esa-sceva/satcom-chunk-collection huggingface dataset:
# Utility Analysis about Log Data Anomaly Detection Based on Federated Learning
## 4 Performance Evaluation
### Environmental Setup and Evaluation
#### 4.2.1 Federated Learning Performance Analysis  
In the case of the proposed federated learning model, to derive the optimal performance and learning time for each model in each local server, the process of deriving the average value after learning 10 iterations according to the FedAVG method was performed for 10, 15, and 20 epochs and weight normalization. The process was conducted at thresholds in the range of 0.2 to 0.5. Values less than 0.2 and 0.6 or more were not normally weighted for the threshold, so the values were aggregated from the 0.2 threshold. The average and standard deviation of the derived values were aggregated to compare the evaluation indicators of each model. For the evaluation indicators, the values were compared by rounding to the fifth decimal place. The change in weighted F1-score mean values for the threshold and epoch of each model by dataset and the comparison results are provided in Figure 6.  
According to these results, most models display a lower learning efficiency after the 0.4 threshold for both datasets. For the HDFS dataset, CNN1D models derive the highest average weighted F1-score of about 0.9830 at a 0.3 threshold and 15 epochs. The LSTM models derive about 0.9865 at a 0.4 threshold and 15 epochs, and the CNN1D-LSTM models derive about 0.9877 at a 0.5 threshold and 15 epochs. For the BGL dataset, CNN1D models derive the highest average weighted F1-score of about 0.9200 at a 0.3 threshold and 20 epochs. The LSTM models derive about 0.9865 at a 0.2 threshold and 15 epochs, and the CNN1D-LSTM models derive about 0.9404 at a 0.2 threshold and 15 epochs. These results confirm the possibility of log anomaly detection with federated learning through performance comparisons with the existing centralized learning methods.  
Figure 6: Weighted F1-Score comparison results by models.  
Although federated learning does not perform as well as general artificial intelligence learning because it aggregates the weights of local models trained separately from the distributed data, the results indicate a slight difference regarding all models used in the experiment when comparing performance with conventional learning methods. The corresponding results demonstrate that the model with federated learning is sufficiently competitive, considering the prevention of leakage of sensitive information during deep learning using log data. In particular, CNN1D-LSTM models derive the highest performance in both federated and centralized learning methods, deriving higher detection performance by extracting features more comprehensively than the other two models through advantages in local feature extraction and sequence data classification through LSTM. Furthermore, as found in the previous study [19], the application of the hybrid model rather than a single model performs better even for log data, which is similar to text data.
## 1 Introduction
In particular, this paper aims to evaluate GPPs by considering: (1) widely used and recent GPPs with demonstrated satisfactory accuracies (i.e., based on the literature) in a consistent framework, (2) a large number of synoptic stations, as the source of in situ observations, spreading throughout Iran, and (3) a long-term period (over two decades) for comparison, which were not considered in the previous studies simultaneously. The evaluations were performed using daily observations on daily, monthly (i.e., daily observations in each month between 2000 and 2020), and annual (i.e., daily observations in each year between 2000 and 2020) time scales based on both continuous and categorical metrics. Furthermore, daily data were aggregated to compute the accumulated and average monthly and annual precipitation data, enabling the examinations of GPPs in capturing the monthly and annual precipitation values. It is believed that these validations can elucidate the path for many other researchers in different disciplines in Iran to have an applied overview of the capability of the most recently used GPPs. Furthermore, these findings could also provide practical implications for other researchers incorporating GPPs into their studies in other parts of the world.
## 3 Materials and Methods
### Simulation Model Including Real-Time Adjustment  
For the analysis of logistics processes with the help of simulation models in tunnel construction, it is of great importance to display the interactions of logistics and production processes with the simulation model. Most simulation approaches in tunneling consider production and logistics processes separately [22; 47]. A specific analysis of the interaction of logistics processes with production processes rarely takes place. In the frame of the research project SFB 837, an approach for the flexible implementation of simulation models considering these interactions was developed for the planning phase by [PERSON] et al. [48], [PERSON] et al. [49], and [PERSON] et al. [50]. This approach is the basis for the implementation of an update with real-time data, as presented in this paper. The simulation approach is implemented in the simulation software AnyLogic (version 8.7.8) using a multi-method approach combining agent-based modeling and discrete event simulation.  
The simulation model displays the main elements of a mechanized tunneling project and consists of 22 agents. Figure 8 shows the structure of the model within a block definition diagram. The agents on the first level represent the three main areas of the project: the construction site above ground, the shaft, and the subsurface logistics. These can be dividedinto further elements. For instance, the agent subsurface, for example, contains the agent TBM, which consists of a cutting wheel, an erector for the ringbuild, and a grouting system.  
The process flows and the different states of each agent can be modeled using state charts. Agents can therefore operate in a discrete manner, but they interact with each other through signals and messages to communicate and mimic the interaction of the real system.  
All parameters are linked to a database, which continuously updates the simulation model with actual data (see Section 3.1.1). The results of the simulation study are generated by Monte Carlo simulations (MCSs) to account for the effects of uncertainties in the input data.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Ocean Chlorophyll (OCH)
#### 2.6.2 Summary and Future Direction  
The coastal ocean accounts for almost one-third of all marine biological productivity in the world. Most studies have shown that retrieval of Chl-a concentrations in complex coastal waters is more challenging than in open ocean waters, especially when band-ratio algorithms use blue and green bands. This is because the impact of CDOM and TSM at those wavelengths adversely affects Chl-a retrievals [191]. Since the spectral range of the red and NIR bands is less affected by these two optically active constituents, the great potential of band ratios using the red and NIR bands for Chl-a detection in coastal waters deserves much more attention in future studies.  
Over recent years, significant advances have been made in the development of biological models in coastal waters focusing on the use of specific IOPs for detecting phytoplankton blooms instead of the historically used Chl-a proxy. Evaluating the accuracy of bio-optical algorithms for detecting phytoplankton blooms in coastal waters is required in the future. While there are currently few optical and biogeochemical in situ datasets which characterize pre- and post-blooms conditions, automated sensors, such as autonomous underwater vehicles equipped with bio-optical sensors, can be helpful in the future.  
It is also expected that hyperspectral sensors with higher spectral and temporal resolutions could improve the development of efficient OC algorithms and, thus the algorithms of Chl-a retrievals.
# Analysis of Biodiesel Influence on Unregulated Gaseous Emissions of Diesel Motor Vehicles
## 3 Results and Discussion  
### Parameters of the Experimental Engine  
Table 4 presents the fuel consumption (FC), given in kg/h, for the individual fuel mixtures. The values of the brake thermal efficiency (BTE) were calculated using data of the engine speed, torque and fuel consumption. It is evident that the combustion of biodiesel increases the fuel consumption, because the biodiesel heat value is lower compared to the standard diesel fuel. At the same time, the combustion of biodiesel increases the BTE thanks to a higher amount of oxygen in this fuel, which has a positive effect on the speed and quality of the combustion process. It is also possible to say that a higher level of the engine speed also increases the fuel consumption and reduces the BTE values. On the other hand, the BTE values are higher if the engine loading is rising. The above-mentioned facts are valid for both kinds of the fuels, i.e., for diesel fuel and for biodiesel, as well.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Mode** & \multicolumn{2}{c}{**ULSD-F**} & \multicolumn{2}{c}{**B-U (25:75)**} & \multicolumn{2}{c}{**B-U (50:50)**} & \multicolumn{2}{c}{**B-U (75:25)**} & \multicolumn{2}{c}{**Biodiesel**} \\ \hline  & **FC** & **BTE** & **FC** & **BTE** & **FC** & **BTE** & **FC** & **BTE** & **FC** & **BTE** \\  & **(kg/h)**
# Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids
## 4 Q-RPL: Q-Learning-Based Routing Protocol Design
### Reward and Policy Design
Furthermore, in designing the reward system, we have considered the compatibility of our reward design with the wireless communication standard in the physical and MAC layers. The target of three re-transmissions aligns with the default number of re-transmissions set by the IEEE 802.15.4 standard, as detailed in [45]. In summary, if we denote the number of re-transmission attempts to successfully transmit a packet by \(m\), the reward function can be expressed as Equation (2):  
\[r= \begin{cases}2-m,&\text{for }0\leq m\leq 3\\ -2,&\text{if the packet fails after all attempts.}\end{cases}  \tag{2}\]  
With the reward structure defined, the next critical element in our Q-learning-based routing protocol is the policy, which dictates how decisions are made based on the learned Q-values. The policy, that we define as \(\pi(s)\), is a strategy that each node employs to decide which action to take in a given state. It plays an important role in balancing the exploration of new routing paths against the exploitation of the best-known action. A balance that is crucial for the adaptability and effectiveness of any routing protocol. We adopt the \(\epsilon\)-greedy policy, a popular choice in RL tasks for its simplicity and effectiveness. With probability \(\epsilon\), the policy allows the routing node to choose randomly a next-hop candidate in the Q-table, encouraging the discovery of a potentially better candidate. Conversely, with a probability of \((1-\epsilon)\), the node exploits its accumulated knowledge by choosing the candidate with the highest Q-value for a given state \(s\). The \(\epsilon\)-greedy policy for a node \(i\) at state \(s_{1}^{i}\) can be represented as follows:  
\[\pi(s_{1}^{i})= \begin{cases}a^{i}\sim\text{Uniform}(\mathcal{A}^{i}(s_{1}^{i} ))&\text{with probability }\epsilon,\\ \underset{a^{i}\in\mathcal{A}^{i}}{\arg\max}Q(s_{1}^{i},a^{i})&\text{with probability }1- \epsilon.\end{cases}  \tag{3}\]  
where the term \(a^{i}\sim\text{Uniform}(\mathcal{A}^{i}(s_{1}^{i}))\) indicates that the action \(a^{i}\) is chosen randomly following a uniform distribution over the set of all possible actions available at the current state. A common practice in RL that we follow in our design is to dynamically adjust \(\epsilon\) over time. Starting with a higher \(\epsilon\) and gradually decreasing it, allows for more exploration early in training and more exploitation later on. Furthermore, to prevent the model from becoming too exploitative, we establish a lower bound for \(\epsilon\), ensuring that the nodes will always maintain some degree of exploration. This dynamic adjustment of \(\epsilon\) is essential for adapting to the changing network conditions and continuously refining routing decisions as the learning algorithm accumulates more knowledge and experience. Algorithm 1 summarizes the operation of the \(\epsilon\)-greedy policy.
## 4 Results and Discussion
### Hydrogeological Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Control Equation (Math.)** & **Porosity** & **Hydraulic Conductivity/m\(\cdot\)s\({}^{-1}\)** \\ \hline
1 & Richards equation & 0.55 & \(1.2\times 10^{-9}\) \\
2 & Richards equation & 0.55 & \(2.03\times 10^{-4}\) \\
3 & Darcy’s law & 0.55 & \(5\times 10^{-9}\) \\
4 & Darcy’s law & 0.55 & \(5.25\times 10^{-4}\) \\
5 & Darcy’s law & 0.412 & \(5\times 10^{-10}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information on stratified soils. Figure 2: Stratigraphy and well locations. water driven by differences in pollutant concentrations. Therefore, in this interface, both diffusion and adsorption processes were considered. Some researchers [20] have extensively studied the adsorption behavior of petroleum pollutants in different soils and provided correlation coefficients for the Freundlich isotherm adsorption model. Based on experimental studies and numerical simulations from the literature, the Freundlich constant was taken as 3.63 \(\times\) 10\({}^{-3}\) mol/Kg, and the Freundlich exponent was set to 1.09. To select the diffusion coefficient, D, parametric scans were performed using the concentration test data of pollutants at different time points at the same well location. A series of values for the diffusion coefficient, D, were set for parametric scan calculations. During this step, different pollutants were calculated separately, and based on actual on-site experiments, only extraction well No. 6 and injection well No. 7 were activated, with a simulation time of 55 days. The value of the diffusion coefficient, D, was continuously changed, and the model was calculated, with all model results saved. After the calculations were completed, the concentration of pollutants at the bottom of well No. 6 was calculated using the derived value function. The size of this value for different diffusion coefficients was observed and compared with the measured values. Utilizing the principle of binary search, optimization was performed continuously until the value of the diffusion coefficient, D, matched the measured pollutant concentration. The parameter values optimized based on on-site experimental data are shown in Table 2.
# A Multi-Segment Expanded Anchor for Landslide Emergency Management
## 4 Calculation Method of the Ultimate Bearing Capacity of MSEAs
### Verification  
During field tests, the formed holes of the MSEAs had a diameter of 120 mm; they were expanded to a diameter of 150 mm. Moreover, the bearing blades had a length of 450 mm, a burial depth of 20 m, and an assumed friction coefficient of 0.4. The test results of rock samples showed that the arenites had a density of 2.2 g/cm\({}^{3}\), a compressive strength of 110 MPa, and tensile strength of 2.1 MPa. According to Equations (5) and (6), the frictional drag between the hole wall and the bearing blades was 215 kN, which was close to the measured ultimate load of the No. 2 load-bearing body of each MSEA (only relying on the frictional drag along the hole wall) and indicating relatively safety.  
The contact width between the upper part of the load-bearing body and the lock of the underream was set at 5 mm. According to Equation (7), a single load-bearing body had a bearing capacity of approximately 465 kN, which was close to the measured ultimate load capacity of the No. 1 load-bearing body of each MSEA (combined effects of the frictional drag along the hole wall and the reaction force at the lock). The equations for the ultimate bearing capacity of the MSEAs indicate that the strength of the rock masses is the key factor affecting the bearing capacity. For a given case, the design value of the initial tensile force and the strength of the rock masses can be used to select the hole-forming process and determine the need for underreaming and the number of underreams.
## 3 Materials and Methods
### The Layered Architecture  
As Figure 1 shows, the whole measurements system has been conceived as a layered structure. This approach allows focusing on the specific functionalities of each single layer. The first layer measures the environmental parameters, the second takes the pictures, and the third shows the collected data. Each layer is autonomous from the others and communicates with the others through an intermediate communication layer.  
The first layer is made using electronic control units connected to sensors placed directly in the environment to be monitored. The units are based on ARDUINO technology [27]. The second layer performs the role of a local hub. It collects data from sensor control units and sends them out on demand or at preset times. This is realized by means of a device based on an ARM processor [28; 29; 30], in our case a Raspberry-Pi [31; 32; 33; 34]. To communicate, the first and the second layer use ZigBee transmitters/receivers. The information gathered by the Raspberry-Pi will be then sent via Internet to a remote server easily accessible by researchers. The hub can be connected to the Internet via any communication protocol whose selection is realizable by software: GPRS, ADSL, Wi-Fi, WiMAX or satellite connection. The third layer is realized by the server, which shows the collected data and pictures. It, by means of a mask, can also communicate with the hub, e.g., asking it to execute some activities rather than automatically. The last layer is the supply one, which provides energy to the hub.
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
###### Abstract  
Wireless sensor nodes are traditionally powered by individual batteries, and a significant effort has been devoted to maximizing the lifetime of these devices. However, as the batteries can only store a finite amount of energy, the network is still doomed to die, and changing the batteries is not always possible. A promising solution is to enable each node to harvest energy directly in its environment, using individual energy harvesters. Moreover, novel ultra-low power wake-up receivers, which allow continuous listening of the channel with negligible power consumption, are emerging. These devices enable asynchronous communication, further reducing the power consumption related to communication, which is typically one the most energy-consuming tasks in wireless sensor networks. Energy harvesting and wake-up receivers can be combined to significantly increase the energy efficiency of sensor networks. In this paper, we propose an energy manager for energy harvesting wireless sensor nodes and an asynchronous medium access control protocol, which exploits ultra-low power wake-up receivers. The two components are designed to work together and especially to fit the stringent constraints of wireless sensor nodes. The proposed approach has been implemented on a real hardware platform and tested in the field. Experimental results demonstrate the benefits of the proposed approach in terms of energy efficiency, power consumption and throughput, which can be up to more than two-times higher compared to traditional schemes.  
wireless sensor networks; energy harvesting; wake-up radio; MAC protocols; energy management +
Footnote †: journal: Sensors
# Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks
###### Abstract  
With the advent of the Internet of Underwater Things, smart things are deployed in the ocean space and establish underwater wireless sensor networks for the monitoring of vast and dynamic underwater environments. When events are found to have possibly occurred, accurate event coverage should be detected, and potential event sources should be determined for the enactment of prompt and proper responses. To address this challenge, a technique that detects event coverage and determines event sources is developed in this article. Specifically, the occurrence of possible events corresponds to a set of neighboring sensor nodes whose sensory data may deviate from a normal sensing range in a collective fashion. An appropriate sensor node is selected as the relay node for gathering and routing sensory data to sink node(s). When sensory data are collected at sink node(s), the event coverage is detected and represented as a weighted graph, where the vertices in this graph correspond to sensor nodes and the weight specified upon the edges reflects the extent of sensory data deviating from a normal sensing range. Event sources are determined, which correspond to the barycenters in this graph. The results of the experiments show that our technique is more energy efficient, especially when the network topology is relatively steady.  
event coverage detection; event sources determination; routing tree; weighted graph; underwater wireless sensor networks +
Footnote †: journal: Sensors