esa-sceva/satcom-chunk-collection huggingface dataset:
# Vehicle Delay Model Applied to Dynamic and Static Traffic Impact Analysis of Large Parking Lots
## 2 Establishment of Vehicle Delay Model
### Establishment of Delay Model  
To better study the relationship between dynamic and static traffic at the entrance and exit of parking lots, this paper made some assumptions and tests on the delay model to reduce the influence of accidental factors.  
(1) Vehicles can pass smoothly after passing through the downstream intersection without queuing. Therefore, it will not affect the operation of road vehicles in this section.  
(2) The entry lane of vehicles at the exit of the parking lot is the first lane closest to the entrance and exit, and there will be no driving behaviors of crossing the traffic flow.  
(3) When the vehicles arriving at the parking lot queue at the entrance gate, the queuing vehicles will not spread to the road and cause delay to the road. At the same time, the vehicles can find the berth smoothly after passing the gate, and there will be no queue in the parking lot.  
(4) There will be no pedestrians passing through the road near the entrance and exit of the parking lot, no parking behaviors of vehicles loading and unloading passengers, and no arrival and parking behavior of buses. It is to highlight the interference of vehicles at the entrance and exit of the parking lot to road vehicles.  
(5) The entrance and exit gates of the parking lot are single-channel services.  
The dynamic and static traffic flow of the parking lot is divided into arrival vehicles, departure vehicles, and road vehicles according to the vehicle driving path. Based on the above assumptions, vehicle delay models are established respectively.
# Wireless-Channel Key Distribution Based on Laser Synchronization
## 2 Principle and Experimental Setup
The drive signal is transmitted over a 2.5 m wireless link and received by antennas \(\mathrm{Rx_{A}}\) and \(\mathrm{Rx_{B}}\). Due to the beamwidth of the transmitter, \(\mathrm{Rx_{A}}\) and \(\mathrm{Rx_{B}}\) are placed approximately 0.5 m apart to ensure that the drive signal is successfully received. For each user, the received RF drive signal is first converted into an optical signal using an electro-optic phase modulator (PM) of a continuous-wave laser (CW) light. The resulting phase-random drive light is then injected into the DFB laser. To adjust the modulation depth, a radio frequency amplifier (AMP) is used. The modulation depth is defined as \(3\sigma/V_{\pi}\), where \(\sigma\) is the standard deviation of the drive signal  
Figure 1: (a) Principle and (b) experimental setup for wireless-channel key distribution. RBG, random bit generator; AWG, arbitrary waveform generator; AMP, radio frequency amplifier; PM, electro-optic phase modulator; CW, continuous-wave laser; VOA, variable optical attenuator; PC, polarization controller; ISO, optical isolator; OC, optical fiber coupler; DFB, distributed-feedback semiconductor laser; PD, photodetector. and \(V_{\pi}\) is the half-wave voltage of the PM. For injection of the drive light, a polarization controller (PC) is used to match injection light polarization to that of the laser, and an optical isolator (ISO) is used to ensure unidirectional injection. A variable optical attenuator (VOA) is used to adjust the injection strength, which is defined as power ratio of DFB free output to drive light. The output of the laser is derived from an optical fiber coupler (OC), detected by a photodetector (PD), and observed using a digital oscilloscope (Keysight Technologies, Santa Rosa, CA, USA, DSAV164A, 16 GHz bandwidth). Although various schemes [20; 26; 30; 31] can accomplish random keying, this does not impact the feasibility of the proposed scheme. Hence, there are no experimental demonstrations of random keying in this work, and our analysis solely focuses on its impact on the key rate.
# The NEWA Ferry Lidar Experiment: Measuring Mesoscale Winds in the Southern Baltic Sea
## 7 Conclusions  
The article presents the Ferry Lidar Experiment conducted within the NEWA project from February to June 2017. It introduces the ship lidar technology, which was applied on a ferry route from Kiel to Klaipeda in the Southern Baltic Sea. The obtained dataset of measurements is described in detail and made comprehensible. It is discussed how the data differ from met mast measurements as typical on-site data used for wind resource assessments in the wind industry. Ship lidar data stand out due to their covering of the detailed wind profile with a good data availability of more than 80% for measurement heights up to 175 m and still a reasonable availability (above 50%) for heights up to 250 m. From this data basis, e.g., low level jet information can be derived.  
Measured wind time series are further compared to output data of an NWP model. The results of this initial comparison demonstrate that mesoscale simulations with their domain sizes in the order of the ship track, i.e., the distance the ship covers in the applied averaging period of 10 min, compare well to the ship lidar measurements.  
To our knowledge, the NEWA Ferry Lidar Experiment provides the first dataset of wind profile measurements using lidar technology covering wind-energy relevant scales along a track of that length and distance (i.e., several hundreds of kilometers).  
[PERSON] prepared the original draft and managed the measurement campaign. [PERSON] developed and performed the data analysis and produced most of the results. [PERSON] provided the simulated data and contributed in numerous discussions. [PERSON] contributed in numerous discussions and supported the interpretation of the results. All authors reviewed and edited the manuscript until it reached the final stage.  
This research: as part of the NEWA project, was funded by the German Federal Ministry for Economic Affairs and Energy (ref. no. 0325832A/B) on the basis of a decision by the German Bundestag with further financial support from NEWA ERA-NET Plus, topic FP7-ENERGY.2013.10.1.2.  
The authors would like to acknowledge the contribution of DFDS, for giving us access to the Victoria Seaways and supporting the measurement campaign. Special thanks go to [PERSON] for his comments to the manuscript and his great efforts in developing and demonstrating the Ship Lidar technology in earlier studies as the ones referred to in the article. The presented simulations were performed at the HPC Cluster EDDY, located at the University of Oldenburg (Germany) and funded by the BMWi (ref. no. 0324005).  
The authors declare no conflict of interest.
# A Novel Global Key-Value Storage System Based on Kinetic Drives +
## 4 Motivation  
Figure 4 shows data storing and retrieval by a user with only a cluster of Kinetic Drives. Based on the discussion above in this paper, we can see that a metadata server is needed for a cluster to find the destination drive.  
Currently, in the Big Data environment, a huge amount of data sources worldwide is distributed in different regions and keeps generating key-value pairs. In order to store data and provide access, a global system with a large pool of Kinetic Drives is needed. Hence, similar to Figure 4, metadata servers are required to manage those Kinetic Drives. Given a _key_, the system should quickly find which Kinetic Drive in which region has stored the key-value pair. Furthermore, if this given _key_ is for a new key-value pair, the location of it to be stored should be decided as well. It is important to design a global KVS system for users in many regions to access data.  
There is a naive approach that there is no mapping table between the _keys_ and IP addresses of drives. In this case, given a _key_, all drives' IP addresses are returned to the user by the metadata servers. However, this naive approach is unrealistic, because the user has to contact all the Kinetic Drives to search their data. This \"exhaustive search\" is not possible, considering the unacceptable cost and inefficiency of the amount of drives. Hence, the system needs an indexing scheme to map _keys_ to IP addresses of drives.  
On the other hand, it is also impractical to build a huge indexing table to map every key-value pair to the IP address of each Kinetic Drive. The _key_ size a Kinetic Drive supports is up to 4 KB. If all the possible key-value pairs were mapped in the indexing table, there would be 2 \({}^{4}\) KB (= 2\({}^{4096}\)\({}^{*}\) 8) records, which is so large that they could not be stored in metadata servers, along with the huge search cost. This \"exhaustive mapping\" should be avoided as well, since the size of the indexing table(s) should be limited. We need to explore the tradeoff between these two extremes \"exhaustive search\" and \"exhaustive mapping\" for the indexing scheme design.  
Practically, some crucial factors should be considered for a large-scale system, such as data backup, recovery, and coordination among metadata servers. For example, it is important to backup data from a drive to others in the case of data corruption and drive failure. Furthermore, with the Big Data scenario, more than one metadata server is needed. The coordination among them is necessary.
## 4 Data-Driven Framework for Building Energy Consumption Prediction
### Evaluation Metrics
For the above-mentioned linear regression, decision tree, and neural network algorithms, the closer the R\({}^{2}\) value is to 1, the better the fitting effect of the model on the building energy consumption data, that is, the higher the proportion of the variance of the energy consumption data that the model can explain. For example, in a neural network model, the R\({}^{2}\) value can help researchers determine whether the network structure is reasonable and whether it has fully learned the internal laws in the energy consumption data. In terms of evaluation strategies, a single indicator often cannot comprehensively evaluate the performance of a building energy consumption prediction model. For example, relying solely on the MAE may ignore the prediction differences of the model in different data distribution regions; while using the MAPE alone, due to its sensitivity to outliers, the overall performance of the model may be misjudged due to individual abnormal energy consumption data. Therefore, it is usually recommended to use a combination of multiple indicators for evaluation. For instance, using both the MAE and the RMSE at the same time, the MAE provides a robust estimate of the error, and the RMSE more intuitively reflects the degree of closeness between the predicted values and the true values, comprehensively evaluating the model performance from different angles. In addition, methods such as cross-validation can also be used. The dataset is divided into multiple subsets, and training and testing are carried out on different subsets to reduce the evaluation bias caused by the division of the dataset so as to more accurately evaluate the generalization ability of the model in building energy consumption prediction.
## 2 Materials and Methods
### Experimental Setup  
The experiments were performed using a regeneratively amplified Ti: sapphire mode-locked Quantronix-Intega-C system (Hamden, CT, USA) delivering 130 fs pulses at a repetition rate of 1 kHz and central wavelength of \(\lambda\) = 800 nm. The laser system possesses the possibility for variation of the repetition rate (from 25 Hz to 1 kHz). The sample was mounted on the XYZ translation stage perpendicularly in respect to laser beam propagation. The experiments were performed in the air. The laser beam was focused by a lens with a focal length of f = 10 cm to a focal spot with a diameter of approximately d = 40 \(\mu\)m. The number of applied laser pulses (N) was controlled by the computer-driven fast mechanical shutter, synchronized by precisely controlling software. The samples were processed by scanning the focused laser beam over the material surface at precisely defined separation intervals, first in the X direction to produce a line pattern and afterward in the Y direction. The translation was achieved between separate sets of fired laser pulses so that the laser-irradiated zone received a defined number of shots. Variation of the applied number of laser pulses (N) from 1 to 100 was performed. The influence  
Figure 1: Experimental setup for femtosecond laser irradiation of \(\beta\)-tricalcium phosphate (\(\beta\)-TCP) discs: (**a**) scheme of the setup for fs-laser microprocessing; (**b**) optical image of irradiation geometry on the pellet surface; (**c**) scanning direction of the laser beam on the surface of the \(\beta\)-TCP disc.  
on morphological change by means of laser energy and overlapping conditions between distinct pulses was studied. The impact of surface roughness and chemistry deviations on treated and untreated zones defined the most appropriate conditions for cellular proliferation. The experimental setup is presented in Figure 1.
## 3 Results  
### Content Characteristics of Heavy Metal in Soil  
Table 3 shows the descriptive statistical data of physical and chemical properties and heavy metal content in the soil around the mining area. The overall soil is acidic, with the pH value ranging from 3.7 to 7.7 and an average value of 5.9. Among them, 86% of the soil is acidic and 34% is strongly acidic. The range of organic matter content is 0.5-10.5 g/kg, with an average of 4.3 g/kg, which indicates that organic matter content in soil is small  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(E_{r}^{i}\) & \(RI\) & **Potential Ecological Risk** \\ \hline \(E_{r}^{i}\leq 40\) & \(RI\leq 150\) & Light ecological hazards \\ \(40<E_{r}^{i}\leq 80\) & \(150<RI\leq 300\) & Moderate ecological hazards \\ \(80<E_{r}^{i}\leq 160\) & \(300<RI\leq 600\) & Relatively strong ecological hazards \\ \(160<E_{r}^{i}\leq 320\) & & Strong ecological hazards \\ \(E_{r}^{i}\sim 320\) & \(RI>600\) & Extremely strong ecological hazards \\ \hline \hline \end{tabular}
\end{table}
Table 2: Corresponding range of ecological risk level in soil.  
and might affect the activity of heavy metals. The content ranges of heavy metals (Cr, Ni, Cu, Pb, Zn, As, and Cd) were 32.7-169.2 mg/kg, 9.1-151.7 mg/kg, 13.4-328.7 mg/kg, 1.8-7104.5 mg/kg, 16.8-20,075 mg/kg, 1.2-4097.9 mg/kg, and 0.2-255.9 mg/kg, respectively. The average content shows a change in the trend of Zn (2422.0 mg/kg) \(>\) Pb (730.0 mg/kg) \(>\) As (307.1 mg/kg) \(>\) Cu (86.5 mg/kg) \(>\) Cr (66.9 mg/kg) \(>\) Ni (42.7 mg/kg) \(>\) Cd (16.4 mg/kg). Except for Cr and Ni, the average contents of the other five elements are higher than background values in Guangxi, indicating that the heavy metal elements in the soil are enriched to different degrees. Among them, Cd and Zn reach 61.4 times and 32.0 times their background values, respectively. Pb reaches 30.4 times the background value, and As and Cu are 5.3 times and 3.1 times higher than their background values, respectively. The results show that the mining and smelling of non-ferrous metals can obviously lead to the accumulation of some heavy metals (e.g., Cd, Zn, Pb) [29; 30]. According to the Soil Environmental Quality Risk Control Standard for Soil Contamination of Agricultural Land (GB15618-2018) [31], Cd, As, Pb, and Zn exceed the risk screening values, and Cd, As, and Pb even exceed the risk intervention values, indicating that Cd, As, Pb, and Zn are obviously enriched and the main pollution elements in the soil, a fact that needs special attention.
# Readiness of Enterprises in Czech Republic to Implement Industry 4.0: Index of Industry 4.0
## 2 Theoretical Background
### M2M Communication  
Digital production includes a wide range of applied sciences. Studies in these fields attract a lot of effort both in academia and in industry, especially in connection with machine connectivity and communication (M2M), vitally important for machine collaboration and process optimization [66]. Computer-to-computer communication brings much greater efficiency and extraordinary security in production units, from factory halls to agriculture. Literally, machine-to-machine is synonymous with technology that communicates without human intervention. M2M communications change some processes by giving more data to the enterprises, and they will require companies to train employees for these purposes. In addition, the integration of M2M elements will require better integration capabilities and the creation of reliable complex networks with a higher level of security [67]. [PERSON] [26]and his team described a farm of \"no farmers\" where cows can be detected by the feeding machines through sensors and M2M communication, and where the digital sensor capsules inside the cow send reports that the cow is fertile. Worldwide, the automotive, energy, transportation, logistics, consumer electronics, and ultimately retail industries are becoming the new view of new M2M applications [66]. M2M communication offers autonomous communication between intelligent encoders and drives and delivers greater value in the transport sector [17]. M2M communication systems implement automated data communication between machine-type communication (MTC) devices, creating a basic communication infrastructure for IoT and 5G networks [68; 69]. M2M communication will be provided both between physical objects and between their cloud-based digital counterparts [70]. Depending on the location of the distant objects relative to the network, cloud computer technology is referred to [71]. In the future, cloud robotics will be used with real-time connectivity. A higher level of M2M communication is related to the Internet of things (IoT), which is a designation for a much more intelligent interconnection of various products, devices, etc. [72]. The key elements are miniature sensors, representing an almost ubiquitous image recognition technology capable of recognizing people, buildings, and other objects [73].
# FedDB: A Federated Learning Approach Using DBSCAN for DDoS Attack Detection
## 1 Introduction
Particularly, when there are significant differences in feature distributions among different clients, the central model might overlook these non-IID features during training, impacting the final model's performance. To address these challenges, personalized federated learning has been proposed as an effective solution. Personalized federated learning aims to adjust models based on the specific data characteristics of different clients, accommodating the unevenness of data across clients. To tackle this issue, we propose a DDoS attack detection model based on personalized federated learning. This model not only plays a crucial role in protecting data privacy but also effectively addresses problems related to low accuracy and poor performance due to data imbalance. We utilize the FedMe framework for model training, combining it with LSTM models. Additionally, this paper employs a federated learning framework with LSTM neural networks for model training, integrating federated learning with DBSCAN clustering and mutual learning techniques. Specifically, DBSCAN clustering groups similar models together, enabling each client to receive models from the same cluster on the server. This approach reduces the risk of models overfitting to noise during mutual learning, especially when significant differences exist between models, which could otherwise lead to notable accuracy variations across clients. Our method focuses on addressing the non-IID data problem by minimizing discrepancies between the personalized model and the exchanged models, thereby enhancing model accuracy. Additionally, we utilize model exchange to achieve greater detection precision. This approach not only enhances model accuracy on clients with imbalanced data distributions but also significantly improves DDoS attack detection performance while maintaining data privacy. Overall, our model demonstrates outstanding performance in addressing DDoS attacks and provides an effective solution for data privacy protection and model performance optimization. Our approach has made significant progress in handling non-IID data challenges and further advances the application of federated learning technologies in real-world scenarios. The contributions of this paper are as follows:  
* We propose a model based on personalized federated learning to address the challenge of DDoS attack detection. This model not only significantly enhances the accuracy of DDoS attack detection but also plays a crucial role in protecting data privacy;
* We combine the FedMe framework with LSTM models and utilize DBSCAN for model clustering and exchange. This approach not only improves model accuracy under data distribution imbalance but also further enhances the effectiveness of DDoS attack detection;
* This study has made significant progress in handling non-IID datasets. By employing personalized federated learning, we are able to adapt to the diversity of data across different clients, thereby improving the overall performance of the model.
## 2 Results
### Multivariate Statistical Analysis for GCG with 5-, 10-, 15-Growth Years
#### 2.4.2 Chemo-Markers Discovery for Distinguishing GCG with 5-, 10-, 15-Growth Years  
To select the ions responsible for discrimination between GCG\({}_{\text{5-years}}\) and GCG\({}_{\text{10-years}}\), GCG\({}_{\text{5-years}}\) and GCG\({}_{\text{15-years}}\), GCG\({}_{\text{10-years}}\) and GCG\({}_{\text{15-years}}\), the above dataset was subjected to OPLS-DA. As shown in Figure 3B-D, OPLS-DA score plots significantly separated each of the two samples into two groups, where the model fit parameters were 0.938, 0.913, and 0.842 for R\({}^{2}\)X(cum), and 0.915, 0.728, and 0.692 for Q2(cum), respectively. These models were subjected to 200 rounds of permutation tests to confirm their high predictability (Figure S1I,K,M). The volatile components intuitively responsible for the differentiation are marked in red in the S-plots (Figure 3H-J). As a result, with VIP \(>\) 1 and \(p\) \(<\) 0.05, 6 ions were determined as the potential chemo-markers for discrimination between GCG\({}_{\text{5-years}}\) and GCG\({}_{\text{10-years}}\), six chemo-markers were determined for the differentiation between GCG\({}_{\text{5-years}}\) and GCG\({}_{\text{15-years}}\), and two chemo-markers were determined for the distinguishing between GCG\({}_{\text{10-years}}\) and GCG\({}_{\text{15-years}}\). Altogether, six potential growth-year-dependent markers, including hexanal, 2-oxo-pentanoic acid, heptanal, benzaldehyde, octanal, and 2-isopropyl-3-methoxypyrazine, were determined, and the information is summarized in Table 4. The contents of all six chemo-markers were the highest in GCG\({}_{\text{5-years}}\) (Figure 4H), which is also consistent with the market assessment that GCG\({}_{\text{5-years}}\) is the main commodity in the market.