esa-sceva/satcom-chunk-collection huggingface dataset:
# A Rapid Method for Information Extraction from Borehole Log Images
## 4 Corner-Based Cell Segmentation
### Preprocessing of the borehole Log  
Due to the accuracy of the equipment, there are often some errors in the scanned images of the borehole log. Therefore, the images need to be preprocessed to eliminate irrelevant information in the image for better results of feature extraction, image segmentation, matching, and recognition [20].  
The preprocessing of the borehole log image in this method is divided into two stages. The first stage is to convert the color image into a gray image, and the second stage is to binarize the gray image. The color of each pixel in the color borehole log image contains three components of R, G, and B, thus, there are more than 16 million color changes per pixel. Whether it is table line extraction or text recognition, color difference is not necessary. Therefore, we need to convert the image to grayscale to filter this information.  
In this paper, the global mapping method is used to convert the image to grayscale. With this method, each pixel uses the same transform function. The function is shown in Equation (1):  
\[\text{gray }=0.299\text{R }+0.587\text{G }+0.114\text{B} \tag{1}\]  
Figure 3: Information extraction process of the borehole log.  
Since the global mapping method performs grayscale on the same mapping function for all pixels in the entire image, the global structure information can be well maintained. In order to outline the image clearer, it is necessary to binarize the grayscale image. The image binarization converts the pixel values of the grayscaled image to 0 or 255 by selecting an appropriate threshold. When the pixel value of the image is less than or equal to the threshold t, it is set to 0, which is the foreground (for example, the text portion or the table segment portion). When the pixel value of the image is greater than the threshold t, it is set to 255, which is the background (such as the blank region). The calculation process is as shown in Equation (2).  
\[P_{xy}=\left\{\begin{array}{c}0,v_{xy}\leq t\\ 255,v_{xy}>t\end{array}\right. \tag{2}\]  
where \(x\) and \(y\) are the column number and row number of the pixel, respectively, \(P_{xy}\) is the binarized pixel value, and \(V_{xy}\) is the pixel value before binarization.
# Investigating the Effects of Fish Effluents as Organic Fertilisers on Basil (_Ocimum basilicum_)
## 2 Materials and Methods
### Substrate Mixes  
A review of the physical, chemical, and biological properties of soil ecosystems revealed that there appears to be no clear definition of what constitutes soil; this is reflected in Regulation (EU) 2018/848 [1], where no definition of'soil', nor of what constitutes soil, is given. With regard to the experimental setup, it was considered that a comparison between a substrate mix with compost and one without compost would be useful in order to determine the influence of compost on the substrate that was exposed to fish effluents. It was therefore decided to include both substrate types, one nutrient-rich where compost was included, and one nutrient-poor without compost. To ensure that the substrates were as similar as possible in composition and texture, both substrates were manufactured from raw ingredients using the Royal Horticultural Society guidelines for creating a John Innes Potting Compost [40]. Two substrate types were thus devised, using only materials approved in organic certification: a substrate without compost, having 3 parts loam, 3 parts sand, and 5 parts coir; and a substrate with compost, having 3 parts loam, 3 parts sand, 3.5 parts coir, and 1.5 parts compost (13.6%). The two substrate types were analysed for nutrients and physicochemical parameters (Table 2).  
All nutrient values, except for manganese and calcium, were higher in the compost substrate, reflecting the higher nutrient load supplied by the compost.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Parameter** & **Tap Water 1** & **Fish Water 2** & **Fish Sludge 2** \\ \hline pH & 7.60 3  \\ Calcium carbonate & 323 ppm & - & - \\ concentration & 594 \(\mu\)S/cm & - & - \\ Conductivity at 25 \({}^{\circ}\)C & - & 791 \(\mu\)S/cm & 1020 \(\mu\)S/cm \\ Turbidity & \textless{}0.09 FTU & - & - \\ Ammonium as NH\({}_{4}\) & 0.12 mg/L & - & - \\ Nitrate as NO\({}_{3}\) & 27.2 mg/L & 44.3 mg/L & 69.6 mg/L \\ Nitrate/nitrite calculation & 0.55 mg/L & - & - \\ Nitrite as NO\({}_{2}\) & 0.013 mg/L & - & - \\ Hardness (total) as CaCO\({}_{3}\) & 244 mg/L & - & - \\ Iron as Fe & 3.6 ug/l & - & - \\ Magnesium & 4.3 mg/L & 12.33 mg/L & 18.34 mg/L \\ Alkalinity (HCO\({}_{3}\)) & - & 36 mg/L & 13 mg/L \\ Sulphate (SO\({}_{4}\)) & - & 161.8 mg/L & 204.4 mg/L \\ Boron & - & 0.01 mg/L & 0.03 mg/L \\ Sodium & - & 44.0 mg/L & 50.5 mg/L \\ Chloride & - & 59.2 mg/L & 69.8 mg/L \\ Phosphorus as P & - & 4.4 mg/L & 12.8 mg/L \\ Potassium & - & 1.5 mg/L & 3.5 mg/L \\ Calcium & - & 112.6 mg/L & 136.4 mg/L \\ Carbonate & - & \textless{}10 mg/L & \textless{}10 mg/L \\ Total dissolved solids & - & 553.7 mg/L & 714 mg/L \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the tap water, fish water, and fish sludge.
## 3 Results and Discussion
### Thermal Conductivity and Thermal Diffusivity  
The thermal conductivity and thermal diffusivity of a PCM are important factors in assessing the thermal performance of a PCM in the solid and liquid states [56]. Improving thermal performance is crucial to decreasing the energy consumption for heating and cooling. The thermal conductivities of the PCMs were evaluated under fully frozen conditions (in the solid state before the onset temperature) and fully melted conditions (in the liquid state after the melting temperature). The thermal conductivities were measured ten times per specimen. The thermal diffusivities under endothermic processes were investigated due to supercooling issues under exothermic processes.  
Figure 5 shows the thermal conductivities of specimens 1 to 4. Specimens 1 to 4 under solid phases had thermal conductivities of 81, 79, 112, and 113 mW/mK, respectively. The specimens under liquid phases had thermal conductivities of 77, 75, 104, and 108 mW/mK, respectively. As shown in Figure 5, the thermal conductivities of the PCMs in the solid state were higher than those in the liquid state. This phenomenon occurs because of the effect of the microstructure changing from an orderly solid structure in the solid state to a disorderly liquid structure in the liquid state [57].  
The thermal diffusivities of the PCMs were obtained using Equation (1). The calculated thermal diffusivities are listed in Table 4. The diffusivities are about one order of magnitude lower than those of the commercial product or those reported in other studies [3; 42; 49; 50; 51; 52]. During the endothermic process, the PCMs under the solid phase showed less superheating due to the relatively high thermal diffusivity. On the other hand, the low thermal diffusivity in the liquid phase accelerated supercooling during the exothermic process. Low diffusivity in the liquid phase can cause nonuniform solidification and, resultantly, phase segregation and supercooling.  
Figure 5: Thermal conductivities of the PCMs.  
In order to analyze the thermal behavior of the PCMs used in this study, the Fourier numbers, which denote the ratio of diffusive or conductive transport rate to the quantity storage rate, were compared with those of the references. In the DSC measurement for an organic PCM with a similar melting temperature [58; 59], the Fourier number was 103.5 at a heating rate of 0.5 \({}^{\circ}\)C/min. In the DSC for an inorganic PCM with a similar melting temperature [42; 60], the Fourier number was 0.11 at a heating rate of 5 \({}^{\circ}\)C/min, which is much lower than that of the organic PCM in references [58; 59]. However, the Fourier numbers of the PCMs used in this study were between 0.0036 and 0.0063 for the solid phase and between 0.0026 and 0.0043 for the liquid phase, which are much lower than the inorganic PCM in references [42; 60]. In the DSC measurement, a large enough Fourier number is necessary for uniform phase change, and the heating/cooling rate should be designed by considering the diffusivity and Fourier number.
## 4 System Model
### Threat Model  
The proposed SDWSN environment faced several challenges in terms of cybersecurity issues. Some of the major points in the threat model are provided below:  
* This work highlights the security of the WSN nodes and edge-assisted switches against MITM, eavesdropping, Denial of Service (DoS), Flow Table Attacks (FTA), and Packet Mistreating Attacks (PMA). Those attacks are held in both the infrastructure and edge-assisted switch layers to mislead the routing information.  
* The reconnaissance attacks are in the edge-assisted switch layer and the SN for manipulating the SDN switches and SNs. The malicious SDN switch can redirect the flow to the malicious destination. Further, the malicious SN causes the WSN nodes to quickly drain out and also causes collision during scheduling.
## 4 The Proposed TransTLA Method
### Temporal Convolutional Network Layer
#### 4.1.2 Residual Connections  
Simply increasing the dilation rate without adding network layers may not maximize the network's learning capabilities. Adding depth can expand the receptive field but may also cause vanishing or exploding gradients. To address these challenges, TCN utilizes residual connections, which integrate skip connections to facilitate a gradient flow directly from earlier layers, mitigating gradient problems and maintaining training stability even with the increased network depth. The network structure of the residual connections is illustrated in Figure 3. The mathematical representation of the residual connections is given as follows:  
\[y_{t}=F(x_{t},\theta_{t})+x_{t}, \tag{3}\]  
where \(y_{t}\) is the output of the residual connection, \(x_{t}\) is the original signal input to the residual block, and \(F(x_{t},\theta_{t})\) is the transformation within the residual block with parameters \(\theta_{t}\).  
By integrating residual connections, TCN enhances the training efficiency and stability while expanding the receptive field, which is essential for capturing long-term dependencies in time series data. In practice, TCNs are often constructed by stacking multiple residual blocks, each incorporating dilated causal convolution layers to broaden the receptive field and preserve the time series causality.  
TCN's combination of dilated causal convolution and residual connections makes it a robust framework for time series analysis. This makes TCN well suited for forecasting tasks, providing precise predictions and strong generalization in home appliance sales forecasting. With an optimized architecture and training approach, TCN effectively extracts valuable features from time series data, serving as a powerful tool for subsequent feature extraction and TL. Despite these advantages, TCNs can be computationally expensive and memory-intensive, especially when dealing with very deep networks or long input sequences.
## 3 Discriminative Regional Weight
### Object Detection Algorithm  
As shown in Figure 7, the proposed algorithm demonstrates a two-stage template-based procedure using Orientation Compressing Map and Discriminative Regional Weight (OCM-DRW) to effectively detect texture-less objects. In the first stage, the orientation compressing map of the template (CMT) can be compared with the orientation compressing map of the input image (CMI) pixel by pixel according to the bitwise operation, and a set of possible object locations with different scale and rotation possible object locations are generated. In the second stage, according to the scale and rotation of each possible object location, the appropriate template is selected and matched with the possible object location based on the discriminative regional weight.
# Towards Federated Learning with Byzantine-Robust Client Weighting
## 2 Materials and Methods
### Problem Setup
#### 2.1.3 Federated Learning Meta Algorithm  
We build upon the baseline federated averaging algorithm (_FedAvg_) described by [PERSON] et al. [2]. There, it is suggested that in order to save communication rounds, clients perform multiple stochastic gradient descent (SGD) steps while a central server occasionally averages the parameter vectors.  
The intuition behind this approach becomes clearer when we mark the \(k\)th client's ERM objective function by \(F_{k}(w)\coloneqq\frac{1}{n_{k}}\sum_{z\in Z_{k}}\ell(w;z)\) and observe that the objective function in Equation (1) can be rewritten as a weighted average of clients' objectives:  
\[F(w)\coloneqq\frac{1}{n}\sum_{k\in[K]}n_{k}F_{k}(w). \tag{2}\]  
Similarly to previous works [10; 13; 14], we capture a large set of algorithms by abstracting _FedAvg_ into a meta-algorithm for the FL (Algorithm 1). We require three procedures to be specified by any concrete algorithm:  
1. _Preprocess_--receives possibly Byzantine \(\hat{n}_{k}\)s from clients and produces secure estimates marked as \(\hat{n}_{k}\)s. To the best of our knowledge, previous works ignore this procedure and assume that the \(n_{k}\)s are correct.
2. _ClientUpdate_--per-client \(w_{k}\) computation. In _FedAvg_, this corresponds to a few local mini-batch SGD rounds. See Algorithm 2 for the pseudocode.
3. _Aggregate_--the server's strategy for updating \(w\). In _FedAvg_, this corresponds to the weighted arithmetic mean, i.e., \(w\leftarrow\frac{1}{\tilde{n}}\sum_{k\in[K]}\hat{n}_{k}\hat{w}_{k}\).  
```
1:\(\{\hat{n}_{k}\}_{k\in[K]}\leftarrow\) collect sample size from clients
2:\(\{\bar{n}_{k}\}_{k\in[K]}\leftarrow\) Preprocess\((\{\hat{n}_{k}\}_{k\in[K]})\)
3:\(w\leftarrow\) initial guess
4:for\(t\gets 1\)to\(T\)do
5:\(S_{t}\leftarrow\) a random set of client indices
6:for all\(k\in S_{t}\)do
7:\(\hat{w}_{k}\leftarrow\) ClientUpdate\((w)\)
8:endfor
9:\(w\leftarrow Aggregate(\{\langle\bar{n}_{k},\hat{w}_{k}\rangle\}_{k\in S_{t}})\)
10:endfor
```  
**Algorithm 1** Federated Learning Meta-Algorithm  
**Hyperparameters:** learning rate (\(\eta\)), number of epochs (\(E\)), and batch size (\(B\)).  
```
1:for\(E\) epochs do
2:for\(B\)-sized batch \(b_{k}\) in \(Z_{k}\)do
3:\(w_{k}\gets w_{k}-\eta\frac{1}{B}\sum_{z\in b_{k}}\
abla\ell(w_{k};z)\)
4:endfor
5:endfor
6:return\(w_{k}\)
```  
**Algorithm 2**_FedAvg: ClientUpdate_
# Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility
### Background and Motivation  
The main focus of this study is the increasing importance of traffic density and difficulties in choosing transportation modes in modern cities. Rapidly increasing urbanization and vehicle numbers have seriously increased urban traffic congestion, making it necessary to develop innovative solutions to optimize the transportation infrastructure of cities. Traffic congestion not only increases travel times but also leads to wider economic and environmental problems such as fuel consumption, air pollution, and general environmental impacts. For this reason, choosing transportation modes efficiently in limited areas such as urban areas and university campuses is of great importance both in terms of saving time and using resources efficiently [10; 11; 12].  
In order to make traffic congestion and transportation systems more efficient, optimization methods should be used to select different transportation modes (car, bicycle, motorcycle, walking, etc.). These choices depend on many factors, such as environmental conditions, traffic density, road conditions, and public transportation facilities. In this context, in addition to traditional solution methods, modern artificial intelligence approaches such as reinforcement learning (RL) and deep reinforcement learning (DRL) offer powerful tools to solve such complex problem sets. These methods allow for dynamic solutions to be produced according to instantaneous changes in traffic conditions. Reinforcement learning (RL) is an algorithm that allows agents to learn by interacting with their environment and allows them to determine optimal actions over time. The use of RL algorithms in areas that require decisions based on time and environmental factors, such as traffic management, provides more effective and flexible transportation solutions. In this study, RL algorithms were used to select various transportation modes in scenarios where traffic density is increased on university campuses. Simulation environments such as SUMO (Simulation of Urban MObility) are frequently preferred tools for such traffic simulations, and this study aims to simulate real-world scenarios by providing integration with SUMO.  
Providing a completely randomly distributed traffic environment allows for a comparison with real-world problems. The operation of a completely randomly distributed traffic environment allows for real-time problem matching. In this study, Gaussian and Poisson distributions were used to take into account the different distributions that occur in real-world scenarios. In addition, the effectiveness of these distributions was compared with the created simple model. As a result, this study aims to increase efficiency by using reinforcement learning and simulation techniques to select transportation modes on university campuses. It aims to test the applicability of these methods not only in limited areas such as university campuses but also in wider urban transportation systems.
## 5 System Architecture
### Physical Layer  
The joint waveform consists of a communications component followed by a positioning component. The communications component contains a payload, a pre-amble and mid-amble surrounding the payload, and a post-amble placed at the end of the waveform. This payload contains all of the ToF and time offset estimates, as well as a reserved space for arbitrary communications. The positioning component contains four orthogonal navigation reference sequences. The size and structure of these components are depicted in Figure 5.  
The length of each component and critical waveform parameters are summarized in Table 2. The ambles are used for coarse acquisition and frequency correction, and the post-amble is used to precisely estimate the carrier frequency offset. The amble sequences are drawn from a set of Gold codes and modulated using BPSK modulation. The length of each amble is chosen based on the receiver operating characteristic (ROC) curve and the Cramer-Rao lower bound (CRLB) for frequency offset estimation. The length of the navigation sequences is chosen based on the CRLB for ToA estimation [15; 17]. The communications payload is modulated using BPSK and temporally spread to support the range requirement of 10 km. The positioning sequences are pseudo-random sequences treated for delay-tolerant orthogonality and modulated using MSK. Further details are provided in [83].  
Figure 4: Depiction of the data link layer for two users labeled \(A\) and \(B\). These users alternate transmitting and receiving every 50 ms. To ensure that the appropriate timing information is shared in the next frame, each user must finish executing their processing chain before the next transmission.
# False Positive RFID Detection Using Classification Models
## 6 Conclusions and Future Work  
Detecting and filtering false positives will improve WMS monitoring accuracy for product delivery to supply chain partners. This study used machine learning to predict false positives based on tag RSS. Different tag movement paths and speeds were considered, and statistical features were extracted from tag RSSs. Outlier detection methods were implemented to filter outliers from the collected dataset, and machine learning models were applied to detect static tags and movement close to the gate as false positives. Experimental results showed that integrating outlier detection with machine learning improved classification accuracy. The most accurate classification (97.5%) was achieved by combining LOF or IQR with RF, and was significantly superior to the other combinations and models considered.  
We demonstrated the proposed classification model integrated into a prototype real-time monitoring system, comprising a client program to receive tag information from the reader and a REST API to store the information in the cloud. The client program also filtered false positives to ensure only correct product details (i.e., true positive tag readings), and were forwarded to the REST API and stored in the database. Thus, managers could monitor products delivered to supply chain partners in real-time using the prototype web-based monitoring system.  
Future study should consider different parameters as addition to RSSI, such as phase and doppler, as well as utilizing multiple readers or antennas. The datasets that better represent complex real situations, such as by considering different tag orientations and tag movement directions, need to be gathered in the near future. Furthermore, extending the comparison with other classification models, as well as applying machine learning models to identify other problems that occur during RFID readings, such as false negative readings or miss-reads, could be presented in the near future.  
Conceptualization, J.R.; data curation, B.Y.; formal analysis, G.A.; funding acquisition, J.R.; methodology, G.A.; project administration, J.R.; resources, B.Y.; software, G.A. and M.S.; supervision, J.R.; validation, B.Y.; visualization, M.S.; writing--original draft, G.A.; writing--review and editing, G.A. and M.S.  
This study was supported by the R&D Convergence Center Support Program of the Ministry of Food, Agriculture, Forestry, and Fisheries, Republic of Korea (710013-03).  
The authors declare no conflicts of interest.  
Figure 8: Prototype of real-time monitoring system: (**a**) System architecture, (**b**) client program to filter false positives, and (**c**) web dashboard presents the delivered products.