esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of the Multi-Facility Location Problem Using Widely Available Office Software
## 2 Materials and Methods  
### Hardware and Software Configurations  
**Hardware configurations.** Calculations were performed on two personal computers with different hardware configurations:  
* PC 1-Processor: AMD A10-9620P RADEON R5, 10 COMPUTE CORES 4C + 6G 2.50 GHz. Installed memory 8.00 GB RAM.
* PC 2-Processor: INTEL CORE i7-7700 3.60 GHz. Installed memory 32.00 GB RAM.  
**Software configuration.** System type 64-bit operating system, Windows 10 Enterprise LSTC, MS Excel 2016, 64 bit, part of the Microsoft Office Suite, with the Solver add-in installed, which performs calculations. In the basic version it allows only one method of calculation using genetic algorithms. The Excel environment is used to enter data, calculate intermediate results, and display the result of the entire process.
## 2 Materials and Methods
### Methodological Procedure
```
Inputs: - features\(\leftarrow\)-Dataframe - scenarios\(\leftarrow\)-Dataframe (dates just for 4 first months of 2023) - labels \(<\)- \"queue_time\" (double) or \"lead_time\" (double) - training_models \(<\)-[\"Linear Regression\", \"Random Forest\", \"Gradient Boosting\", \"Decision Tree\"] Output: - forecasted_data \(<\)- \"queue_time\" (double) or \"lead_time\" (double)
# Scale features  scaler \(<\)- initialize scaler \(\leq\)- scaler(features) scaler(labels)
Split into test and training sets  feature_train, feature_test, label_train, label_test \(<\)- split(features, labels)
Train Models for model in training_models:  train(feature_train,label_train)  test(feature_test_label_test)  new_rmse \(<\)-test_rmse  if new_rmse \(<\) old_rmse  best_model \(<\)- model  old_rmse \(<\)- new_rmse
# Forecast forecasted_data \(<\)- best_model(scenarios) return forecasted_data
```  
**Algorithm 1** Machine learning pseudocode. Following model training and selection, the forecasting model was applied to all days during the first four months of 2023. Subsequently, for queue time, the total waiting time was aggregated, while for lead time, the average was computed for each month. Finally, for both targets, the monthly averages were calculated.
## 3 Methodology  
The deployment phase of the nodes is crucial for their success. During this phase, 250 nodes are randomly deployed. The nodes are divided using three tiers and three nearest-neighbor schemes (NNSs), with three neighbor head nodes per tier and nine NHNs in total. Twelve receivers(sink) are placed on the water surface, and one base station is positioned offshore. All NHNs in the upper layer (layer 3) gather data from the nearest sensor nodes and transmit them directly to the sink. The sink sends the data to the base station. The rest of the procedure follows the same process for data transmission from nodes to the best station. For a better understanding of the proposed NBEER, the research methodology is described in the below steps.  
The proposed work purely focused on neighbor node identification (NNI) and NNSs as shown in Figure 2. The experiment was carried out by placing different random clusters and moving nodes underwater. Each node has the ability to detect, identify, and forward the routing path to the nearest one. This method is carried out by the route discovery and route maintenance. The flooding mechanism takes place here, which uses this idea to discover the nearest node.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Year** & **Utilized Technique** & **Benefits** & **Limitations** \\ \hline \multirow{4}{*}{[22]} & \multirow{4}{*}{2022} & A blend of approaches is employed to select & & \\  & & forwarder nodes for data transmission, & & \\  & & encompassing both single and multipath routing & & \\  & & schemes. Various factors, i.e., minor bit error rate & & \\  & & (BER), minimum distance to the sink node, and & & \\  & & highest residual energy, are considered during the & & \\  & & selection process. & & \\ \hline
[23] & 2021 & Multipath routing techniques are employed for & Improved the PDR & & \\  & & efficient data transfer among the nodes. & (packet delivery ratio) & & \\ \hline \multirow{4}{*}{[24]} & \multirow{4}{*}{2020} & The selection of the best forwarder nodes is based & & \\  & & on the weight function, and the scheme & & \\  & & incorporates the use of the Maximum ratio & & \\  & & combining (MRC) technique for improved & & \\  & & performance. & & \\ \hline
[25] & 2019 & Data transmission utilizes the Manhattan and RSSI & Improved transmission & \\  & & techniques for enhanced efficiency. & & latency and energy \\ \hline \multirow{4}{*}{[26]} & \multirow{4}{*}{2019} & Both the collaborative and independent approaches & & \\  & & employed the positional data of nodes, considering & & \\  & & both proximity and the movement of sinks, to & & \\  & & facilitate the propagation of information. & & \\ \hline
[40] & 2018 & The non-cooperative routing scheme employes a & & Improved network & \\  & & designated area of interest. & & lifetime, reliability, and \\  & & & throughput & \\ \hline
[41; 42; 43] & 2017 & Clustering-based cooperative techniques; nodes & & \\  & & cooperate within clusters to send and receive data. & Consumes less energy & \\  & & & cooperation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compilation of newly released approaches.
# Potential of Homomorphic Encryption for Cloud Computing Use Cases in Manufacturing
## 3 Encryption for Cloud Computing in Manufacturing
### Experimental Comparison of Classic and Homomorphic Encryption
#### 3.3.2 Experimental Results
Therefore, only SWHE and FHE were applicable, again only with a limited number of operations, due to the increasing error of SWHE. Again, the classic encryption required less computation time (factor 13) than SWHE and FHE, which were again similar. As for the other three scenarios, the suitability of the absolute values (2.6 s) must be considered for the use case. Figure 5: Results of experimental comparison. For the comparison of the transferred data amount, the overall data transfer for all three scenarios was analyzed. This shows that the total data amount of homomorphic encryption was only 25% of the classic encryption. This was the consequence of down- and uploading data for each calculation using classic encryption. Again, this up- and download was simulated on the same hardware, which has no effect on the data amount that would be down- and uploaded to the cloud. Therefore, the results are transferable to a real cloud scenario. In summary, the validation results generally confirm the knowledge of the literature presented in Section 3.3.2. Nevertheless, it is important to consider whether the absolute values of PHE, SWHE, and FHE are applicable for the practice use case.
## 2 New Model Biodiesels and Their Fuel Properties  
### New Model Biodiesels  
For transesterification, the interest was focused on ethylene glycol monomethyl ether. Ethylene glycol can be made by methanol, as well as ethylene glycol monomethyl ether. They are cheap, small molecular with high oxygen content, high cetane number. In this case, the produced biodiesel may have a reasonable price to compete with petrodiesel.  
The new model of biodiesel is synthesized just like FAME, ethylene glycol monomethyl ether first reacts with catalyst, see sodium (Na) in Equation (1). Sodium ethylene glycol monomethyl ether is formed, and then it is mixed with vegetable oils like rapeseed oil, palm oil, et al. Transesterification reaction occurs as described in Equation (2) with an optimized reactants fraction. The chemical process completes under moderate condition like 60 \({}^{\circ}\)C, water bath at atmosphere pressure in laboratory. The mixture should be treated by phase separation, hydrochloric acid (HCI) washing, filter, dry, and finally, new model biodiesel is obtained. The procedures can be simply showed in Figure 5.  
\[HOCH_{2}CH_{2}OCH_{3}+Na\to NaOCH_{2}CH_{2}OCH_{3}+H_{2}\uparrow \tag{1}\] \[CH_{2}COOR_{1}\] \[\left|\begin{array}{c}CH_{2}ONa\\ R_{1}COOCH_{2}CH_{2}OCH_{3}\\ CHCOOR_{2}+3 NaOCH_{2}CH_{2}OCH_{3}\to R_{2}COOCH_{2}CH_{2}OCH_{3}+CHONa\\ R_{3}COOCH_{2}CH_{2}OCH_{3}\\ CH_{2}COOR_{1}\end{array}\right| \tag{2}\]  
The chemical structure of the synthesized biodiesel can be identified through Fourier transform infrared (FT-IR) spectrum and proton nuclear magnetic resonance (\({}^{1}\)H NMR) spectrum analyses [33; 34]. Different groups, such as methyl, methylene, carbonyl, C-O-C and C=C, etc., possess distinct vibrational frequency and generate different characteristic infrared absorption spectrum. Furthermore, protons attributed to different group under different chemical environment in the molecule also have quite different characteristic chemical shifts, splitting multiplicity and coupling constants. Based on these obtained characteristic data, the chemical structure of the new model biodiesel can be deduced. In addition, analyses by gel permeation chromatography (GPC) can acquire the data of average molecular weight and molecular weight distribution of the new model biodiesel as well as its originating vegetable oil. The GPC data can be used to make a comparison between the biodiesel and the vegetable oil to further confirm the chemical structure of the new model biodiesel.
## 4 Future Trends  
Regarding the evolution perspectives of the technology that incorporates AI, particularly intelligent systems and devices, for the next 20 years, embedded systems technology will grow, developing devices capable of executing supervised and unsupervised learning algorithms and training complex models.  
TinyML emerges as a new trend that seeks to provide a different ML technique implementation alternative. Over time, TinyML is included in an area called AIoT. The philosophy of AIoT, and specifically of TinyML, is to perform ML tasks using algorithms that run locally within the hardware-constrained embedded systems and then use the result for the direct application or share it in another service with more excellent storage and processing capacity [91].  
The fields in which TinyML techniques are currently implemented are sign language detection, handwriting recognition, medical face mask detection, gesture recognition, speech recognition, and autonomous mini vehicles, robotics, and dynamic control systems, among other applications. For the subsequent years, the application spectrum covered by TinyML will be greater in areas where optimal performance of tasks supported by ML algorithms is required [92]. On the other hand, according to our review, the possible perspective of evolution for robotics and computer vision applications in the next twenty years is focusing on using hardware-constrained embedded systems with 8-bit, 32-bit, and 64-bit architectures that implement TinyML algorithms.  
However, not all development processes in TinyML and dynamic systems control applications using vision systems will be easy. Open problems must be resolved so that the systems achieve expected results much better than those currently available.  
Supporting more complex trained model files in hardware-constrained embedded systems presents challenges. The problem is focused not on increasing the hardware capabilities of the system, specifically RAM, but, rather, on making the compression of the model file efficient through processes that achieve better results by reducing file size without compromising aspects such as model accuracy and application performance. Tools are being developed to compress trained models through code abstraction, such as TinyML applications, obtaining reduced models capable of running on 8-bit and 16-bit architectures.  
Another challenge is oriented to executing tasks in real time, especially in those applications in which object detection is carried out using a vision system. Currently, systems such as the ESP32-S3 and microcontrollers with Cortex-M4 CPU achieve a balance between low cost and acceptable performance, maintaining low energy consumption compared to other systems. However, the latency times of the algorithms generate delays between 50 and 500 milliseconds, which may not be acceptable in critical control and detection tasks.
# Construction of Power Fault Knowledge Graph Based on Deep Learning
## 4 Experiments and Results
### Experiment 2-BBiGRU-GA-CRF
#### 4.2.5 Experimental Results  
The above four models are used for comparative experiments on the data set, and the experimental results are shown in the Figures 12 and 13.  
As can be seen from the experimental results in Figures 12 and 13, compared with the BiLSTM-CRF model and the BiGRU-CRF model, the accuracy, recall, and F-measure of the BBiGRU-GA-CRF model improved by 5.61%, 6.17%, and 5.41%, and 3.18%, 4.28% and 3.45%, respectively. However, the time consumption of each round increased by 161% and 829%, respectively. This aptly demonstrates that adding a BERT layer can fully extract character level features, dynamically adjust the vector representation of characters according to the changes in semantic environment, significantly improve the gene  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Name** & **Meaning** & **Parameter Value** \\ \hline
**batch\_size** & Number of samples per batch & 64 \\
**max\_seq\_len** & Number of words & 64 \\
**lr** & Initial learning rate & 0.001 \\
**Dropout** & Proportion of discarded neurons & 0.5 \\
**BERT-Base** & Number of hidden layers of BERT base & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 6: BBiGRU-GA-CRF Model parameter setting.  
Figure 12: Experimental results of entity extraction model performance comparison.  
model, and significantly enhance the effect of entity recognition; however, it also increases the time overhead. Compared with BERT-BiLSTM-MHA-CRF, the accuracy, recall, and F-measure of this model were improved by 1.98%, 2.13%, and 0.71%, respectively. Moreover, the time cost per round is reduced by 28%. It aptly demonstrates that the introduction of GRU and the Gated Attention Mechanism can capture local features, improve the model recognition effect, and reduce the time cost.  
In order to verify the effects of the addition of a BERT layer and Gated Attention Mechanism on the experimental results, two groups of ablation experiments were set up in this paper. The model with only the BERT layer was compared with other classical models, and the model with only the Gated Attention Layer was compared with other classical models. The experimental results are shown in the Figures 14 and 15.  
Figure 14: Ablation experiment, BERT layer.  
Figure 13: Each round of the entity extraction model takes time to compare the experimental results.  
Different dimension vectors generated by BERT model training also have a certain impact on the experimental results. This experiment used GRU\({}_{\text{dim}}\) values of 100, 200, 300, and 400 for comparison. The comparison results are shown in Figure 16. It can be seen that the F1 value extracted by the entity is the best when the vector dimension is set to 300. When the dimension is low, the vector features are not complete enough, resulting in underfitting. When the dimension is too high, the noise generated in the experiment is captured, resulting in overfitting.
# Simplified Vibration PSD Synthesis Method for MIL-STD-810
## 3 Vibration PSD Synthesis
### Vibration PSD Synthesis Made Simpler
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Events**} & \multicolumn{3}{c}{**Calculations**} \\ \hline
**Event** & **Frequency** & **Time** & **PSD** & **FDS Equation (7)** & **DP Equation (9)** & **GRS Equation (16)** \\ \hline
1 & 300 Hz & 2 h & 5 (m/s\({}^{2}\)) \({}^{2}\)/Hz & \(7.6\times 10^{2}\) & \(2.4\times 10^{5}\) & \(1.8\times 10^{5}\) \\
2 & 300 Hz & 1 h & 10 (m/s\({}^{2}\)) \({}^{2}\)/Hz & \(1.5\times 10^{3}\) & \(9.6\times 10^{5}\) & \(3.6\times 10^{5}\) \\ \hline \multirow{4}{*}{Synthesis} & \multicolumn{3}{c}{SUM (Event 1 + 2)} & \(2.3\times 10^{3}\) & \(12.0\times 10^{5}\) & \(5.4\times 10^{5}\) \\ \cline{2-6}  & \multicolumn{3}{c}{test PSD} & Equation (12) & Equation (11) & Equation (17) \\ \cline{1-1}  & \multicolumn{3}{c}{for test T = 10 min} & 30 (m/s\({}^{2}\)) \({}^{2}\)/Hz & 30 (m/s\({}^{2}\)) \({}^{2}\)/Hz & 30 (m/s\({}^{2}\)) \({}^{2}\)/Hz \\ \hline \hline \end{tabular}  
* b = 4, \(\zeta=0.05\).  
\end{table} 
Table 1: PSD synthesis with FDS, damage potential (DP), and GRS \({}^{1}\). It should be noted that the intermediate calculation values have no physical meanings and that the unit of the test PSD follows the unit of the input PSDs. Equation (15) is the best representation showing these characteristics in synthesis.
## 3 Trading Power Consumption against QoS  
To investigate the effect of sleep modes on the nodes of a network and their consequence on QoS, we use a real testbed in which nodes have the sleep mode capability and more specifically the ACPI S3 suspend-to-RAM state [29], which is a low wake-latency sleep state, where only the RAM remains powered. We measure the power consumption of the network using the Watts up?.Net power meter [30]. The work we present aims to:  
* Understand the drawbacks of introducing sleep modes in the network;* Identify the required operating state of the network based on power consumption savings requirements and QoS constraints;
* Provide the basis for future mathematical models of the behaviour of such a system;
* Facilitate pricing techniques depending on QoS demands and the cost of power needed for these constraints to be guaranteed.
# Impedance-Based Stability Analysis of Grid-Connected Inverters under the Unbalanced Grid Condition
## 3 The Simplified Stability Analysis Method Based on the GNC and Verification  
According to the established impedance models of the inverter under the balanced and unbalanced grid conditions, the grid-connected inverter systems are MIMO high-order systems. Therefore, the GNC needs to be adopted for the stability analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Grid voltage & 380 V & Grid frequency & 50 Hz \\ DC-link voltage & 800 V & Switching frequency & 10 kHz \\ Filter inductance & 4 mH & Filter resistance & 0.05 \(\Omega\) \\ Damping coefficient & 1.414 & Grid inductance & 5 mH \\ Proportional gain of PLL controller & 0.7376 & Integral gain of PLL controller & 84.352 \\ Proportional gain of current controller & 6.47 & Integral gain of current controller & 4194 \\ \(d\)-axis current reference & 21.5 A & \(q\)-axis current reference & 0 A \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters and values of the DSOGI-PLL-based inverter system for frequency sweeping.  
Figure 4: Comparison of theoretical and measured impedance of the DSOGI-PLL-based inverter.
## 3 Security and Privacy Challenges
### AI Role in EC Security
#### 3.3.2 Federated Learning  
Google has proposed a distributed ML scheme called federated learning (FL) which requires a local ML model at each data site. Later, it trained a complex ML model on an aggregating centralized server [164; 165]. FL allows the training of AI models without the need to transmit sensitive data to third-party servers. However, FL networks need a large number of heterogeneous distributed devices, which reduces their communication efficiency. To circumvent the problem of channel efficiency, [PERSON] et al. have proposed a Hierarchical Federated Learning (HFL) framework with an intermediate model aggregator [166]. In a typical distributed learning environment shown in Figure 11b, each participant has access to a local dataset, and the parameter server coordinates the participants. The parameter server in the role of an aggregator has no control over or access to the data stored on  
Figure 11: (**a**) Centralized and (**b**) distributed training approaches.  
participants. The aggregator server selects participants and aggregates the updated model parameters from the intended participants. Secure model transmission to the server is achieved using cryptographic techniques like Secure Multi-Party Computation (SMC), Differential Privacy (DP), Homomorphic Encryption (HE), etc., among multiple clients without revealing any classified data to each other. Hence, FL has reduced the communication overhead due to the processing of data locally and can offer data privacy and security. Integration of blockchain technology with the FL takes data security to the next higher levels [167]. Blockchain prevents security and privacy threats with its decentralization, immutability, consensus, and transparency characteristics [168]. Among the challenges that FL faces, resource constraints stand at the forefront due to limited power computing nodes and slower communication links. Hence, the FL process at the edge node may take a longer time than expected, as well as energy overheads. Each data source frequently communicates with the central server as the FL model needs to be updated repeatedly and continuously, and there is a higher probability that some nodes upload wrong or old model parameters [169]. A lightweight protocol using secret sharing and a weight masks-based framework is proposed in [170] which protects gradients during FL against attacks like replay and gradient leakage attacks without compromising the model's accuracy.
## 5 Evaluation of Coefficient of Restitution (CoR)
### Practical Significance of the Results  
The experimental work conducted in this study provides several practically relevant insights. Beyond the primary objective--validating simulation models for use with the discrete element method (DEM) to predict grain behaviour in pneumatic transport systems and mechanical sorters as support for machine design--the results also enable more direct, application-oriented conclusions. The most evident utility lies in their potential to reduce the risk of mechanical damage to grains during harvesting, transport and processing.  
Based on the relationships observed between grain moisture content, impact velocity and the coefficient of restitution (CoR), we propose the following recommendations:  
* Moisture content: A strong correlation exists between CoR and grain moisture, as illustrated in Figure 14; for the 'Memory' wheat variety, structural damage is markedly lower when moisture remains below 10%. Higher moisture significantly increases plastic deformation and internal damping, thereby lowering the CoR and prolonging contact time during impact.
* Impact velocity: Grain-surface impact velocity is another critical factor, particularly when selecting transport speeds in pneumatic conveying systems such as seed drills. In this study, CoR decreased from 0.6 to 0.4-0.5 as velocity rose from 1.0 to 5.0 m\(\cdot\)s\({}^{-1}\) (Figure 8). High-moisture kernels impacted at higher velocities are therefore especially vulnerable to internal mechanical damage.
* Equipment design: Impact dynamics also inform equipment design. Machine contact surfaces (e.g., metal plates, guides or rotary components) should absorb part of the impact energy or distribute forces evenly. Using slightly elastic materials and non-perpendicular geometries can help to minimise grain damage under operating conditions.
# Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT
## 3 Logistics Warehouse Object Detection
### YOLO Algorithm Network Structure
#### 3.1.5 SPP (Spatial Pyramidal Pooling)  
SPP is known as Spatial Pyramidal Pooling, as shown in Figure 8. Its function is resizing the input feature maps to a fixed size. In CNN, SPP is commonly used to solve problems that include inappropriately sized images.  
SPP obtains features through Maxpool, and then merges the features with concatenation to obtain a fixed size feature. In addition, YOLOv5s adds a CBL module for data sampling enhancement before and after the pooling layer [23].