esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Dataset for Fabric Defect Detection: Bridging Gaps in Anomaly Detection
## 2 Related Work  
### One-Class Classification  
A one-class classification is an approach that involves labelling only a single class in a dataset so that any mild deviation from these data is classified as an anomaly. This method is widely applied in financial fraud detection, cybersecurity, healthcare, and automated visual inspection [10]. The primary advantage of this approach is its ability to detect anomalies without needing labelled data for every possible class of anomalies, which is especially useful in rare or diverse scenarios.  
Various techniques exist to handle the problem of texture defect detection, including statistical, transform-based, model-based, and graph-based approaches. Each of these methods offers distinct advantages and drawbacks. More recently, deep learning-based feature extraction has produced outstanding results in identifying important patterns. For instance, supervised training methods [11] are often used in industrial settings for defect detection. However, gathering data for every possible defect is laborious and may lead to poor performance if some defect types are not considered.  
Unsupervised approaches, such as autoencoders [12], suffer from high generalization capabilities, making them less effective for specific defect types. To improve performance, knowledge distillation [13] has been adapted for unsupervised anomaly detection [14; 15]. This involves training a student network on normal samples using the output of a pre-trained teacher network. During testing, the student network replicates the features of the teacher network for normal samples but fails to do so for anomalous samples, allowing for the computation of a relevant anomaly score.  
Several one-class classification methods have been developed, including but not limited to PatchCore [16], FastFlow [17], PaDiM (Patch Distribution Modeling) [18], CutPaste [19], Uninformed Student [20], and Patch SVDD [21]. [PERSON] et al. used one-class classification for fabric defect detection with mixed results [22]. In our study, we evaluated two methods, DBFAD [23] and RD4 AD [24], chosen based on their availability as open-source code and their suitability for one-class classification.  
The advantages of the selected methods are that they are effective in scenarios where anomalies are rare or diverse, as they do not require labelled data for every possible anomaly.
## 5 MADM Algorithm Based on q-ROFAAWA  
This section contains information using q-ROSF data to design a methodology and apply the proposed operators in MADM.  
Consider \(a=\left\{a_{1},a_{2},\ldots,a_{m}\right\}\) are \(m\) alternatives for selection, let \(J=\left\{j_{1},j_{2},\ldots,j_{n}\right\}\) are \(n\) attributes with WV, \(\zeta\). Let the q-ROSF data be \(H=\left(\tau_{ij}\right)_{m\times n}\) in the form of a matrix where q-ROFN \(\tau_{ij}=\left(\alpha_{ij},\ \beta_{ij}\right)\) represents the value of the characteristic that the decision-maker (DM) provides for the alternate \(j_{n}.\tau_{ij}=\left(\alpha_{ij},\beta_{ij}\right)\) show alternative evaluation values where \(0\leq\alpha_{ij}{}^{q}+\beta_{ij}{}^{q}\leq 1\). As a result, the q-ROF decision matrix \(H=\left(\tau_{ij}\right)_{m\times n}\) is formed with the help of q-ROF information.  
In this scenario, to select the best possible option, we construct the algorithm by using the q-ROFAAWA and q-ROFAAWG operators to explain the MADM issue in the q-ROF environment. The following steps of this algorithm are discussed below:  
1. First, the q-ROF decision matrix is formed, which is further into the normalized decision matrix. \[\overline{\tau_{ij}}=\begin{cases}\tau_{ij}&\text{benefit attribute}\\ \tau_{ij}^{c}&\text{cost attribute}\end{cases}\]  
Let \(\tau_{ij}\) and \(\tau_{ij}^{c}\) be the benefit and cost attributes of the decision matrix, respectively. There is no need to change any of the attributes if they are of the same type. Both categories must be changed if there are two different categories (cost and benefit).
2. For alternatives \(\zeta_{i}\) aggregate all the values of \(\tau_{i}\) with the help of q-ROFAAWA operator is given by: \[q-ROFAAWA\ (\tau_{1},\ \tau_{2},\ \ldots,\tau_{n})=\underset{\zeta=1}{\oplus}( \zeta_{i}\tau_{i})=\left(\sqrt[q]{1-e^{-\left(\sum\limits_{i=1}^{n}\left(( \zeta_{i}(-\ln\left(1-(\alpha_{\tau_{i}}\right)^{q})^{N}\right)^{\frac{1}{N}} \right)\right)}}e^{-\left(\sum\limits_{i=1}^{n}\left(((\zeta_{i}(-\ln\left( \beta_{\tau_{i}}\right)^{N})^{\frac{1}{N}}\right))\right)}\right)\] And \[q-ROFAAWG\ (\tau_{1},\ \tau_{2},\ \ldots,\tau_{n})=\underset{i=1}{\otimes} \left(\tau_{i}^{c}\right)=\left(e^{-\left(\sum\limits_{i=1}^{n}\left(((-\ln \left(\alpha_{\tau_{i}}\right)^{q})^{N}\right)^{\frac{1}{N}}\right)\right)^{ \zeta_{i}}}\sqrt[q]{1-e^{-\left(\sum\limits_{i=1}^{n}\left(((-\ln\left(1-\beta _{\tau_{i}}\right))^{N}\right)^{\frac{1}{N}}\right)\right)^{\zeta_{i}}}}\] Step 3. Calculate the score value by applying this SF provided by [PERSON] et al. [36], which is given by: \[[labelsep=9 mm]\mathcal{S}(\tau)=\alpha_{i}^{q}-\beta_{i}^{q}\] Step 4. We arrange the ranking values of all of the options to choose the best one while keeping \(\zeta_{i}\) in mind.
## 5 Discussion  
### Effect of Parameters Besides Wetness on ROD Extraction from SAR Data  
Wetness is the primary parameter influencing the SAR backscatter during the snowmelt period. The theoretical basis for using SAR backscatter to extract the ROD is that the date of maximum wetness corresponds to the date of minimum backscatter, which defines the ROD. However, under certain conditions, other parameters besides wetness may cause the two dates to be inconsistent, resulting in inaccurate ROD extraction based on the backscatter.  
Besides wetness, other intrinsic attributes of snow such as density, grain size, depth, and surface roughness also have an influence on the SAR backscatter [24; 40; 41]. The impact of SWE on the ROD discussed in Section 4.3 can be seen as the combined effect of density and snow depth. Additionally, SAR parameters, such as the polarization mode and local incidence angle, as well as underlying surface parameters, such as type and roughness, also affect the SAR backscatter. During the melting process, the magnitude of their respective influences also changes, thereby limiting the accuracy of ROD extraction to varying degrees.  
Extracting the ROD based on wetness is an effective way to improve the accuracy, but the multitude of backscatter-influencing parameters makes it challenging to retrieve snow wetness using SAR data. Existing retrieval algorithms mainly use full polarization [42; 43; 44] or VV and HH polarization data [45; 46]. However, these algorithms are not applicable to Sentinel-1 data, making it impossible to extract the ROD based on the variations in wetness. Therefore, quantitatively assessing the role of different parameters in snow wetness retrieval  
Figure 10: Correlation between the ROD extracted from Sentinel-1 data and the SWE under different land cover types (**(a)** tree cover, **(b)** shrubland, **(c)** grassland, and **(d)** bare land).  
and developing more universal wetness retrieval algorithms will help improve the accuracy of ROD extraction.
# An Improved Method for Named Entity Recognition and Its Application to CEMR+
## 4 Experiments and Analysis
### Evaluation Metrics  
The accuracy (P), Recall (R), and F1 value proposed at the MUC-6 meeting were used for NER. The specific indicators in the indicator system are as follows: The sample was divided into positive and negative; if the sample was positive, the number predicted by the model as positive was recorded as TP; if the sample was negative, the number predicted by the model as positive was recorded as FP; if the sample was negative and was modeled as suchthe number predicted to be negative was denoted as TN; the number of samples being positive and predicted by the model as negative was denoted as FN.  
\[P=\frac{TP}{TP+FP} \tag{13}\]  
\[R=\frac{TP}{TP+FN} \tag{14}\]  
\[F1=\frac{2 PR}{P+R} \tag{15}\]  
Accuracy is the ratio of the number of correctly-identified named entities to the number of named entities identified in the experiment; the recall rate is the ratio of the number of correctly-identified named entities to the total number of named entities in the sample. The F1-value is the harmonic average of the accuracy and recall rate.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Module** & **Parameter Name** & **Value** \\ \hline \multirow{4}{*}{Word Representation} & word embedding dim & 256 \\  & char embedding dim & 64 \\  & POS embedding dim & 64 \\  & position embedding dim & 128 \\ \hline \multirow{4}{*}{Iterated Dilated CNNs} & filter width & 3 \\  & numfilter & 256 \\  & dilation & [1,1,2] \\  & block number & 4 \\ \hline \multirow{4}{*}{Other} & learning rate & 1e-3 \\  & dropout & 0.5 \\  & gradient clipping & 5 \\  & batch size & 64 \\  & epoch & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter setting for the modules.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Word Feature** & **POS Feature** & **Description** \\ \hline \(U01\):\%W[\(-1\),\(0\)] & \(U06\):\%P[\(-1\),\(0\)] & previous word (POS) \\ \(U02\):\%W[\(0\),\(0\)] & \(U07\):\%P[\(0\),\(0\)] & current word (POS) \\ \(U03\):\%W[\(1\),\(0\)] & \(U08\):\%P[\(1\),\(0\)] & next word (POS) \\ \(U04\):\%W[\(0\),\(0\)] \%W[\(-1\),\(0\)] & \(U09\):\%P[\(0\),\(0\)] \%P[\(-1\),\(0\)] & current word and previous word (POS) \\ \(U05\):\%W[\(0\),\(0\)] \%W[\(1\),\(0\)] & \(U10\):\%P[\(0\),\(0\)] \%P[\(1\),\(0\)] & current word and next word (POS) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Feature templates of the CRF model.
## 4 Results  
### Getting the DestPos and DistMx  
The basis for creating the DistMx matrix is a list of destinations (DestPos), containing at least the destination identifier (DestId) and geographical coordinates (latitude, longitude). Location coordinates are the basis for obtaining the distance value. A matrix with dimensions N \(\times\) N will be created for N destinations, where N \(\times\) (N-1) independent values exist. In the first step, an up-to-date list of destinations with their coordinates was obtained. Using the APIs the list includes all necessary 203 points (1 depot and 202 destinations) was downloaded. The presented destinations were located in an area estimated 160 (E-W) \(\times\) 145 km (N-S). The spatial distribution of destinations is shown in Figure 2.  
Next, to create a distance matrix, all destinations' identifiers and geographical coordinates should be provided as starting and ending points. This service takes the following arguments: coords_src (list of geographical coordinates of the starting points), coords_dest (list of geographic coordinates of endpoints), ds_origin (list of identifiers of starting points) and ids_dest (list of endpoint identifiers). If a new destination appears, there is no need to recalculate the entire matrix. An additional script has been created, which works in such a way that it calculates the distances between a particular destination and the rest of the destinations. Thus, iterating over N destinations creates an N \(\times\) N matrix. This way is to work around the limit on the engine side of the simultaneous number of data sent in the query. To add a new destination, it will only need to calculate one new column for a given destination, and transpose that column into a row, which then also add to the distance matrix. Such a solution is beneficial in the situation of a dynamically changing list of destinations. It has been experimentally found that updating the distance table in the event of an additional destination takes an average of a few seconds.
## 6 Conclusions  
In this paper, we have proposed an FD C-NOMA system where the BS serves multiple geographically separated users over the downlink. All users receive the information with the assistance of a FD relay node. The performance of this FD C-NOMA multi-user system is analyzed by considering the impact of channel estimation errors, the resiual LI, and low-resolution ADC. Furthermore, an accurate approximate expression of the ergodic rate and a closed-form expression of the outage probability for each user are presented. The accuracy of each theoretical result was verified by Monte Carlo simulations. The obtained results have shown that the performance is influenced by the LI level, channel estimation error, and the quantization bits in ADC. Both the outage and ergodic rate performance encounter floors caused by high channel estimation errors and LI. Moreover, the FD C-NOMA mode is degraded at high SNRs due to a high residual LI. It also can be observed that the outage and ergodic rate improve as the number of quantization bits increases. However, the performance improvement tends to flatten out as quantization bits continue to increase.  
Investigation, methodology, and supervision, S.W.; simulation and validation, Y.H.; conceptualization and formal analysis, Y.Y.; writing--original draft preparation and visualization, Y.H. and Y.Y.; writing--review and editing, S.W. All authors have read and agreed to the published version of the manuscript.  
This paper is supported by National Natural Science Foundation of China (Grant 61971061) and Beijing Natural Science Foundation (Grant L223026).  
Not applicable.  
This work is conducted in Information Theory and Technology Center (ITTC) of Beijing University of Posts and Telecommunications. The authors would like to thank the reviewers for several insightful comments and suggestions which helped us to improved the paper.  
The authors declare no conflict of interest.
# Life Cycle Assessment of Land Use Trade-Offs in Indoor Vertical Farming +
## 3 Results and Discussion
### Limitations and Future Studies  
This study was subjected to several limitations that must be acknowledged. Firstly, the development of a reliable and detailed Life Cycle Inventory required the integration of data from three different databases. This integration may introduce uncertainties and potential errors, as each database is structured and developed differently. The Inventory was also compiled for a prospective technology as the unit is not yet fully operational, and therefore some activity data may differ once the farm begins production. Additionally, land use effects are notably local and can vary substantially even within relatively small regions. Life Cycle Inventories are not yet fully regionalized and, even when land use flows are regionalized, they are represented as country level averages, which may be insufficient detail. Further, the LANCA model still requires further refinement, and some limitations may affect the reliability of the aggregation indices used in the model. The most significant limitation is its reliance on generalized characterization factors (CFs) based on either global or country-level averages, which may lead to inaccuracies in varied ecological contexts [29; 39; 71; 72; 73].  
Additional studies are needed to implement the LCA to more IVFs and enable a proper comparison among studies to assess the effectiveness of the sustainability of said systems. These may include different substrates and crops to assess the suitability to be cultivated in such systems. Future research might also develop a Life Cycle Costing analysis to evaluate the suitability and performance of a photovoltaic system, included in this study as a potential prospective technology.  
While this study focuses on the role of energy use on the performance of a single IVF system, it does not include comparisons with conventional farming. Such comparisons would require different methodological assumptions, system boundaries, and life cycle data for conventional products replaced by microgreens grown in this specific IVF. Product replacement, and therefore their selection for comparison, has to be determined on a case-by-case basis as it depends on the study area location, the season and time of the year, and market availability. As the system is not yet being commercially exploited, we were unable to determine the replaced products, preventing any comparisons. Nonetheless, these comparisons remain important and should be pursued in future research to fully contextualize the environmental performance of vertical farming systems.
## 2 Related Work  
### Traditional Energy Efficient Routing Protocols  
In the single-hop communication model, each sensor node communicates with the cluster head within a single-hop, and the cluster head transfers the sensed data directly to the sink (base station). Low Energy Adaptive Clustering Hierarchy (LEACH) [6] is a cluster based protocol that randomly rotates cluster heads to evenly distribute the energy load among the sensor nodes in the network. LEACH is able to incorporate data fusion into the routing protocol to reduce the amount of information that must be transmitted to the sink. This largely reduces the energy dissipated since the computation requires a negligible amount of energy compared with communication. LEACH uses single-hop routing where the cluster head transmits directly to the sink. However, this is infeasible in large scale sensor networks comprised of thousands of sensor nodes, due to the limited transmission power of sensor nodes.  
The multi-hop communication model [20; 21] is a more practical approach to solve this problem. In this model, data hops from one node to another until it reaches the sink. In view of the limited transmission range of the sensor nodes, this is a viable approach and most nodes can connect and transmit their packets to the sink. Therefore, the coverage area of the sensor nodes and the sink in this model is an improvement over the single-hop model. Sensor Protocols for Information via Negotiation (SPIN) [20] efficiently disseminates information in a multi-hop manner. Nodes running SPIN assign a high-level name to their data, called meta-data, and perform meta-data negotiations before any data is transmitted. This assures that there is no redundant data sent throughout the network. In addition, SPIN has access to the current energy level of the node and adapts the protocol for WSNs. However, since all nodes participate in the route discovery phase, the multi-hop routing protocols may cause data communication problems, such as broadcasting storm and unnecessary communication overhead. Consequently, applying these approaches to large-scale sensor networks might considerably increase the overhead and associated costs.
# Collision Avoidance Adaptive Data Rate Algorithm for LoRaWAN
## 6 Results and Discussion
### Uplink Packet Delivery Ratio  
Figures 6 and 7 illustrate how the proposed SSFIR-ADR1 and SSFIR-ADR2 algorithms perform in terms of the uplink packet delivery ratio. Both variants of SSFIR-ADR exhibit improved UL-PDR compared to other algorithms. The primary objective of the proposed ADR algorithm is to reduce energy utilisation while preserving a high packet delivery ratio by mitigating interference and optimising data rates for end devices. However, due to the limited transmission parameters that can be adjusted by ADR in LoRaWAN, complete elimination of collisions is not feasible. As network density increases, the potential number of interferers during each transmission also rises. In this scenario, the proposed ADR algorithm surpasses all the algorithms used for comparison, highlighting its effectiveness in reducing collisions. For instance, at a network density of 200 EDs and an application data interval of 600 s, the standard ADR algorithm achieves a PDR of 0.72, the ns3-ADR algorithm achieves 0.88, the FL-ADR scheme achieves 0.64, SSFIR-ADR1 achieves 0.90, and SSFIR-ADR2 achieves 0.92. These results indicate an approximate improvement of 18% and 20% in UL-PDR compared to the standard ADR algorithm for SSFIR-ADR1 and SSFIR-ADR2, respectively. While FL-ADR outperforms SSFIR-ADR with reference to energy consumption, it falls short in achieving a comparable packet delivery ratio. This result showcases the balance between energy consumption optimisation and PDR performance.  
Figure 5: Number of EDs vs. Total consumed energy.
## 4 Hardware Implementation and Tests of Proposed RMEDS-SP Simulation Model  
### Hardware Implementation of RMEDS-SP  
The proposed RMEDS-SP with a simulated error compensated by CPF has shown a higher accuracy and lower complexity than other models. We would further implement this model to the hardware and construct the test environment to analyze the performance.  
The hardware implementation principle of the RMEDS-SP model is shown in Figure 14. First, we have to configure the parameters of the hardware implementation according to the model of RMEDS-SP. The parameters include the amplitude \(c_{i,n}\), frequency \(f_{i,n}\), phase \(\theta_{i,n}\), the number of partition sets, and the convergence coefficient of different sinusoids. Then, the DPS would be implemented by two unrelated colored Gaussian processes, which get the sum of them and configure the maximum DFO \(f_{max}\) and the number of sinusoids. Then, we use CPF to process the generated signals and improve its ACF. Figure 15 shows the framework of hardware implementation of RMEDS-SP. There are three key parts: The RMEDS-SP module, CPF module, and DPS module.  
Figure 14: Hardware implementation of RMEDS-SP model.  
Figure 13: DPS of RMEDS-SP and after error compensations.  
* A part: RMEDS-SP module  
The hardware implementation of RMEDS-SP can be shown in Figure 15. The functions of this module are realized by SOS with the configuration of the number of sinusoids and the number of simulations time, \(N_{1}=20,N_{2}=21,K=4\). There are real parts \(Z_{1}(t)\) and imaginary part \(Z_{2}(t)\) of the channel. A three-layer loop structure is exploited. The first loop layer is to generate 1000 sampling points in a period, which would be the input parameters of sinusoids. The second loop layer is to superpose the sinusoid with the parameter of \(N_{1}=20,N_{2}=21\). The SOS would be calculated by the sum of the array from the first layers through shift registers. The third loop layer is to part the AOA into 4 sets and calculate the average.  
* B part: CPF module  
The CPF module can be shown in Figure 15 This module is used to calculate the ACF of the real part and the imaginary part of the channel. As Figure 15 shows, the CPF module would exploit the if-else function to realize the piecewise function to converge the error. By multiplying the results of SOS from the RMEDS-SP module in the time-domain, the SEF of ACF decreases, which converges the diversion of processing unrelated colored Gaussian processes. Then, the results would export to the next module and get the channel response of the DPS.  
* C part: The DPS implementation module  
The DPS module can be shown in Figure 15 This module is used to introduce the real response of DPS to the input signals. The real part and imaginary part of the DPS response  
Figure 15: Framework of hardware implementation of RMEDS-SP.  
would be superposed to the output signals. These signals already have the response of DPS of LEO-SIN.