoding the email addresses. Upon completion of these processes, the dataset comprises a total of 21,452 nodes and 517,401 email records.
## 5 Applications of BC-based Photocatalysts
### Bismuth-BCPs  
Bismuth-based photocatalyst has attracted considerable interest in wastewater treatment for its excellent photo-oxidation ability, unique electronic structure and toxic-free properties [107]. Very recently, [PERSON] et al. [64] prepared bismuth impregnated biochar composites for efficient estrone degradation. They found the \(K_{obs}\) was more than 20 times than that of pristine Bi/Bi\({}_{2}\)O\({}_{3}\). The promoted result was owing to the \(\cdot\)OH radicals generated through the synergistic effect between BC and Bi/Bi\({}_{2}\)O\({}_{3}\). [PERSON] et al. [107] synthesized Bi\({}_{2}\)O\({}_{3}\)/ZnAl-LDH-BC composite, which displayed an excellent performance in photocatalytic degradation of contaminations under visible light. Owing to the synergistic effects between precursors, the \(E_{g}\) of the BCPs was lower (2.42), compared with Bi\({}_{2}\)O\({}_{3}\) and ZnAl-LDH (2.75 and 2.9, respectively). It was found that the light absorption and the separation of e/h\({}^{+}\) pairs was expedited. Moreover, organic contaminants include afranine T, brilliant green, RB and 4-nitrophenol exhibited optimal stability (Figure 8). The BCPs hold great potential in the field of wastewater treatment.  
Figure 7: UV-visible diffuse reflectance spectra of BC-TiO\({}_{2}\). Reproduced with permission from reference [41].  
[PERSON] et al. [60]'s research showed 97.4% of methylparaben was removed in 2 h under natural sunlight by BC-Fe\({}_{3}\)O\({}_{4}\)-BiVO\({}_{4}\) composite. The introduction of BC brings high surface area, which facilitated the degradation via radical attack and expedited the radical generation. [PERSON] et al. [38] synthesized BC/Fe\({}_{3}\)O\({}_{4}\)/Bi\({}_{2}\)O\({}_{3}\) photocatalyst using corn cobs char. Compared with pure Bi\({}_{2}\)O\({}_{3}\), BC and BC/Fe\({}_{3}\)O\({}_{4}\) showed notable red-shift and a broad absorption region. The degradation rate of tetracycline by BC/Fe\({}_{3}\)O\({}_{4}\)/Bi\({}_{2}\)O\({}_{3}\)-10 was 2.5 times greater than the bare Bi\({}_{2}\)O\({}_{3}\). [PERSON] et al. [108] found that the increased visible-light absorption was owing to the unique biochar-based heterojunction structure. Bismuth oxyhalides (BiOX) exhibited higher photocatalytic activity than anatase TiO\({}_{2}\) due to its unique layered structure interleaved with [Bi\({}_{2}\)O\({}_{2}\)] slabs and double halogen atoms slabs [51]. [PERSON] et al. [109] fabricated a magnetic Fe\({}_{3}\)O\({}_{4}\)/BiOBr/BC (reed straw) composite with the visible-light response via a one-step hydrolysis route. The BCPs exhibited better photocatalytic activity for carbamazepine removal (95.51%), owing to the promoted separation of e/h\({}^{+}\) pairs.
## 3 Related Works
### Lack of End-to-End Security in MQTT  
Concerning security in MQTT, the studies in the literature mostly focus on the client-to-broker segment, while the client-to-client (end-to-end) security in MQTT is typically neglected. Some of the few proposals in this area primarily focus on ensuring end-to-end confidentiality, whose goal is to protect the messages so that an untrusted broker, mediating the communication, cannot access their content.  
Among these, some works leverage symmetric key cryptography for securing MQTT communications [32; 51]. These solutions often involve protocols that enable authorized MQTT clients to exchange symmetric keys securely. For instance, [32] introduces a lightweight protocol (designed for constrained devices) that achieves both message confidentiality and client authentication. However, [51] focuses on establishing secure end-to-end key exchange mechanisms. This protocol allows MQTT clients to securely exchange keys to ensure that messages can be encrypted and decrypted without relying on a trusted broker.  
Several studies have proposed comprehensive frameworks to address both end-to-end authorization and message confidentiality. Refs. [52; 53; 54; 55] provide solutions that ensure only authorized clients can access the messages they are authorized to receive. These frameworks often involve complex mechanisms to manage and verify client authorizations, along with a secure key exchange process to guarantee the end-to-end confidentiality of the exchanged messages.  
Despite this, these solutions commonly rely on third-party trusted entities to manage keys and enforce authorization policies. Such dependencies can often hinder the adoption of these solutions in real-world scenarios. Furthermore, third-party entities can represent single points of failure [56]. Indeed, if these entities are compromised or unavailable, the client communications may be potentially exposed to unauthorized access [57]. Finally, these solutions may suffer from scalability issues, especially in large-scale IoT deployments where thousands of devices may be interacting simultaneously [58].  
On the other hand, the solution we propose in this paper offers end-to-end security guarantees without relying on third-party parties. The only proposal in the literature not relying on such parties is [9]. Similar to [9], our proposal is also aimed at achievingend-to-end integrity of the flow of messages exchanged via an MHT-based approach. However, differently from [9], our solution also achieves accountability guarantees both on the publisher and the broker sides.
# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 3 Methods
### Motion Correction Strategies
#### 3.3.4 Handled Plane  
As depicted in Section 2 and, specifically, Figure 1, the planes in the 3D-OCT volume were defined according to the scanning direction. As a consequence of various causes, such as the increase in the acquisition speed or the refinement of the registration algorithms, the planes handled in retinal motion correction change between studies. Thus, a possible classification of the retrospective approaches can be made taking into account the planes used to drive the registration.  
_Fast_: The early TD-OCT devices only allowed to acquire a few B-scans within a reasonable exposure time. Thus, the first applied corrections used the information on the fast plane and at the A-scan level (\(F_{A}\)). As the acquisition speed increased, the registration methods that used the information on the fast plane switched to correct misalignments between adjacent B-scans (\(F_{B}\)).  
_Slow_: Motion artifacts are easily observed in the slow plane of an unregistered 3D-OCT volume. A correction can be done by adjusting the smoothness constraints to a wavy profile. This correction can be applied to adjacent A-scans (\(S_{A}\)) or B-scans (\(S_{B}\)) when orthogonal data are available.  
_Coronal_: Lastly, there are some studies [49; 50] that have opted for the correction of the fast and slow axis simultaneously, using the coronal plane (\(C\)). This is commonly linked to multimodal acquisitions, although it is not a necessary condition, as it can be performed by means such as adding smoothness or continuity constraints to the vessels in the en face image.
## 3 Results
### NIRS Calibration  
A calibration model was developed for dry matter, starch, and protein with the laboratory analysis data obtained using standard wet chemistry methods and the near-infrared spectra of the samples. The coefficient of prediction (R\({}^{2}\)pre) was used to evaluate the performances of the models; this indicates the percentage variation in spectra data that is accounted for by the chemical constituents of the samples and other statistical variables such as SEC, SECV, RPD, and SEP. The standard error of calibration is SEC, while SECV is the standard error of cross-validation; the ratio of performance to deviation (RPD = SD/SECV) was also used for performance evaluation. For fresh chopped samples, R\({}^{2}\)pre of \(0.78\%\) for starch, \(0.61\%\) for dry matter, and \(0.04\%\) for protein were obtained. Grated samples had R\({}^{2}\)pre of \(0.15\%\) for starch, \(0.84\%\) for dry matter, and \(0.50\%\) for protein, while samples presented as blended had an excellent prediction performance for dry matter (R\({}^{2}\)pre = \(0.95\%\)), and R\({}^{2}\)pre of \(0.83\%\) for starch, and a very poor R\({}^{2}\)pre of \(0.26\%\) for protein (Figures 2-4).