esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Overall Accuracy
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Map Classes Classifier**} & \multicolumn{4}{c}{**Reference Data (\%)**} & \multicolumn{4}{c}{**Total (\%)**} \\ \cline{2-13}  & \multicolumn{2}{c}{**Cereals**} & \multicolumn{2}{c}{**OCFC**} & \multicolumn{2}{c}{**Vegetables**} & \multicolumn{2}{c}{**Olive Trees**} & \multicolumn{2}{c}{**Fruit Trees**} & \multicolumn{2}{c}{**Total (\%)**} \\  & **SVM** & **RF** & **SVM** & **RF** & **SVM** & **RF** & **SVM** & **RF** & **SVM** & **RF** & **SVM** & **RF** \\ \hline Cereals & 88.23 & 87.40 & 6.05 & 24.43 & 2.05 & 1.52 & 1.21 & 5.17 & 0.65 & 6.17 & 64.17 & 64.32 \\ \hline OCFC & 5.27 & 4.48 & 88.10 & 41.86 & 4.80 & 10.76 & 1.66 & 7.39 & 0.48 & 0.62 & 5.54 & 5.81 \\ \hline Vegetables & 3.97 & 3.30 & 1.88 & 10.96 & 92.61 & 84.30 & 0.97 & 0.39 & 0.39 & 0.06 & 14.63 & 13.13 \\ \hline Olive trees & 2.20 & 3.41 & 3.76 & 13.47 & 0.43 & 0.88 & 94.79 & 70.05 & 2.36 & 23.69 & 11.42 & 10.79 \\ \hline Fruit trees & 0.33 & 1.40 & 0.21 & 9.29 & 0.11 & 2.53 & 1.37 & 17.01 & 96.13 & 69.46 & 4.23 & 5.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion Matrix. OCFC = Other Crops Followed by Cereals. \begin{table} \begin{tabular}{c c c c c c c c} \hline \hline
**Classes** & \multicolumn{2}{c}{**Commission (\%)**} & \multicolumn{2}{c}{**Omission (\%)**} & \multicolumn{2}{c}{**Producer’s Accuracy (\%)**} & \multicolumn{2}{c}{**User’s Accuracy (\%)**} \\ \hline
**Classifier** & **SVM** & **RF** & **SVM** & **RF** & **SVM** & **RF** & **SVM** & **RF** \\ \hline Cereals & 0.74 & 1.91 & 11.77 & 12.60 & 88.23 & 87.40 & 99.26 & 98.09 \\ OCFC & 82.93 & 92.26 & 11.90 & 58.14 & 88.10 & 41.86 & 17.07 & 7.74 \\ Vegetables & 20.49 & 19.38 & 7.39 & 15.70 & 92.61 & 84.30 & 79.51 & 80.62 \\ Olive trees & 15.56 & 33.95 & 5.21 & 29.95 & 94.79 & 70.05 & 84.44 & 66.05 \\ Fruit trees & 9.27 & 53.29 & 3.87 & 30.54 & 96.13 & 69.46 & 90.73 & 46.71 \\ \hline \hline \end{tabular}  \endand other crops followed by cereals, represent low precision, with values of 46.71% and 7.74%, respectively. In terms of commission error, the results showed that the other crops followed by cereals represents the highest values for the two classifiers SVM and RF, with values of 82.93% and 92.26%, respectively, followed by vegetables and fruit trees. The other two classes represented a low value for this error. For the omission error, cereals and other crops followed by cereals represented the highest value for the SVM, whereas for RF, cereals and other crops followed by cereals represent a high value of 58.14%, followed by fruit trees with 30.54% and olive trees with 29.95%.
## 3 Signal Model
### OFDM Modulation  
OFDM combines multiple orthogonal narrowband subcarriers into a single wideband signal and it is a core modulation technology for 5G NR systems. Consider an OFDM signal containing \(K\) subcarriers with subcarrier spacing of \(\Delta f\). For modulation symbol \(c_{n,k}\), the useful OFDM symbol \(s_{u}(t)\) is expressed as  
\[s_{u}(t)=\frac{1}{K}\sum_{k=-K/2}^{K/2-1}c_{n,k}e^{|2\pi k\Delta ft}u_{T_{u}}(t) \tag{2}\]where \(T_{u}=1/\Delta f\) represents the useful symbol duration. This paper employs 1024 subcarriers with a subcarrier spacing of 15 kHz. The rectangular window function \(u_{T}(t)\) is defined as  
\[u_{T}=\left\{\begin{array}{l}1,0\leq t<T\\ 0,\text{others}\end{array}\right. \tag{3}\]  
The cyclic prefix (CP) is adopted in the NR system to mitigate inter-symbol interference (ISI) caused by the multipath. CP is a copy of OFDM symbol:  
\[s_{g}(t)=\frac{1}{K}\sum_{k=-K/2}^{K/2-1}c_{n,k}e^{j2\pi k\Delta f(t)}u_{T_{g}} (t) \tag{4}\]  
where \(T_{g}\) denotes the CP duration and is determined by maximum multipath delay.  
CP is appended at the beginning of the useful OFDM symbol and the complete time-domain representation of an OFDM symbol with CP is given by  
\[s_{\text{sym}}(t)=\frac{1}{K}\sum_{k=-K/2}^{K/2-1}c_{n,k}e^{j(2\pi k\Delta f(t ))}u_{T_{\text{sym}}}(t) \tag{5}\]  
where \(T_{\text{sym}}=T_{u}+T_{g}\) is the duration of total OFDM with CP.
## 2 Problem Formulation
### State Separation Property  
In order to derive the explicit solution of the stochastic LQ control problem with conic constraints, Reference [14] gives the following lemma for the state separation property of the LQ problem,  
**Lemma 2** (State Separation [14]).: _In the system (1), for any \(x\in\mathbb{R}\), the optimal solution for problem (3) at time \(l\) is a piecewise linear feedback policy_  
\[\mathbf{u}^{*}(x_{l})=\begin{cases}\hat{\mathbf{K}}^{*}x_{l},&\text{if }x_{l} \geq 0,\\ -\bar{\mathbf{K}}^{*}x_{l},&\text{if }x_{l}<0,\end{cases} \tag{10}\]  
_for \(l=0,1,\cdots\), where \(\mathcal{K}:=\{\mathbf{K}\in\mathbb{R}^{m}|\mathbf{H}\mathbf{K}\in\mathbb{R}_{ +}^{n}\}\) associated with the control constraint sets \(\mathcal{C}_{l}\); \(\hat{\mathbf{K}}^{*},\bar{\mathbf{K}}^{*}\in\mathcal{K}\), are the optimal values of two correspondent auxiliary optimization problems, and the superscript \"\(*\)\" corresponds to the optimal control._  
Based on (10) in Lemma 2, the stationary control can be written as \(\mathbf{u}(x)=\hat{\mathbf{K}}x\mathbf{1}_{x>0}-\bar{\mathbf{K}}x\mathbf{1}_ {x<0}\), where \(\mathbf{1}_{B}\) is an indicator function, such that \(\mathbf{1}_{B}=1\), if the condition \(B\) holds true and \(\mathbf{1}_{B}=0\) otherwise; and \(\hat{\mathbf{K}},\bar{\mathbf{K}}\in\mathcal{K}\). Applying this control, the system dynamics (1) becomes  
\[x_{l+1}= \hat{\mathcal{C}}x_{l}\mathbf{1}_{x_{l}\geq 0}+\bar{\mathcal{C}}x_ {l}\mathbf{1}_{x_{l}<0}\] \[+[\bar{\mathcal{C}}x_{l}\mathbf{1}_{x_{l}\geq 0}+\bar{\mathcal{C}}x _{l}\mathbf{1}_{x_{l}<0}]\bar{\mathcal{C}}_{l}, \tag{11}\]  
for \(l=0,1,\cdots\), where  
\[\hat{\mathcal{C}}=A+\mathbf{B}\hat{\mathbf{K}},\ \bar{\mathcal{C}}=A-\mathbf{B}\bar{ \mathbf{K}}. \tag{12}\]  
Moreover, the performance measure (3) becomes  
\[\eta^{\mathbf{u}}(x)=\lim_{L\to\infty}\mathrm{E}[\sum_{l=0}^{L-1}\hat{W}x_{l}^ {2}\mathbf{1}_{x_{l}\geq 0}+\bar{W}x_{l}^{2}\mathbf{1}_{x_{l}<0}|x_{0}=x],\]  
where \(\hat{W}=Q+\hat{\mathbf{K}}^{T}\mathbf{R}\hat{\mathbf{K}}\) and \(\bar{W}=Q+\mathbf{K}^{T}\mathbf{R}\mathbf{K}\). Therefore, the performance function (4) becomes  
\[f(x)=\hat{W}x^{2}\mathbf{1}_{x\geq 0}+\bar{W}x^{2}\mathbf{1}_{x<0}. \tag{13}\]  
It is easy to verify that \(\hat{W}\) and \(\bar{W}\) are positive semi-definite. We assume that this one-dimensional state system is stable, and then the spectral radiuses of \(\hat{\mathcal{C}}\) and \(\bar{\mathcal{C}}\) are less than 1, i.e., \(C^{max}=max(\hat{\mathcal{C}},\bar{\mathcal{C}})<1\). In the next section, we will derive the performance potentials for the LQ problem, which is the foundation of the performance optimization. Based on this, the Poisson equation and the Dynkin's formula can be derived. The direct-comparison based approach provides a new perspective for this problem,and the results can be extended easily.
## 2 Materials and Methods
### ANN Models  
Experimental data have been used to train the ANN model and parametric analysis has been performed when varying and combining the following parameters and factors:  
The network type is feedforward backpropagation;  
The training function is Levenberg-Marquardt;  
Three successive numbers of layers were used, namely 2, 3, and 4;  
Three numbers of neurons were used, namely 10, 11, and 12.  
A sigmoid function, a special tool of the logistic function to comprehend how a neural network learns to solve difficult problems, is shown in Equation (2). It is employed as an activation function in ANN. It is applied to the weighted sum of inputs in a layer and the outcome is used as input for the following layer. A neuron that uses this S-shaped function is called a sigmoid unit. It changes the model's input variables into values between 0 and 1. It makes it simple for the model to expand to a wide range of data. The variable x is calculated using Equations (3) and (4).  
\[\text{f(x)}=\frac{1}{1+\exp(-\text{x})} \tag{2}\]  
\[\text{x}=\sum_{\text{j=1}}^{\text{m}}\text{w}_{\text{j}}\text{u}_{\text{i}}+ \varnothing_{\text{i}}\text{with j}=1\text{ to n} \tag{3}\]  
Figure 4: (**a**): Aluminum blocks—machine configuration 11: Ra = 1.16 μm (speed 2000 rpm, feed 500 mm/min, radial depth 1.5 mm, and axial depth 20 mm); (**b**) machine configuration 13: Ra = 0.5 μm (speed 2000 rpm, feed 100 mm/ min, radial depth 1.5 mm, and axial depth 20 mm).  
\[\text{x}=\sum_{\text{j}=1}^{\text{m}}w_{\text{kj}}u_{\text{j}}+\varnothing_{\text{k}} \text{with k}=1\text{ to i} \tag{4}\]  
where m represents the number of input nodes; n represents the number of hidden nodes; i represents the number of output nodes; u represents the input node values; v represents the hidden node values; w is the synoptic weight, and \(\omega\) is a threshold.
## 3 Challenges in FSO Communication
### Jamming
Moreover, implementing a higher FoV does not necessitate additional circuitry for pointing and tracking mechanisms, thereby guaranteeing reliable wireless connectivity between the transmitting and receiving apertures. As a result, FSO systems with a higher FoV are less susceptible to misalignment errors, which helps to mitigate the blocking  
Figure 7: FSO detector with (**a**) a narrow FoV and (**b**) a wide FoV. probability caused by obstacles between the source and destination points. This reduces the need for stringent alignment control mechanisms. However, the extensive FoV essential for effective signal reception, coupled with the multiple receiving nodes required in cooperative networks, renders FSO links susceptible to jamming attacks at any node. The detector can easily capture the jamming signal, as illustrated in Figure 7b, creating a significant challenge in differentiating the transmitted signal from unwanted jamming signals [46; 82]. Moreover, in a dual-hop link, an attack that blocks either node will result in the failure of the entire source-relay-destination link. Jamming in point-to-point (p2p) FSO communication is studied in [75; 85], while a similar scenario for cooperative relaying is explored in [86; 87]. In [75], the impact of jamming, where the jammer aims to congest the Rx with unwanted noise, is investigated for FSO communication systems to analyze the outage probability (OP) and bit-error rate (BER) expressions of the system. However, none of these studies address the issue of jamming in the context of aperture averaging and phase error impairment in Exponentiated Weibull (EW) statistics. In [75], it is demonstrated that jamming can severely degrade the performance of single-input single-output (SISO) FSO systems. In [46], the threat to security-constrained reliable FSO communications due to jamming over an exponentially weighted fading channel is examined, taking into account various AT conditions, aperture averaging, and PE. Analytical expressions for the average BER (ABER) and OP are derived for the FSO system under the influence of a random jammer. In [88], jamming for cooperative relaying is further investigated. To address the impact of jamming in FSO communication networks, a buffer-aided relaying approach is proposed. This method employs a relay node with a finite-size buffer and assumes that both relay and destination nodes are susceptible to jamming. Comparisons with a non-buffer-aided (NBA) cooperative FSO system impacted by jamming show that the proposed buffer-aided FSO system considerably outperforms its NBA counterparts.
## 8 Introduction to Proprietary and Standards-based Solutions
### Proprietary Technologies
#### 8.1.3 Telensa  
Telensa is an ultra-narrowband LPWAN technology. It works in 868 MHz and 915 MHz unlicensed ISM bands. It has bi-directional communication capabilities, and hence it can be used for monitoring as well as for control. It has a central management system (CMS) called Telensa PLANet, which is used for end-to-end operations. It has been adopted for intelligent street lighting application. It consists of wireless nodes connecting individual lights in a dedicated network [22]. CMS reduces overall energy consumption and maintenance costs through its automatic fault detection system. These sensor nodes on the street light poles can be used for gathering the data of various parameters such as pollution, noise level, temperature, humidity, radiation level, and so on, as required in the smart city applications. One Telensa base station can control 5000 nodes with low power for around 2 km in urban and 4 km in rural areas. It supports integration with support services such as asset management, metering, and billing systems. Telensa is presently available in more than 30 countries worldwide.
## 5 Performance Testing
### Fingerprint Space Interval Test  
The viability of increasing fingerprint grid spacing to reduce labour at a negligible cost to accuracy was also studied. The same configuration with the deployment density test, using 12 iBeacons on the ceiling, was conducted in Room ZB214, and the fingerprint data were collected at 1 m grids as shown in Figure 8. Grid spacing was compared, ranging from 1 m to 10 m at 1 m intervals.  
Experimenting with fingerprint grid widths represents an opportunity for those implementing BLE-based systems to save time and manpower. The results of collecting fingerprint at different space intervals with 12 iBeacons installed on the ceiling are shown in Figure 11. The CDF from 9 m and 10 m grids were very similar, having the lowest accuracy, which was over 5 m in overall RMSE. This was the result of a lack of fingerprint collection points in the room, since these two intervals collected signals at one point as database only.  
Figure 10: Root-mean-square error (RMSE) of deploying different number of iBeacons.  
Figure 9: CDF comparison of deploying different number of iBeacons.  
When the fingerprint became denser by reducing the interval from 7 m to 2 m, the accuracy improved significantly. The 1 m grid barely outperformed the 2 m grid in terms of positioning accuracy. In the 2 m grid, the RMSE was 1.70 m, which was very close to the result of a 1 m grid at 1.54 m RMSE. The 1 m fingerprint grid had the advantage of producing the highest accuracy, making it a preferred choice when time and manpower are not limiting factors. However, this setting required double effort in collecting the fingerprints compared to the 2 m grid. The 2 m grid was the most balanced grid width choice with regards to accuracy-labour-time trade-offs. If a large-scale deployment was to take place, this might allow significant savings of time and manpower. Figure 11 also suggests that even-number intervals performed better than odd-number intervals for most cases, e.g., 8 m was better than 7 m and 6 m was better than 5 m. The 3 m grid result was substantially less accurate than 2 m and 4 m grid. If the 2 m interval is unachievable because of the limited time or labour resources, selecting even-number intervals can slightly improve the positioning accuracy.
## 2 Methods: FMTL Framework
### FedAvg Algorithm
```
Input: Global shared encoder \(\theta_{sh}(0)\), client datasets \(D_{k}\), number of rounds \(T\), local epochs \(E\), batch size \(B\) for \(t=1\) to \(T\) do Server selects random subset \(S_{t}\subseteq\{\) \(1,\dots,\,K\) (20% of clients) for each client \(k\in S_{t}\) in parallel do Receive \(\theta_{sh}(t)\) from server Update local model via multi-task training for \(E\) epochs: \(\theta_{sh},k(t+1)\), \(h_{stage}\), \(k\), \(h_{sh}\), \(k\)\(\leftarrow\) LocalTrain\((D_{k},\,\theta_{sh}(t),\,B)\) Upload \(\theta_{sh},k(t+1)\) to server if training completed within \(\tau_{k}\) end for Server aggregates: \(\theta_{sh}(t+1)\leftarrow\sum k\in S_{t}\Big{(}n_{k}/\sum j\in S_{t},\ n_{j} \Big{)}*\theta_{sh}k(t+1)\) end for Return: Final global encoder \(\theta_{sh}(T)\)
```  
**Algorithm 1** Federated multi-task training loop (FedAvg).
## 2 Walnut Blight Prevention
If bacterial blight is present in the orchard, it may be necessary to apply fungicides or use other treatments to prevent the disease from spreading, such as artificial techniques for cross-pollination, which probably directly causes or prevents bacterial blight in walnut trees. Apart from the infection of catkins with _X. arboricola_ pv. _juglands_, which plays a role in pathogen dissemination, walnuts produce pollen that is desiccation intolerant. Pollen that does not have homeostatic mechanisms for maintaining a constant water content dies rapidly after opening of the anther or after pollen dispersal [26]. The previously described 'partially hydrated' or, more precisely, desiccation-sensitive pollen of this type may serve as the connection point with _X. arboricola_ pv. _juglands_ when the environmental conditions such as temperature and relative humidity affect pollination processes or are highly favorable for pathogen growth in plants [26]. However, the relationship between this pollen property and walnut blight disease during the flower cycle when the daily temperature and leaf wetness are more favorable for _X. arboricola_ pv. _juglands_ has not been supported with detailed experimental data. The epiphytic colonization of the stigmas of the kiwifruit flower after inoculation by pollen contaminated with GFPuv-labeled _Pseudomonas syringae_ pv. _actinidae_ (Psa), which is responsible for the bacterial canker of the kiwifruit, indicated that Psa is often transmitted to the stigma by pollen contamination [27]. Further, it is well known that plants employ sexual mimicry, and flowers mimic the mating signals of their pollinator insects. Based on the mimicry phenomenon, researchers demonstrated that fire blight, a serious disease of pear and apple trees, requires the combination of warm temperatures, open blossoms, and wet weather. The disease spreads quickly from flower to flower through wind, water, and pollinating insects. So, the specific mimic odors that serve as a tool to enhance honeybee foraging and pollination activities in pear and apple crops spread serious diseases such as a fire blight of pear and apple trees [28; 29]. So, concerning walnut blight disease, the fundamental questions are: Is flower/corolla closure linked to a decrease in viability of desiccation-sensitive pollen? Is this disease related to other pollination activities or phenomena such as mimicry or dissemination by wind? As far as walnut plants are concerned, if pollen serves as a possible pathway for the dissemination of _X. arboricola_ pv. _juglands_ and walnut blight disease, to prevent the disease from spreading, apart from fungicides, it is necessary to use artificial techniques for cross-pollination, as mentioned above, with uninfected pollen (pollen that is free from the above pathogens or diseases).
# Research on Monitoring and Early Warning of the Mine Backfill System Based on Blockchain Technology
## 4 System Experiment and Analysis Evaluation  
### Storage Performance Analysis  
#### 4.1.1 Theoretical Analysis  
In this paper, the original monitoring data information is stored in the IPFS system, and the file identifier generated by the IPFS is stored in the blockchain system, which greatly reduces the length of the blockchain ledger.  
The calculation formula for the storage space compression ratio is shown in Equation (1).  
\[\frac{H+iHash\times N}{H+\sum\limits_{i=1}^{N}T_{X_{i}}} \tag{1}\]  
where \(H\) represents the data volume of all block headers in the blockchain, \(N\) represents the number of all transactions in the blockchain, \(iHash\) represents the size of the IPFS hash corresponding to each transaction, and \(T_{X}\) represents the original data volume of each transaction.  
Among them, the block header takes up 80 bytes, each block contains at least 500 transactions on average, and each transaction takes up at least 250 bytes on average, but an IPFS hash value only takes up 46 bytes. It can be estimated that the optimization of the storage space of this solution is considerable [25]. It is assumed that the average size of each monitoring data point is 1 MB. As each object in the IPFS can store up to 256 kB of data, each monitoring data point needs four IPFS objects, and the hash value generated  
Figure 5: Data query process: (**a**) Flow chart of the administrator querying the data (**b**) Flow chart of user query data.  
by each data object is 46 bytes. As a result, the data on the chain are reduced to about \(1/5698\) of their original size. The reduction in size of blockchain ledger data can optimize the storage space, improve the storage rate of monitoring data, and reduce the access threshold of blockchain nodes. The number of nodes that meet the conditions increases, which makes it more difficult for malicious nodes to modify the ledger, improving the security performance of the system.
# Permittivity of Undoped Silicon in the Millimeter Wave Range
## 6 Conclusions  
The permittivity of undoped silicon was measured using a quasi-optical system. The system was designed to create a quasi-plane wave that was preferable for the dielectric measurement millimeter wave range. A multi-layer model was employed for parameter retrieval. The process takes into account multi-path propagation and equals the reflection and transmission coefficients to the scattering parameters in a two-port network. Undoped silicon was measured at the E-band (60-90 GHz). It was found that the undoped silicon exhibited stable permittivity over the whole band and in the temperature range of 20-30 \({}^{\circ}\)C. It was found that the measurement was in agreement with the provided value and results in the literature. In addition, it was demonstrated that silicon has very weak dispersion below 450 GHz.  
Data curation, X.Y. and S.Y.; Investigation, X.L.; Methodology, L.G. and J.Z.; Resources, Y.Z.; Writing--original draft, X.L.; Writing--review and editing, X.Y., and J.Z.  
This work was supported by the National Natural Science Foundation of China, Grant Numbers: 61871003, 61601472, 61505022, the Open Project of the State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System under the number of CEMEE2019Z0203B, and the Natural Science Foundation of Anhui province under the contract number of 1708085 QF133.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Real-Time PCR  
RAW264.7 macrophage cells were seeded into 6-well plates at a density of 2 \(\times\) 10\({}^{6}\) cells/well. The cells were then treated with KRG-P (10, 25, and 50 \(\upmu\)g/mL) for 6 h, the control group was treated with DMEM medium, and the positive group was treated with LPS (500 ng/mL). Total RNA was extracted from the cells using the RNeasy mini kit and cDNA was synthesized using AccuPower(r) RocketScript(tm) Cycle RT PreMix (Bioneer, Daejeon, Korea). All quantitative real-time PCR reactions were performed with Power SYBR Green PCR Master Mix (ThermoScientific, Waltham, MI, USA) using the Quant Studio 3 real-time PCR system (Applied Biosystems, Waltham, MA, USA). The following forward and reverse primers were used for amplification: \(\beta\)-Actin forward primer: ATCACATATTGGCAACGACG-3, \(\beta\)-actin reverse primer: TCAGCAATGCCTGGGTACAT; IL-6 forward primer: GAGGATACCACTCCAACAGACC, IL-6 reverse primer: AAGTGCATCATCGTTGTCATACA; TNF-\(\upalpha\) forward primer: GCCTTTCTCATTCCTGTGT, TNF-\(\upalpha\) reverse primer: CTGATGAGGGGGCCATT; NOS2 forward primer: GAGAACAGACC, NOS2 reverse primer: AATGTTCTACA. All of the experiments included a non-template control group, which was treated with DEPC water instead of cDNA to eliminate the possibility of reagent contamination.