esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Durability of Wood Treated with Nano Metal Fluorides against Brown-Rot and White-Rot Fungi
## 5 Conclusions  
Synthesis of low-water soluble metal fluorides in their nanoscopic form has opened the possibility of assessing their biocidal efficacy against wood-degrading fungi. NMFs have a low risk on human cell viability as observed from cytotoxicity assays that makes the resultant treated wood products safe for various applications in construction. The advantage of magnesium fluoride and calcium fluoride in comparison to conventional fluoride-based wood preservatives is their low water solubility. Results in the present study confirmed that NMFs were present in treated wood after accelerated ageing. More importantly, it is reported that wood treated with nano metal fluorides and leached had mass losses below 3% against brown-rot and white-rot fungi. Therefore, the NMF treatment of wood is considered to be durable according to EN 113 (1996) test. The results here demonstrate the viability of low-water soluble fluorides synthesized in their nanoscopic form to provide wood protection. In summary, their high biocidal efficacy, low susceptibility to leaching, and reduced risk to human health and environment demonstrates their potential to become the next generation of fluoride compounds for use in wood protection above ground.  
**Author Contributions:** Conceptualization, S.M.U., E.K. and T.H.; methodology, S.M.U., L.V., E.K., I.S. and T.H.; software, S.M.U. and L.V.; validation, S.M.U. and L.V.; formal analysis, S.M.U.; investigation, S.M.U. (sol-synthesis, nanoparticle and treated wood characterization, _in-vitro_ agar test, wood treatment, leaching, and fungal tests) and L.V. (cytotoxicity assays); resources, E.K., I.S. and T.H.; data curation, S.M.U.; writing--original draft preparation, S.M.U.; writing--review and editing, S.M.U., L.V., E.K. and I.S.; visualization, S.M.U.; supervision, E.K., I.S. and T.H.; funding acquisition, E.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We thank [PERSON] for support with the synthesis of CaF\({}_{2}\) and MgF\({}_{2}\) sol, [PERSON] for support with the _in-vitro_ agar and wood decay testing, [PERSON] for acquiring the X-ray micro-CT images, [PERSON] for acquiring the HAADF-STEM images, and [PERSON] for measuring the MAS \({}^{19}\)F NMR spectra of NMF treated wood specimens. The results of this paper have been also published in the form of doctoral dissertation of S.M.U. at the Humboldt-Universitat zu Berlin.  
The authors declare no conflict of interest.
## 3 Design Process
### DAid(r) Smart Socks-Based Biofeedback System Parameter Selection
#### 3.3.3 Information Flow  
Overall information flow: For the intended application, in the first iteration there were up to three participants simultaneously using the system. Each participant wore a pair of smart socks, thus there were data flows from six socks simultaneously.  
The number of issues were considered while researching different connection types between sensors and application, application, and headset. Different communication technologies exhibit varying levels of latency which can impact real-time applications. For instance, TCP (Transmission Control Protocol) can experience high latency due to delays in data transfer, which is critical in applications requiring immediate data updates, such as the given food plantar changes monitoring.  
BLE (Bluetooth Low Energy) is optimized for low-latency communication, making it suitable for timely sensor data updates. However, in congested environments or with multiple devices connected, latency issues can still arise.  
After performing the research, we decided to use Bluetooth. It was chosen for its higher bandwidth compared to Bluetooth Low Energy (BLE), making it more suitable for managing the larger volumes of sensor data generated during high-frequency monitoring of foot pressure and movement. The system requires the transmission of continuous, real-time data, with each sensor sending 3-4 data points per package, and Bluetooth can support this without the bandwidth limitations of BLE.  
Wi-Fi was chosen as the main communication between the HMD and computer. However, Wi-Fi cannot be used as the data transfer source between sensors and computers as it can affect latency, impacting the speed at which data are displayed and commands are sent as high latency can disrupt applications needing real-time data.  
This balance between Bluetooth for sensor communication and Wi-Fi for data transfer to the HMD ensures the system meets the real-time requirements of biofeedback applications while minimizing latent issues.
## 2 Methodological Background
### Particle Swarm Optimization (PSO)
#### 2.3.1 Algorithm of PSO  
Assume that the \(i\)th particle among the group can be represented as \(X_{i}=(x_{i1},\,x_{i2},\,x_{i3},\,\ldots,\,x_{id})\) in a \(d\)-dimension space. The best previous positions of the \(i\)th particle can be represented as \(\text{pbest}=(\textit{pbest}_{i1},\,\textit{pbest}_{i2},\,\textit{pbest}_{i3 },\,\ldots,\,\textit{pbest}_{id})\). The best particle in the group can be represented as gbest. The velocity of the \(i\)th particle can be represented as \(V_{i}=(v_{i1},\,v_{i2},\,v_{i3},\,\ldots,\,v_{id})\). The updated formula for the particle velocity and particle position can be expressed as \(v_{im}{}^{t+1}\) and \(X_{im}{}^{t+1}\), respectively.  
\[v_{im}{}^{t+1}=w^{\star}v_{im}{}^{t}+C_{1}{}^{\star}rand^{\star}(\textit{pbest} _{im}-X_{im}{}^{t})+C_{2}{}^{\star}rand^{\star}(\textit{gbest}_{im}-X_{im}{}^{t}) \tag{6}\]  
\[X_{im}{}^{t+1}=X_{im}{}^{t}+v_{im}{}^{t+1}\text{ for }i=1,\,2,\,3,\,\ldots,\,n \text{ and }m=1,\,2,\,3,\,\ldots,\,d \tag{7}\]  
where  
\(n\): number of particles.  
\(d\): dimension.  
\(t\): number of iterations.  
\(v_{im}{}^{t}\): velocity of particle at iteration \(t\).  
\(w\): inertia weight factor.  
\(C_{1}\), \(C_{2}\): acceleration constant.  
_rand_: random number between 0 and 1.  
\(X_{im}{}^{t}\): current position of \(i\)th particle at iteration.  
pbest: best previous position of the \(i\)th particle.  
gbest: the best particle among all the particles in the swarming population.  
The flowchart of PSO is shown in Figure 3.
# Vascular Structure Identification in Intraoperative 3D Contrast-Enhanced Ultrasound Data
## Materials and Methods
### Validation  
One key point in image registration is the similarity measure. The Normalized Cross Correlation (NCC) and the Normalized Mutual Information (NMI) are typical similarity measures commonly used [32; 33]. The NCC computes pixel-wise normalized cross-correlation between two images and usually aims at registering images of the same modality. The NMI describes how well one image can predict the other one and is, therefore, suitable for multi-modality registration. Robust and easier to interpret, the Normalized Gradient Field (NGF) measures a normalized distance between the gradients of the images to be registered [34; 35]. Some works proposed calculating the similarity measures from binary data, arguing that the registration process is, in this way, accelerated [36; 37; 38; 39]. These three similarity measures were used for comparison in the registration algorithm. In order to validate the method, a neurosurgeon visually checked that the vascular segments, automatically identified in the 3D-iCEUS\({}_{start}\) and 3D-iCEUS\({}_{end}\), corresponded to the blood vessel selected in the cT1 MR data. Moreover, the results of the algorithm were quantitatively compared with a registration, interactively performed, using the Dice Similarity Index (DSI) and the Hausdorff distance. The DSI represents the percentage of overlapping of two objects. Its value is 1 if they overlap perfectly and 0 if they do not intersect at all. The Hausdorff distance represents the maximal distance between two objects. The computing time of the registration process was also measured for each similarity measure. The NCC, NMI and NCC measures were alternately computed by using the following equations:  
\[NCC=\frac{1}{\sigma_{x}\sigma_{y}}\sum_{i}(x_{i}-\bar{x})(y_{i}-\bar{y}) \tag{7}\]  
\[NMI=\frac{H(X)+H(Y)}{H(X,Y)} \tag{8}\]  
where \(x_{i}\) and \(y_{i}\) are respectively the set of points of images \(X\) (fixed image) and \(Y\) (moving image). In addition, \(\bar{x}\) and \(\bar{y}\) are the mean of \(X\) and \(Y\); and \(\sigma_{x}\) and \(\sigma_{y}\) are their standard deviation. \(H(X)\) and \(H(Y)\) are the entropies of random variables \(X\) and \(Y\), and \(H(X,Y)\) represents their joint entropy.  
\[n(I,x):=\left\{\begin{array}{ll}\frac{\
abla I(x)}{||\
abla I(x)||}&\
abla I (x)\
eq 0\\ 0&\text{otherwise}\end{array}\right. \tag{9}\]  
Figure 4: Workflow of the proposed method.  
\[d^{c}(Y,X)=||n(X,x)\times n(Y,x)||^{2} \tag{10}\] \[d^{d}(Y,X)=<n(X,x),n(Y,x)>^{2} \tag{11}\]  
where \(n(I,x)\) is a regularized normalized gradient field of a given image \(I(x)\). Then, \(d^{c}(Y,X)\) and \(d^{d}(Y,X)\) represent the distance between the gradients of images \(X\) and \(Y\).  
Besides, the Dice Similarity Index (DSI) and the Hausdorff distance are described as follows:  
\[D(X,Y)=\frac{2|X\cap Y|}{|X|+|Y|} \tag{12}\]  
\[d_{\rm H}(X,Y)=\max\bigg{\{}\max_{x\in X}\min_{y\in Y}\|x-y\|,\,\max_{y\in Y} \min_{x\in X}\|y-x\|\bigg{\}} \tag{13}\]  
where \(X\) is the fixed image and \(Y\) the moving image.
# Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities
## 4 Experimental Results  
In this section, we present experimental results on data collected from XGC simulations. Compressing all the data generated from XGC simulations is unrealistic due to extremely large storage requirements. To give a sense of scale, a simulation model of the ITER-scale [42] problem typically contains trillions of particles, runs for thousands of timesteps, and can produce over 200 petabytes of data each day. Hence, we compress discretized data of XGC simulations. We first apply our algorithm to the 420 th timestep of XGC data, which consists of eight cross-sections and 16,395 nodes per cross-section. Each node represents a 39 \(\times\) 39 2D velocity histogram of electrons. Other XGC time point simulation data are deployed as test data, with results shown using the pretrained machine. All the experiments are performed on Summit nodes in the Oak Ridge National Laboratory. The Summit nodes contain POWER9 processors with 512 GB of DRAM and NVIDIA TESLA V100 GPUs.
# Imaging Microstructure on Optically Rough Surfaces Using Spatially Resolved Acoustic Spectroscopy
## 4 Discussion and Conclusions
### Temperature  
As discussed, the ability to make SRAS measurements on rough surfaces raises the possibility of integrating SRAS within the manufacturing process, which increases the final challenge discussed in this work, i.e., temperature. The acoustic velocity of material is sensitive to the temperature, and thus the use of laser ultrasound in steel mills, L-PBF chambers, and direct energy deposition (DED) systems requires calibration of the temperature. By way of example, the SAW velocity on the (001) plane of nickel at temperatures between 100 and 760 K displays a near-linear trend of \(-\)0.4 ms\({}^{-1}\) K\({}^{-1}\). Thus, a temperature instability of a few degrees can become the limiting factor in the accuracy velocity measurements and orientation determination.  
Experimental results presented in this work have been at nominal room temperature, but this was not controlled and probably spans a range of between 5 and 10 \({}^{\circ}\)C. To work at higher temperatures would require accurately measuring the temperature during a scan so that the temperature effect can be calibrated.
# Path Loss Measurements and Model Analysis in an Indoor Corridor Environment at 28 GHz and 38 GHz
## 3 Path Loss Measurements and Models
### NLOS Measurement Evaluation: Study Results and Discussion
Depolarization occurs as a result of this. The range value for the \(\alpha^{\text{FI}}\) model ranges from 16.22 at 4.5 GHz to 120.5 at 73 GHz [39], with 16.22 being the lowest number and 120.5 being the highest. Our work's lowest value of \(\alpha^{\text{FI}}\) is 65.11 at 38 GHz for V-V polarization, but it rises to 87.81 at 28 GHz for V-H polarization, which is still within the acceptable range for the works we took into consideration. The values in our work, however, fell within the range of the value \(\mathbf{\beta}^{\text{FI}}\) (documented in both upper ranges) [50]. The values of additional parameters, such as \(\alpha^{\text{CI}}\) and \(\alpha^{\text{FI min}}\), were congruent with those reported in the literature, after carefully contrasting the path loss parameters from the work with those from the literature. The majority of the work's ranges were consistent; however, a key result was that these parameters differed based on the indoor environment. This is because different indoor settings in the literature were constructed from various materials, which caused the multipath components to vary and, thus, impacted the received signals. Different building materials and obstacles have varying penetration losses, dispersions, and fading. It is possible that some situations could experience signal attenuation because of air concentration, which can also be the reason for the various path loss propagation parameters. The comparison results show that almost all of the path loss parameters in our work were within the range of the works considered in the literature. The main accomplishment was the better analysis of our work, which resulted in superior results in the PLE and standard deviation values. These two factors are critical when assisting engineers and researchers in budget calculations in 5G wireless network propagations in indoor environments. The study's main goal was to examine single-frequency path loss models in an indoor environment at frequencies of 28 and 38 GHz. A thorough comparison of the path loss parameters was also performed to compare the values of the work's parameters with those in the literature. Although the ranges were consistent with the majority of the work, one major finding was that these parameters varied depending on the indoor environment. This is due to the fact that various indoor environments in the literature were made of different materials, causing the multipath components to differ and, thus, affecting the received signals. Each obstacle and building material had different penetration losses, scattering, and fading. However, more measurements in these frequency bands will be required in the future, taking into account the losses of building materials and obstructions in each indoor environment. This will aid in the conception of a building structure-specific model.
## 2 Materials and Methods
### Concept Model  
The SNOMED CT Concept Model defines which combinations of Concepts and Attributes are allowed for concept definitions and PCEs. Therefore, the domain and range for each of the current 125 Attributes in SNOMED CT are specified.  
The domain refers to the subset of possible source concepts for an Attribute relationship (the Focus concept in Figure 2) and covers one of the 19 Top-level hierarchies of SNOMED CT in its entirety, or a large subhierarchy within. Similarly, the Attribute's range sanctions a subset of Attribute values as the relationship's target concept. Here, the subset can consist of a few enumerated concepts up to complete Top-level hierarchies. Additionally, the Concept Model specifies how often an Attribute can occur for the given domain (cardinality) and if the usage of one or more Role groups is allowed [7].  
A human-readable version of the Concept Model can be found within the domain-specific sections of SNOMED CT's Editorial Guide [25]. For software processing, on the other hand, the Machine Readable Concept Model (MRCM) is provided [26]. It is distributed via four dedicated Reference Sets (RefSet) within the RF2 files of each SNOMED CT release. For our purpose, we extracted the MRCM Domain Reference Set from the International Edition of 2022-09-30. This RefSet contains the Concept Model rules sorted by domain in the form of Expression Templates (see Section 2.3).
# Enhanced Multicast Repair Fast Reroute Mechanism for Smart Sensors IoT and Network Infrastructure
## 4 M-REP FRR Mechanism  
Based on the analysis and identified problem areas introduced in the previous section, a M-REP FRR mechanism has been proposed. The M-REP mechanism does not require precomputation of an alternative route, it is not dependent on any unicast routing protocol, it does not use a metric calculation of the alternative route, and, finally, it provides full repair coverage.  
The M-REP FRR mechanism uses a multicast [65; 66; 67] routing protocol, Protocol Independent Multicast-Dense Mode (PIM-DM), as its basis. PIM-DM, at the beginning of multicast transmission, floods multicast packets to all PIM-enabled routers in a domain. We decided to use this flooding feature as the basic behavior of our M-REP algorithm. However, to fit PIM-DM to our purpose, we modified its Reverse Path Forwarding control mechanism.  
In this section, the description of original M-REP mechanism is provided. In the next section, we will follow up with the presentation of its improved version, the Enhanced M-REP mechanism (EM-REP). EM-REP can create alternative route that allows recovery from the occurrence of multiple and even parallel failures.
## 3 Results and Discussion
### Structure and Morphology of rGO  
Owing to the large specific surface area and excellent conductivity of reduced graphene oxide (rGO), NiCo\({}_{2}\)S\({}_{4}\) was also combined with rGO to improve its conductivity and cycling performance.  
Graphite was firstly used to synthesize GO by the modified Hummer method and the obtained GO was then reduced with hydrazine hydrate to produce rGO. The rGO Raman curve in Figure 5a shows two high-intensity peaks at 1340 cm\({}^{-1}\) and 1618 cm\({}^{-1}\), which are consistent with the graphene D band and G band (in-plane vibration of sp\({}^{2}\) carbon atoms) [14]. The D and G characteristic peaks of rGO obtained from flake graphite redox were more remarkable than those of GO. The disorder of graphene crystal structure is usually determined by the ratio of D-band and G-band intensity (\(I_{\rm D}/I_{\rm G}\)), which also represents the defect density of graphene [13]. The value of \(I_{\rm D}/I_{\rm G}\) in Figure 5a is 1.08, indicating that the rGO prepared by the modified Hummer method was much more disordered than the untreated graphite. In addition, the \(I_{\rm D}/I_{\rm G}\) value of rGO was also greater than that of the untreated GO, revealing that the average size of the sp\({}^{2}\) domain decreased after GO reduction, which is common in GO and other chemically reduced graphene.  
XRD spectra of graphite, GO, and rGO are shown in Figure 5b. All curves display the (002) characteristic diffraction peak of graphene around 25\({}^{\circ}\). Compared with GO, the (001) reflection of rGO almost disappeared, suggesting that GO has been effectively reduced [41].  
Figure 5c,d present SEM and TEM images of the prepared rGO. Both images reflect successful synthesis of corrugated rGO. The almost transparent and layered flakes indicate  
Figure 4: (**a**) EIS Nyquist plots and (**b**) cycling performance of the MISR-prepared and the STR-prepared NiCo\({}_{2}\)S\({}_{4}\) composites.  
an ultra-high specific surface area, which could allocate adequate attachment sites for anchoring NiCo\({}_{2}\)S\({}_{4}\).
## 3 Organisational Data-Sharing Models
This is very similar to the B2C model; however, the consumers are interacting with one another. The C2C platforms are typically electronic market platforms created by businesses to reach more consumers online. In a C2C platform, the consumers require basic data about one another to communicate. However, the businesses that are facilitating these platforms will collect more data about the consumers' activities. In regard to the process of interaction between the consumers, they may only require the personal information of the consumer. However, the businesses may also collect engagement and behavioural data as part of the process. Each business model delivers a unique strength based on who the targeted audiences are. In the context of privacy, understanding these business models can help authorities regarding the privacy risks involved. By recognising the type of audiences and the type of data that businesses are dealing with, organisations can eliminate the possibilities of privacy risks by introducing privacy regulations. Nonetheless, privacy disclosure still may occur even if legal steps have been taken to be compliant with the privacy regulations [38]. These unforeseen consequences can happen because of a number of reasons, such as unintentional accidents caused by human error, a lack of understanding, or misuse of personal information. These accidents usually occur when incorporating third-party entities that have different policies or lack transparency when it comes to personal data handling. Organisations have a legal obligation to comply with the privacy regulations as they collect, process, and share personal information [39; 40]. In this regard, organisations are trying to minimise any potential privacy risk that threatens their business or the privacy of their information by implementing correct privacy practices. Despite the fulfilment of legal obligations, such as complying with privacy regulations and implementing privacy measures, privacy disclosure still occurs. Privacy disclosure is a major disruption for organisations financially and reputation-wise. Luckily, privacy-protection measures such as privacy frameworks exist to minimise the impact of privacy risk. A misconception in privacy protections regarding the privacy framework and other measures, such as privacy by design, is the implementation costs and the disruption to the organisation. From an execution perspective, privacy frameworks require planning and a fair amount of consideration in the implementation of the standards. However, compared to privacy by design, the requirement is the implementation of the standards from the foundation level. Simply put, organisations need to re-design their operations for the handling of personal data from a privacy perspective. This means major changes to the collection, processing, and disclosure of the information operations. For simplicity, we will review privacy-protection methods that are efficient and quick in implementation.
## 4 Design of TLQoS
### Parameter Estimation  
#### 4.9.1 Hop-Count Measurement  
The hop-count parameter is significant in TLQoS as it is the only time-invariant parameter considering the low-mobility or lower frequency in topology change. The neighbor manager sets the hop-count of a node upon receiving HELLO packet from its neighbors. Initially, all nodes except the sink will initialize their hop-count value to a very large number while the hop-count for sink will be 0. Upon receiving HELLO packet from a neighbor, \(n_{j}\in NB(n_{i})\); a node, \(n_{i}\) updates its hop-count value as follows:  
* IF (\(HC(n_{j})+1\leq HC(n_{i})\)), then \(HC(n_{i})=HC(n_{j})+1\)  
Thus, the hop-count value is determined as the minimum hop-count to the \(BC\)_i.e._, the shortest path length. However, if a node \(n_{i}\) detects any topological changes then it recalculates the hop-count as  
\[HC(n_{i})=min[HC(n_{j})]+1,\forall n_{j}\in NT \tag{21}\]