esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Drawbacks to the Use of Blockchain in Agriculture  
BCT may improve trust, efficiency, and traceability in several sectors, including agriculture [50]. Like every technology, it has drawbacks. This study examines the downsides of adopting BCT in agriculture.  
While decentralization and more transparency are possible benefits of using blockchain techniques in agriculture, it is important to be aware of the potential obstacles presented by governmental and corporate institutions. Because of the revolutionary potential of blockchain technology, governments and large businesses may want to create legal frameworks that address their worries over data access and control. This is because blockchain technology threatens the conventional supremacy of central authority. The main challenge is to strike a careful balance between the advantages of decentralization and the rules that these organizations impose. The maintenance of the integrity of protected data while fostering an atmosphere that supports the dramatic changes brought about by blockchain in agriculture requires striking this balance. We explore these issues in detail in our essay, providing a nuanced view on how to work within the regulatory framework to fully use blockchain technology in the agricultural industry.  
BCT's intricacy is a drawback. Farmers and other stakeholders struggle to grasp and apply the technology's complex algorithms and encryption. Small-scale farmers may not have the means or technical know-how to deploy the system due to its complexity [51]. Second, BCT's decentralized nature might be a drawback. Decentralized systems are subject to malicious attacks, fraud, and other security problems because they lack central regulation. If the blockchain system is hacked or interfered with, agricultural yields, pesticide consumption, and soil health might be jeopardized [52].  
Finally, BCT in agriculture may be expensive. Blockchain implementation needs major hardware, software, and staff investments. Small-scale farmers and other stakeholders may not be able to afford this technology. BCT might cause data overload. The blockchain stores all transactional data, which might be difficult to analyze and comprehend. Growers and other stakeholders may struggle to utilize data to make educated choices due to this overburden.
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 5 Security of Quantum Money: Attack Vectors and Countermeasures
### Countermeasures and Defensive Strategies  
* DFS : Quantum wallets should encode quantum money in DFS to shield stored states from environmental noise and collective decoherence, which not only extends storage times but also resists certain classes of tampering.
* Tamper-evident quantum hardware: Devices used to store and process quantum money must include tamper detection mechanisms. Unauthorized access or manipulation should result in the collapse of the quantum state, rendering the stored money unusable.
* Authentication of classical data: Quantum money may be associated with classical serial numbers or metadata. These identifiers should be cryptographically authenticated using digital signatures to ensure that only authorized entities (e.g., the central mint) can issue valid quantum tokens.
* Redundant verification protocols: Instead of relying solely on a single measurement outcome, quantum money verification protocols should include probabilistic checks or interactive procedures that increase the difficulty of forging valid states without detection.
* Quantum channel authentication: Communication over quantum networks (used for teleportation or entanglement distribution) must be authenticated to prevent spoofing or hijacking. This includes techniques from QKD to generate secure encryption keys and classical cryptographic authentication for control messages.
* Device-independent protocols: Where feasible, device-independent approaches--where security does not depend on trusting the internal workings of the hardware--can provide added resilience against implementation-level attacks.  
Quantum money offers inherent security advantages due to its non-clonability and measurement sensitivity. Nonetheless, ensuring its practical deployment requires a comprehensive security model that addresses not only quantum information-theoretic guarantees but also physical and cryptographic safeguards. By incorporating these countermeasures, a functional and resilient infrastructure for quantum money can be achieved within emerging quantum networks.
## 3 Collaborative AUVs  
Once the navigation and localization problem for the AUVs is solved, a scheme for collaborative work between a group of robots can be proposed. Collaborative work refers to an interaction of two or more AUVs to perform a common task which can be collaborative navigation, exploration, target search, and object manipulation. Using a team of AUVs navigating on a certain formation has the potential to significantly expand the applications for underwater missions; such as those that require proximity to the seafloor or to cover a wide area for search, recovery, or reconstruction. At first, researchers focused their work on how multiple vehicles could obtain data simultaneously from the same area of interest. Nowadays, their focus has moved to the trajectory design and operation strategies for those multi-vehicle systems [99].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Navigation Technology** & **Approaches** & **Information Available** & **Accuracy** & **Range** & **Results** \\ \hline \multirow{4}{*}{Acoustic} & \multirow{4}{*}{SONAR} & \multirow{4}{*}{Distance from obstacles.} & Depending on distance from obstacles, from 5–10 cm to more than a meter (10–120 cm). & From 5 m up to hundreds of meters from obstacles. & Experimental in real conditions. \\ \cline{3-6}  & & & Depending on distance from & \\ \cline{3-6}  & & & & Depending on distance from & \\ \cline{3-6}  & Acoustic range & & & hydrophone array and the frequency, from some centimeters up to tens of meters of meters. & Experimental in real conditions. \\ \hline \multirow{4}{*}{Geophysical} & Gravity, geomagnetic, TAN, TRN, TBN & \multirow{4}{*}{Position} & Meters. Depending on the map resolution and filter applied. & Kilometers from initial position. & Simulation, Experimental under controlled conditions. \\ \hline \multirow{4}{*}{Optical} & \multirow{4}{*}{Light sensors.} & Position and orientation relative to a target. & Up to 20 cm for position and 10\({}^{\circ}\) for orientation. & 1–20 m from markers. & Simulation, Experimental under controlled conditions. \\ \cline{3-6}  & & & Up to 1 cm for position and 3\({}^{\circ}\) for orientation. & 1–20 m from markers. & Experimental in real conditions. \\ \hline \multirow{4}{*}{SLAM} & \multirow{4}{*}{Acoustic} & Position and orientation relative to the mapped environment. & From some centimeters up to more than a meter. & Up to tens of meters from targets. & Experimental in real conditions. \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & \\ \hline \multirow{4}{*}{Sensor fusion} & \multirow{4}{*}{ELC, LC, TC.} & Position, orientation and velocity. & Depending on the approach and filter applied, accumulative error can be reduced up to some meters (5–20) & Kilometers from initial position. & Simulations, Experimental. \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Technologies for AUV localization and navigation.
## 4 Results
### Visualizing Vertical Flow  
Since the 3D point cloud includes flow coordinates in three directions, the flow patterns can be interpreted to determine upwelling or downwelling patterns, as well as their changes through time and changing discharges and water levels.  
Figure 10: Visualizing the high velocity core (HVC) as a 3D point cloud of flow velocities viewed at an angle, looking downstream. The view angle is indicated by the rotational axes. The HVC here is the top 20% of velocities for each day. The HVC is colored according to velocity (\(\mathrm{ms^{-1}}\)). The points with velocities lower than the HVC are shown in light grey to give the three-dimensional context. The Z-axis is exaggerated by a factor of two. The river bed model of 23 May is shown to give a consistent spatial context.  
The flow patterns can not only be interpreted to determine upwelling or downwelling patterns, but also to find turbulence, indicated by a local mixture of flow in both directions. One way to analyze these patterns is to plot the point clouds showing vertical motion (Figure 11).  
Figure 11: Vertical flow velocities. The color scale is symmetrical centered on zero, meaning horizontal flow. Positive values indicate upwards flow motion (upwelling), and negative values indicate downwards flow motion (downwelling). The Z-axis is exaggerated by a factor of two. The river bed model of 23 May is shown for spatial context.  
Before the water starts rising, the motion is mostly horizontal (16 May), with some upwelling near the river bed in the deepest part of the meander bend. As soon as the water rises, the flow starts moving more vertically too (17 May), with a fairly clear pattern of the water in the meander bend moving in a downward direction in the shallow parts near the point bar, with some upwelling in the deeper part towards the outside bank. The same pattern intensifies on 18 May. It needs to be considered, though, that the sampling pattern on that day was coarse; hence, more of the data is relying on interpolation than on the other days. By 19 May, as the flood reaches its preliminary peak, the water is downwelling towards the drop-off at the peak of the point bar, then it moves mostly horizontally in the deeper part of the channel with some upwelling continuing there, as on the previous days. Towards the exit of the meander, an overturning motion starts, with downwelling near the surface and upwelling from below. Thereafter, the water body seems to be split into layers of vertical motion, with the top layer generally descending and the bottom layers rising. This tendency remains until the end of the measurement period, but decreases in magnitude on 23 May, as the lake effect becomes more pronounced and velocities have decreased from the flood peak. During the high water stages, the inundated point bar is dominated by downwelling water, whereas the main part of the channel is dominated by horizontal motion with upwelling near the bottom.
## 6 Experimental Verification
### Comparison of Experimental Results
#### 6.3.2 Low-Speed Motion Performance  
The PI control method and the proposed composed control method respectively perform speed closed-loop control on the direct drive component, the harmonic drive component, and the RV drive component. Keep the swing table still and set the reference speed v\({}_{\rm{r}}\) to 0.01\({}^{\circ}\)/s, then collect the encoder data and plot the angular position value. The typical curve is shown in Figure 9. In Figure 9,c,e, the red slash represents the motion trajectory envelope when using PI, the blue slash represents the motion trajectory envelope when using the PI + SAKF. Meanwhile, the solid red line represents the actual trajectory by the PI, the solid blue line represents the actual trajectory by the PI + SAKF, and the green line represents the ideal trajectory without any tracking errors. Figure 9,d,f show the tracking error of the three components when using the PI and the PI + SAKF as the control scheme respectively.  
Figure 8: Comparison of speed observation by LPF and SAKF.  
By measuring the fluctuation amplitudes of the respective curves in Figure 9, the low-speed motion performance by the two control schemes can be listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Category** & **PI** & **PI + SAKF** \\ \hline \multirow{2}{*}{1} & Direct-driving & \multirow{2}{*}{0.61} & \multirow{2}{*}{0.23} \\  & component & & \\  & Harmonic-driving & & \\  & component & & \\  & RV-driving component & & \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of low-speed motion performance.  
Figure 9: Measured angular position of the (**a**) direct-driving component, (**c**) harmonic-driving component, and (**e**) RV-driving component, and the calculated angular error of (**b**) the direct-driving component, (**d**) harmonic-driving component, and (**f**) RV-driving component by the PI and the PI + SAKF with the reference speed \(\mathrm{v}_{\mathrm{r}}=0.01^{\circ}\mathrm{/s}\).  
By analyzing the data in Table 3, it can be seen that when the control method proposed in this paper is adopted, all three components can obtain better low-speed motion performance, which means that the proposed control scheme can be applied in various types of OESs and can compensate torque disturbance effectively.
# Towards Federated Learning with Byzantine-Robust Client Weighting
## Appendix C Convergence of \(FedAvg\) in the Absence of Byzantine Clients  
For this analysis, we make the following assumptions:  
**Assumption A1**.: _Aggregate \(=\) weighted arithmetic mean._  
**Assumption A2**.: _All clients send their true \(ClientUpdate\) result._  
**Assumption A3**.: _Full device participation._  
**Assumption A4** (Smoothness).: \(\forall k\in[K]:F_{k}\) _is \(L\)-smooth (see Definition 5)._  
**Assumption A5** (Strong convexity).: \(\forall k\in[K]:F_{k}\) is \(\mu\)-strongly convex (see Definition 6)._  
**Assumption A6** (Bounded variance).: \(\forall k\in[K]:\) _Let \(b_{k}\sim Z_{k}\) be the uniformly sampled \(B\)-sized batch of client \(k\)'s local data. The variance of stochastic gradients of each client is bounded: \(\mathbb{E}\|\
abla F_{k}(w_{k},b_{k})-\
abla F_{k}(w_{k})\|^{2}\leq c_{k}^{2}\), where \(\
abla F_{k}(w_{k},b_{k})=\frac{1}{B}\sum_{\varepsilon\in b_{k}}\
abla\ell(w_ {k};z)\)._  
**Assumption A7** (Bounded squared norm).: \(\forall k\in[K]:\) _The expected squared norm of stochastic gradients is bounded: \(\mathbb{E}\|\
abla F_{k}(w_{k},b_{k})\|^{2}\leq G^{2}\)._  
We also define the following:  
**Definition A2** (Client proportion).: \(\forall k\in[K]:p_{k}=\frac{h_{k}}{n}\).  
**Definition A3** (Degree of heterogeneity).: _Let \(F^{*}\) and \(F_{k}^{*}\) be the minimum values of \(F\) and \(F_{k}\), respectively. The degree of heterogeneity is defined as: \(\Gamma=F^{*}-\sum_{k=1}^{K}p_{k}F_{k}^{*}\)._  
As mentioned previously, we utilize the results of [PERSON] et al. [16] for our analysis.  
**Theorem A1** ([16] Theorem 1).: _Let Assumptions A4-A7 hold and \(L,\mu,\sigma_{k},G\) be defined therein. Choose \(\kappa=\frac{L}{\mu}\), \(\gamma=\max\{8\kappa,E\}\) and the learning rate \(\eta_{t}=\frac{2}{\mu(\gamma+t)}\). Then, FedAvg with full device participation satisfies_  
\[\mathbb{E}[F(w^{T})]-F^{*}\leq\frac{\kappa}{\gamma+T-1}\bigg{(}\frac{2B}{\mu }+\frac{\mu^{\gamma}}{2}\mathbb{E}\Big{\|}w^{0}-w^{*}\Big{\|}^{2}\bigg{)},\] (A6)  
_where_  
\[B=\sum_{k=1}^{K}p_{k}^{2}\sigma_{k}^{2}+6L\Gamma+8(E-1)^{2}G^{2}.\] (A7)  
Here, \(w^{t}\) denotes the model-weights vector at the server at the end of step number \(t\) such that \(0\) is the first step of the algorithm.  
From this theorem, we immediately get the following:  
**Corollary A1**.: _Assuming the predefined assumptions laid out above are true, Theorem A1 holds for any Preprocess procedure._
# Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning
## 4 System Model  
In this section, we propose and discuss the architecture of the coexistence scheme for uncoordinated LTE and WiFi networks that use WiFi saturation status to estimate the traffic demand of the WiFi network. Figure 1 shows the main blocks of the coexistence schemes implemented in the LTE-U eNB considering uncoordinated LTE-U and WiFi networks. The LTE eNB has three main features, namely the _Technology recognition model_, the _WiFi saturation sensing model_, and the _Coexistence decision model_. The _Technology recognition model_ is used to capture and identify concurrent transmissions by different co-located technologies. In practical applications, the LTE and WiFi transmissions are classified based on technology recognition solution that requires capturing and processing the IQ samples of LTE and WiFi traffic [26]. Once the WiFi frames are classified, they are fed into the _WiFi saturation sensing model_, where the saturation state of WiFi is determined based on the CNN model proposed in our previous work [8]. The _WiFi saturation sensing model_ is used to distinguish saturated and unsaturated WiFi traffic in real-time by analyzing the Inter-Frame Spacing (IFS) distribution. The IFS distribution is derived by processing the statistics obtained from the _Technology recognition model_. Finally, the _Coexistence decision model_ is used to decide the optimal action that leads to efficient coexistence. This decision is made based on the saturation status of the WiFi network and the status of the LTE-U network. The proposed coexistence decision algorithms are discussed in the next sections.  
Figure 1 shows that the coexistence scheme for uncoordinated LTE-U and WiFi networks is executed by the central eNB. The LTE central eNB uses technology recognition system [26] and a WiFi saturation sensing model [8] to estimate the traffic demand of the co-located WiFi network. The decision made by the central eNB also considers the number of co-located eNBs, \(LTE_{\text{Of}}\), \(LTE_{\text{Tar}}\), and \(LTE_{\text{Ob}}\) of the collocated eNBs within its collision domain. The central eNB collects this information using the X2 interface and the X2 AP services [34]. Once the best configuration is determined by the central eNB, this X2 interface is used to send the decision to the co-located eNBs. After that, each eNB adjusts its transmission time based on the decision. A similar approach of coexistence decision exchange between central eNB and other co-located eNBs is also used in existing LTE/WiFi coexistence solutions [13].  
Figure 1: Topology of proposed Coexistence scheme for uncoordinated LTE-U and WiFi networks.
# Development and Effect Analysis of UVB-LED General Lighting to Support Vitamin D Synthesis
###### Abstract  
Ultraviolet rays from sunlight are involved in vitamin D synthesis, which is essential in maintaining health. Most in vivo vitamin D synthesis occurs via exposure to sunlight, whereas a small portion is obtained through diet. Therefore, appropriate exposure to sunlight is recommended to satisfy vitamin D requirement. However, vitamin D deficiency still poses a serious issue to numerous people. To resolve this problem, artificial lighting has been studied to provide rays of UV light with characteristics similar to that of sunlight. The present study introduced vitamin D synthesis stimulating UVB-LED light via the general diffusion lighting type to indoor dwellers. However, despite the strides of advancement made in this type of lighting, studies on UVB with relation to vitamin D synthesis are limited. This study aimed to suggest UVB-LED lighting for indoor dwellers and confirm the effectiveness of vitamin D stimulation through animal testing. For this, a UVB-LED providing safe UVB dosage was developed based on the photobiological safety (IEC 62471) of lighting. Next, a comparison study of the suggested lighting and general lighting was executed in an isolated system. The study continued for two weeks and the vitamin D levels within the animal test subject (Rat, Wistar, male) were measured. At this time, the lighting was turned on for 06:00-22:00 considering the sunrise timing and average lighting time for modern people, and blood was collected from the caudal veins of the rats and analyzed for the amount of 25-hydroxyvitamin D [25(OH)D]. Analysis results showed that the amount of 25-hydroxyvitamin D [25(OH)D] in blood was higher, on average, by 6.8% (0.97 ng/mL) and 7.3% (0.9 ng/ml) in rats reared under UVB-LED lighting on the seventh and fourteenth experimental days, respectively, than those under general lighting. It was, therefore, confirmed that vitamin D synthesis in the human body can be supported by the proposed lighting in this study.  
Ultraviolet-B LED; vitamin D; UVB-LED general lighting; effect analysis +
Footnote †: journal: Applied Soft. 2020, 10, 889; doi:10.3390/app10030889
# Just-in-Time Fluid Flow Simulation on Mobile Devices Using OpenVisFlow and OpenLB
## 3 Numerical Experiments and Discussion of Results
### Performance
Table 4 presents a comparison of the specifications obtained via Geekbench, a well-established mobile benchmark suite, and the achieved total performance in MLUPS. Despite the decision to not utilize parallelization, _paint2 sim_'s performance compares favourably at a speed-up of approximately 1.45. Among the tested mobile devices in Figure 8, the Samsung Galaxy A80 offers the lowest CPU performance. Despite this, the visualization remains smooth for users at an average throughput of 10 MLUPS and no noticeable lags. At the top end, the single-threaded LBM performance on a Samsung Galaxy S22 Ultra is quite close to the unvectorized performance on higher-end x86 CPU cores at approximately 24 MLUPs. Overall, the performance characteristics exhibited by _paint2 sim_ are sufficient for the intended simulation cases and are highly competitive to single cores of full-powered x86 CPUs, considering the comparably smaller power envelope. A notable issue associated with mobile devices is their tendency to decrease the power output of their CPUs in order to prevent overheating. Figure 9 presents the average MLPUs of the mobile device _Samsung Galaxy S22 Ultra_ during extended simulation periods. The graph illustrates a significant decline in performance over time. Moreover, the decline is not continuous; rather, the device maintains its performance until the temperature reaches a critical point, at which it then ramps down to a lower performance level. Figure 8: Performance comparison of _paint2 sim_ across different devices. Performance is measured in Mega Lattice Updates per Second (MLUPS).