esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Truncation Threshold Determination for Multimode Fiber Single-Pixel Imaging
## 2 Method
### Background: Measurement and Recovery
#### 2.1.2 Recovery via Truncated Singular Value Decomposition (TSVD)  
As a common solution to the inverse problem, TSVD utilizes a truncation threshold (\(k\)) to obtain the Moore-Penrose inverse of \(\mathbf{A}\) (\(\mathbf{A}_{k}^{\dagger}\)). Then, the target (\(\mathbf{x}\)) can be restored as follows:  
\[\hat{\mathbf{x}}_{k}=\mathbf{A}_{k}^{\dagger}(\mathbf{y}+\mathbf{e})=\sum_{j= 1}^{k}\frac{\mathbf{u}_{j}^{\mathsf{T}}(\mathbf{y}+\mathbf{e})\mathbf{v}_{j}}{ \mathbf{s}_{j}},1\leq k\leq N_{\text{meas}}, \tag{1}\]  
where column vectors \(\mathbf{u}_{j}\) and \(\mathbf{v}_{j}\), together with the corresponding SV (\(\mathbf{s}_{j}\)), are obtained via the singular value decomposition (SVD) of \(\mathbf{A}\), i.e., \(\mathbf{A}=\mathbf{U}\mathbf{S}^{\mathsf{T}}\). \(\mathbf{u}_{j}\) and \(\mathbf{v}_{j}\) are the columns of orthogonal matrices \(\mathbf{U}\) and \(\mathbf{V}\), respectively. \(\mathbf{S}=\text{diag}(\mathbf{s}_{j})\) contains all SVs. Truncation (\(k\)) limits the measurement number (\(N_{\text{meas}}\)). Recovery involves solving an inverse problem to obtain \(\hat{\mathbf{x}}\) from \(\mathbf{A}\) and \(\mathbf{y}+\mathbf{e}\). The negative values of \(\hat{\mathbf{x}}_{k}\) are set to zero due to the physical nature of the images.  
As Equation (1) shows, assuming that stochastic noise (\(\mathbf{e}\)) affects each vector (\(\mathbf{u}_{j}\)) homogeneously, those with a smaller weight (\(\mathbf{s}_{j}\)) would have a worse signal-to-noise ratio due to the amplification of \(1/\mathbf{s}_{j}\), introducing more unwanted effects on the result. Thus, it is important to cut off at a certain threshold (\(k\)) in order to abandon components with smaller SVs [41].
## 3 Results and Discussion
### GC\(\times\)GC-TOF-MS Volatile Metabolome
As well, significant differences between the concentrations were found for a number of them, all being more abundant in Teran then in Plavac mali wines. Ho-trienol emerged as the most important differentiator with the highest \(F\)-ratio value, followed by 10,11-epoxycalamenene, linalool, and dihydro-\(\gamma\)-terpineol. Interestingly, the difference between linalool concentrations was statistically significant when determined by GC\(\times\)GC-TOF-MS, contrary to the result of the analysis of the same compound by GC-MS, although a same trend towards higher concentration in Teran wines was observed by both techniques. It is possible that this discrepancy was a result of the co-elution of linalool with particular unidentified compounds and lower resolution of GC-MS in comparison to GC\(\times\)GC-TOF-MS instrumentation used, although strict measures have been taken to ensure the quality of the results. As well, different HS-SPME setup was applied in the two methods, which could have influenced the differences observed. Among the terpenes for which significant differences were observed, only linalool is commonly included among the compounds with sensory relevance. It is possible that this monoterenol plays a more significant role in the aroma of Teran than in Plavac mali red wines, although precise conclusions were not possible at this point, considering the discrepancy between the GC-MS and GC\(\times\)GC-TOF-MS results. Moreover, the data obtained by GC\(\times\)GC-TOF-MS were semi-quantitative, meaning putting them in relation with the corresponding odor detection threshold would not be meaningful. The determined differences in terpenes profiles of the two monovalent wines were probably largely a result of different varietal origin. However, it should be stressed out that the grapes of Plavac mali and Teran used for production of the investigated wines were grown in two different geographical regions in Croatia, Dalmatia and Istria, respectively, characterized by different pedoclimatic condition, so variety and location possibly produced a synergistic effect. Lower temperatures favor the accumulation, whereas increased temperatures reduce the content of terpenes in grapes [46], possibly due to reduced expression of 1-deoxy-D-xylulose-5-phosphate synthase, germacrene D synthase, and linalool synthase which regulate the biosynthesis of terpenes, as reported by other authors [47]. It is possible that higher terpene concentrations in Teran wines were at least partly a result of the differences in temperature, with Istria further north being characterized by lower average values (Table S1). Similar results were found in our previous report where Dalmatian white wines contained significantly lower concentrations of monoterenes in relation to those produced in Istria [5]. Water supply and soil water retention capacity are also important factors that determine the growth and
# Architecting and Deploying IoT Smart Applications: A Performance-Oriented Approach
## 6 Discussion
### IoT Architectural Layers vs. Deployment Stages  
IoTecture architectural layers paired together with IoTinuum deployment stages provide a simple and unambiguous way to bind software, hardware, and communication components to physical infrastructure in an IoT context. The physical location where a software component is executed has a high impact on the performance and brings different tradeoffs, as revealed by our performance analysis results. IoT smart applications have their specific characteristics and peculiarities regarding the location of physical infrastructure--stages S2-Mist, S3-Fog, and S4-Cloud--compared to traditional Web-based distributed applications.  
There is no direct association between specific layers and stages, even though some choices make for sense than others. For example, components from layers L2-Transport, L3-Data, L4-Model, and even L5-Service can be deployed into an intermediary stage, such as configurations C1 and C2 that placed L2-Transport to L4-Model components into S3-Fog. On the other hand, all of them can be executed in S4-Cloud, such as configurations C5 and C6. Also, if S3-Fog implements a lightweight fog with no local L4-Model, there is no way to consume data locally from an edge device. Configurations based on lightweight S3-Fog for communication only are not able to consume data locally or decrease application latency, avoiding long internet delays. However, they can still be used for load balance purposes. Also, we evaluated configurations where ChirpStack is deployed in S3-Fog and S4-Cloud. Even though it could be installed in S2-Mist, we do not consider it a stable solution, since the mist node may not adequately fulfill the performance and reliability requirements of IoT smart applications.  
Applications requiring strict compliance with low latency--such as smart city applications--should be designed having in mind layer L4-Service deployed in stages S2-Mist or 3S-Fog, as data can be consumed locally, thus, faster. On the other hand, for applications with an unstable connection between S1-Thing and S4-Cloud--e.g., smart agriculture applications--there is the need to store and process data in an intermediary stage, for increased uptime. Thus, such applications should have L3-Data components running in S2-Mist or S3-Fog. Here we presented some preliminary insights related to the deployment of layered components into staged locations, and further research is necessary to provide methods, mechanisms, algorithms, and best practices to avoid the need of a specialist and allow an automated deployment process.
# Internet of Nano-Things, Things and Everything: Future Growth Trends
## 6 Challenges and Impediments to IoT
### Architectural Limitations  
The adoption and viability of IoT clearly puts many technological demands on the current Internet infrastructure. Many of these limitations have been clearly exposed when IoT devices were practically implemented over the current Internet infrastructure, such as: weak security, interoperability problems,data provenance and excessive human interaction. These problems still require to be addressed for the rollout of 5G and for the deployment of the Future Internet (FI).  
Because IoT devices are composed of so many different technologies, when networked, they form inherently quite complex structures. Thus, IoT network failure may require more time for fault diagnosis and restoration of service. This also means having maintenance personnel with multiple networking and protocol skills. This naturally entails a more expensive workforce to both hire and retain.  
The architecture of an IoT system may be broadly classified into four layers, as shown in Figure 8, below.  
Constructing a global unified IoT ecosystem that is communicating transparently is still not possible yet, at the time of writing of this paper. This is due to no universal protocol currently being in place that can work across heterogeneous networks. The IoT at the moment is just a collection of Intranets of Things. For IoT to be a seamless ecosystem, a standardized internationally agreed application layer protocol needs to be created. This protocol would also take into account communication across the various physical interfaces of the IoT devices. Instead of inventing a new protocol, it would be far easier to reuse the technologies from the Web itself. The \"Web of Things\" [70] has precisely these goals needed to make the IoT ecosystem a reality. These are to: \"reuse and leverage readily available and widely popular Web protocols, standards and blueprints to make data and services offered by objects more accessible to a larger pool of (Web) developers.\" [70] The Web of Things does not actually stipulate the physical layer connections between devices, thus the Web of Things will function whether connected to a company intranet, domestic network or any type of LAN.  
To clarify, the Web of Things (WoT) encompasses every aspect of the software approach to make WoT be fully integrated into the World Wide Web (www). Analogous to the mapping of the Web (Application Layer) to the Internet (Network Layer), the WoT also has an Application Layer that aids in the authoring of IoT applications.
## 2 Materials and Methods  
### Experimental Design  
Two concentrations of DMAHDM were used (3 and 5 wt.%) in the absence and presence of NACP (20 wt.%) to evaluate their outcomes on the dependent variables. Figure 1 illustrates the design of the study. The designed formulations containing 3 or 5% DMAHDM with and without NACP, the experimental and commercial controls were aged via wateraging for one year or fatigue aging using a chewing simulation machine for 250,000 cycles, equivalent to one year of clinical service under chewing simulation. This experiment was conducted according to a randomized complete block design, with 6 repetitions for each group.  
The base resin composed of bisphenol glycidyl dimethacrylate (BisGMA, Esstech, Essington, PA, USA) and triethylene glycol dimethacrylates (TEGDMA, Esstech) at a mass ratio of 1:1. Cumborquinone (0.2 wt.%) and ethyl 4-dimethylaminobenzooate (0.8 wt.%) were used as photoinitators. Barium boroaluminosilicate glass particles (median size: 1.4 um; Dentsply Sirona, Milford, DE, USA) silanized with 4 wt.% 3-methacryloxymethyltrimethoxysilane were incorporated as base fillers.  
Then, specimens were divided into the following groups:  
1. Commercial control (Heliomolar, Ivoclar Vivadent, Mississauga, ON, Canada): 22% BisGMA and Urethane dimethacrylate resin mix + 77% highly dispersed silicon dioxide, prepolymer, and ytterbium trifluoride as fillers + \(<\)1% stabilizers, catalysts, and pigments;
2. Experimental Control: 35% BisGMA-TEGDMA resin mix 1:1 ratio + 65% glass particles;
3. 3% DMAHDM: 32% BisGMA-TEGDMA resin mix 1:1 ratio + 3% DMAHDM + 65% glass particles;  
Figure 1: Flowchart of the experimental design proposed in this study. Different antibacterial formulations were designed. Following the aging, the mechanical properties were assessed via flexural strength, elastic modulus, and measurements of the topography changes following simulated wear. The antibacterial activity was analyzed by biofilm plate counting method, metabolic assay, colorimetric quantification of lactic acid, and Baclight® bacterial viability assay.  
4. 5% DMAHDM: 30% BisGMA-TEGDMA resin mix 1:1 ratio + 5% DMAHDM + 65% glass particles;
5. 3% DMAHDM+20% NACP: 32% BisGMA-TEGDMA resin mix 1:1 ratio + 3% DMAHDM + 20% NACP + 45% glass particles;
6. 5% DMAHDM+20% NACP: 30% BisGMA-TEGDMA resin mix 1:1 ratio + 5% DMAHDM + 20% NACP + 45% glass particles.
# Tablet Scoring: Current Practice, Fundamentals, and Knowledge Gaps
## Overview of Table Preparation
### Tablet Manufacturing
#### 5.1.5 Tablet Compression  
Following granulation, the final blend is compressed to desired tablet shapes, sizes, weights. Following tablet compression, a rotary tablet press is used to form the tablets by compressing dry granular powders to sufficient pressure to make the particles cohere. Different types of punches with appropriate cup ratio are used to compression to target the desired tablet sizes and shapes [160]. Factors that are important during tablet compression include high flowability, high compactability and low segregation. The most common challenges with tablet compression include:  
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\begin{table}
\begin{tabular}{c c c} \hline \hline
**Exipients** & **Function** & **Example** \\ \hline Diluent & Serve as bulking agent and facilitate accurate dosing. & Sugar compounds: lactose, mannitol, dextrose, sorbitol, silicate, calcium, magnesium salt, sodium chloride, potassium chloride, cellulose derivatives \\ \hline Binder, compression aid, granulating agents & Facilitate tablet compression. Ensure tablet robustness. & Natural and synthetic polymers: starch, gelatin and sugars as sucrose, glucose, dextrose, and lactose \\ \hline Disintegrants & Aid with tablet disintegration and dissolution by increasing the surface area of the tablets, facilitate release of drug substance. & Compounds which swell in the presence of water: Starch, cellulose derivatives, alginates and cryospoidone \\ \hline Glidants & Granulation flow enhancer, aid with tablet compression and eliminate particles agglomeration (anticaking) & Colloidal anhydrous silicon, silica compounds, talc \\ \hline Lubricants & Table compression aid, reduce blend cohesiveness characteristic during compression, reduce disintegration rate & Steric acid, salts and derivatives of steric acid, talc, hydrogenated vegetable oils and PEG \\ \hline Coating agent & Prevent tablet degradation environmental conditions (Temperature, light and moisture). Serve as taste masking agent, inhibit odor, facilitate administration and appearance enhancer & Natural and synthetic polymers, polymers that are insoluble in acid \\ \hline \hline \end{tabular}
\end{table}
Table 2: Excipients used for tablet formulations.
## 1 Introduction
A tightly coupled model, in which the phase-specific ISB was estimated station-by-station and the resolvable ambiguity between different satellite systems was formed, was proposed by [PERSON] et al. [13] for multi-GNSS PPP AR. The day-to-day scattering was within 0.05 cycles for 85% of phase-specific ISBs. Compared with the intra-system PPP AR, the initialization time of inter-system PPP AR was reduced by 10%. Several studies were related to the ISB in SPP processing. [PERSON] and [PERSON] [14] employed the Allan deviation to validate the stability of ISB in GPS/Galileo combined SPP solutions. In addition, the multi-GNSS navigation performance could degrade when mitigating the ISBs (instead of estimating them) with the time offset parameters provided in the broadcast ephemeris. [PERSON] et al. [15] focused on the ISB between GPS and BDS-2 derived from their integrated SPP processing. The results indicated that the ISB estimates were receiver-dependent and found to be stable over a day and repeatable over adjacent days based on statistical hypothesis testing. Moreover, the estimated ISBs of BDS-2 geostationary orbit (GEO), inclined geosynchronous orbit (IGSO), and medium earth orbit (MEO) satellites with respect to GPS were consistent. [PERSON] and [PERSON] [16] performed an analysis of the ISB for multisystem combined SPP using the data sets at nine European stations from four days in 2013. The results indicated that the ISB estimates between GPS and Galileo and between GPS and QZSS were dependent on the employed broadcast or precise ephemeris, while it was not the case for those between GPS and GLONASS. According to above description, most of the research concentrated on the mitigation and estimation of ISB in precise relative positioning and PPP, while only several studies focused on the ISB in SPP. In addition, the improved accuracy of broadcast ephemeris and the increased number of in-orbit satellites in recent years may leave the conclusions on the SPP-related ISB summarized in the existed studies obsolete. Most importantly, the existed SPP studies did not cover the ISB about BDS-3 satellites. Furthermore, the contribution of a priori ISB estimates to SPP solutions was not rigorously investigated. In this study,we carefully characterize the ISB estimates in GPS/GLONASS/Galileo/BDS-3/BDS-2 integrated SPP, and the performance enhancement of ISB estimates as a priori constraints for multisystem SPP position solutions with a prediction period from a day to a week under a cutoff elevation angle from 10\({}^{\circ}\) to 50\({}^{\circ}\) (to simply simulate the harsh observation environments) is rigorously evaluated. The paper starts with the multisystem integrated SPP model taking ISB as a priori constraints. Next, the results are exhibited and analyzed. Subsequently, the discussion is conducted. Finally, the main conclusions are summarized.
## 1 Introduction
### Selection of the Magnetic Sensor  
The literature review indicates that the magnetic flux leakage (MFL) method provides advantages over eddy current (EC) in conducting area tests and facilitating the mapping of reinforcement mesh. However, effective mapping of the reinforcement mesh necessitates the selection of appropriate sensors suitable for the area transducer. An analysis of commercially available elements is conducted in [41]. Sensitivity, size, and bandwidth were chosen as critical selection criteria. A summary of the values for these three parameters is presented in Figure 1.  
The magnetic transducer is designed to operate in the range of 1 to 10 mT--suitable for applications within this magnetic field range (Figure 1). The sensors must enable measurements of the DC magnetic field and be as compact as possible. The technologies that best meet these criteria are GMR, AMR, and MO. The MO sensor system was previously designed, constructed, and tested; results are presented in [56].  
Figure 1: Comparison of magnetic sensors based on sensitivity, bandwidth, and size [41]. GMR—giant magnetoresistance; AMR—anisotropic magnetoresistance; TMR—tunnel magnetoresistance; GMI—giant magnetoimpedance; MO—magneto-optical; SQUID—superconducting quantum interference device.  
In the case of an eddy current (EC) transducer, the measurement range should match that of the magnetic flux leakage (MFL) method. The required bandwidth is a minimum of 50 kHz, with no specific size limitations. The technologies that best meet these criteria include GMR, AMR, and search coils.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 9 Discussion
### Performance of TTA  
Our results in Section 8.3 show that even though DUA and Tent can reduce the domain gap, their effectiveness fails to match that of SDA using FISH Mask; compare the performances of DUA and Tent in Figure 10 to the upper-bound performance of FISH Mask (applied to source cloud detectors at a mask sparsity level of 100%) in Figure 6(a). At this stage, the TTA methods may not be effective enough to be recommended for online adaptation (i.e., onboard updating of machine learning models). Updating other layers of the model, in addition to the batch normalisation layers, in an unsupervised manner could enhance the performance of TTA methods, but how to perform this update remains an open problem.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Model Settings**} & **DUA** & **Tent** \\  & **\#Samples = 16; No Augmentation** & **Batch Size = 1; \#Samples = 250; Epoch = 1** \\ \hline CloudScout-3-S2 & 181.25 & 5672.52 \\ CloudScout-8-S2 & 198.27 & 5852.24 \\ ResNet50-3-S2 & 186.32 & 4782.63 \\ ResNet50-8-S2 & 192.16 & 4882.97 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Execution time (s) of TTA methods for source cloud detectors in Table 4(a) on the Advantech ARK-1123L embedded computer. For Tent, the embedded computer ran out of memory for any batch size larger than 1.
## 3 Results and Discussion
### Antioxidant Activities  
The DPPH assay, as an in vitro antioxidant activity assessment, exhibits a lower value, indicating more significant antioxidant activity. The concentration of BHT corresponding to a 50% DPPH radical scavenging rate (SC\({}_{50}\)) was found to be 0.11 mg/mL (Figure 6). The samples of W2 and W3 demonstrated values of 0.59 mg/mL and 1.75 mg/mL, respectively, indicating better antioxidant activity among samples. Additionally, the samples of W3 exhibited slightly lower antioxidant activity compared to the samples of W1. The samples of both W1 and W4 exhibited relatively weaker antioxidant capabilities, with SC\({}_{50}\) values of 7.84 mg/mL and 5.96 mg/mL, respectively.  
The ABTS and FRAP assay results were expressed in terms of Trolox equivalents and Fe\({}^{2+}\) concentrations, respectively, where higher values indicate stronger antioxidant capacity. The samples of W2, according to Figure 7, exhibited the highest antioxidant capacity in both the ABTS and FRAP assays, with values of 97.06 \(\upmu\)mol TE/g and 60.62 \(\upmu\)mol Fe\({}^{2+}\)/g, respectively. The W3 samples showed a slightly lower antioxidant capacity than W2 in the ABTS and FRAP assays, with values of 64.08 \(\upmu\)mol TE/g and 45.29 \(\upmu\)mol Fe\({}^{2+}\)/g, respectively. However, the samples of W1 and W4 demonstrated relatively weaker antioxidant capacities; W1 displayed the weakest antioxidant activity in both tests.  
Figure 4: The \({}^{1}\)H NMR spectrum of meranzin hydrate.  
Figure 5: The \({}^{13}\)C NMR spectrum of meranzin hydrate.  
Previous studies demonstrate the favorable antioxidant activity of flavonoid compounds in Citrus fruits [24]. Rutin (CP5), for instance, exerts its free radical scavenging effect through its ability to eliminate hydrogen peroxide and peroxides [25]. Naringin (CP10) exhibits strong antioxidant properties due to its ability to eliminate free radicals [26; 27], while hesperidin (CP11) exerts antioxidant effects by attenuating oxidative stress reactions [28]. Neohesperidin (CP12) was also confirmed to possess excellent free radical scavenging abilities in both superoxide and hydroxyl radical tests [29]. These four compounds were detected in all the samples in this study, and in the absence of considering compound synergistic effects, these four compounds provide a relevant explanation for the observed antioxidant activity. However, the compound kaempfer (CP14) was shown to possess antioxidant activity [30]. Considering the differences in compound composition among samples and the potential synergistic effects between compounds [31], this may explain why the samples of W2 and W3 exhibited stronger antioxidant capacity, while the samples of W1 and W4 showed relatively weaker activity.