esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed SH-WPBS
### Wind Power Generation System (WPGS)  
The WPGS control system consists of a wind turbine, a PMSG, and a boost converter. The power output from the wind system is given by Equation (2)  
\[P_{w}=12\rho Av^{3}C_{p}(\lambda,\theta) \tag{2}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Component** & **Rating** \\ \hline Source & \(V_{s}\): 415 V; \(f\): 50 Hz \\  & \(R_{s}\): 0.10 \(\Omega_{\text{J}}\): \(L_{s}\): 0.150 mH \\ Shunt compensator & \(R_{s\text{h}}\): 0.0010 \(\Omega_{\text{J}}\): \(L_{s\text{h}}\): 2.150 mH; \(C_{s\text{h}}\): 1.0 uf \\  & hysteresis band: 0.010 A \\ DC-Link & \(C_{dc}\): 9400.00 uf; \(V_{dc}\) Voltage: 700.0 V \\ \hline \end{tabular}
\end{table}
Table 1: SHAPF specifications and loads.  
\begin{table}
\begin{tabular}{c c} \hline
**Equipment** & **Parameter** & **Rating** \\ \hline Rated-capacity & 400 Ah \\ Maximum-capacity & 500 Ah \\ Nominal-voltage & 750 V \\ Maximum-voltage & 756 V \\ Nominal turbine mechanical & 3 MW \\ power & 11 m/s \\ Pitch angle controller integral & 5 \\ gain & 25 \\ Pitch angle controller & 45 deg. \\ Maximum rate of change of & 25 deg./s \\ pitch angle & 25 deg./s \\ \hline \end{tabular}
\end{table}
Table 2: WPGS and BSS specifications.  
Figure 2: WPGs and BSS connected to DC-Link.  
where  
\(\rho\) is the density of air in kg/m\({}^{3}\)  
\(A\) is the area under swept by the rotor turbine blades in m\({}^{2}\)  
\(v\) is the velocity of the wind in m/s  
\(C_{p}\) is the coefficient of power, function of (TSR, \(\lambda\)) and pitch-angle (\(\theta\)).  
The reference current \(i^{ref}_{dc}\) is approximated by reducing the of DC-Link voltage error \(V_{dc,err}\) using a PI controller. In mathematical terms, the approximation can be explained as given in Equations (3) and (4). The wind error current reference \(i_{W,err}\)\({}^{*}\) is obtained by PI of wind error current \(i_{W,err}\). Where \(i_{W,err}\) is the difference between the reference, the DC-Link current \(i^{ref}_{dc}\) and wind current \(i^{ref}_{W}\) obtained from LPF is given in Equations (5)-(7).  
\[V_{dc,err}(t)=V^{ref}_{dc}-V_{dc} \tag{3}\]  
\[i^{ref}_{dc}=K_{p,1}~{}V_{dc,err}(t)+K_{i,1}\int\limits_{0}^{t}V_{dc,err}(t)~{}dt \tag{4}\]  
\[i_{W,err}(t)=i^{ref}_{dc}-i^{ref}_{W} \tag{5}\]  
\[i_{W,err}\)\({}^{*}=K_{p,2}~{}i_{W,err}(t)+K_{i,2}\int\limits_{0}^{t}i_{W,err}(t)~{}dt \tag{6}\]  
where  
\[i^{ref}_{W}=(\frac{1}{1+T.S})*i_{W} \tag{7}\]  
\(K_{p1}=1.15\), \(K_{i1}=0.2\), \(K_{p2}=1.477\) and \(K_{i2}=3.077\) are chosen heuristically.
## 2 Datasets, Feature Extraction, and Classification Methods
### Classification Methods  
Two main blocks of classification strategies were considered to be applied on the datasets, summarized as the use of: (a) a Support Vector Machine and a Random Forest classifier, being these two classifiers those that were used in [18] (presented in Section 2.3.1); (b) a Stacked Generalization approach (a particular type of ensemble machine learning algorithm) (Section 2.3.2), where we try to take advantage of the diversity that different classification methods may give, in order to help improve classification accuracy, when using them in a coordinated/simultaneous way.
# IoT Botnet Attack Detection Model Based on DBO-Catboost
## 2 Related Works
The experiments demonstrated that the proposed ensemble-learning-based online detection method for zombie networks could effectively detect zombie network traffic at multiple stages, with a malicious traffic detection rate of up to 96.4%. [PERSON] et al. [26] utilized IoT zombie network traffic to design an experimental test platform for generating real-time datasets. Extensive comparative studies were conducted on the proposed dataset and existing datasets using popular machine learning techniques. [PERSON] et al. [27] evaluated common machine learning algorithms based on performance metrics, and the results showed that tree-based algorithms achieved the highest accuracy in Botnet attack detection on the same sensor devices. [PERSON] et al. [28] introduced zombie network detection architecture based on artificial neural networks, which could better differentiate DGA domains and correctly handle more domains. [PERSON] et al. [29] enhanced the LGBM model by utilizing a Bayesian hyperparameter optimization algorithm and employed it for zombie network detection. Compared to existing models, this model outperformed existing methods in terms of accuracy, false alarm rate, and recall rate. [PERSON] et al. [30] proposed a graph neural-network-based model for detecting IoT zombie networks, which was more easily applicable to unseen data during training compared to other methods. Currently, many detection models are based on intelligent optimization algorithms. Table 1 summarizes the related research on intelligent optimization algorithms in IoT botnet detection models. In conclusion, the academic community has made significant progress in research on detecting IoT zombie network attacks. However, due to the complex and ever-changing nature of the IoT environment, as well as the rapid evolution of zombie networks, there is a need for further improvement and refinement in the accuracy of detection models. Additionally, most studies have only conducted experiments on a single dataset, which fails to demonstrate the generalizability of the proposed methods. This work aims to optimize the process of model hyperparameter selection using a novel swarm intelligence optimization algorithm. It is the first time that the DBO algorithm has been combined with the Catboost model and applied in the field of IoT botnet detection, resulting in the achievement of optimal classification results.
## 4 Next Forwarding Node Selection in UWSNs
### Void-Aware
The advantages of AMCTD are as follows: it employs two different weight functions. The first is based on depth and residual energy in the initialization phase, whereas the second is based on depth information to handle the communication void. Moreover, the employment of a multi-sink structure has a direct impact on the overall performance. However, the main weaknesses of AMCTD can be highlighted as follows: first, courier nodes are expensive, which leads to an increase in the network cost; second, courier movement to handle the communication void problem is not efficient and reduces the overall routing performance; third, the use of a depth threshold reduces the performance when selecting the next forwarding nodes with different density and shortest path selection. Last, it did not employ a link quality metric in performing the weight calculation, which has a direct impact on the reliability of the network. Another pressure beacon based routing protocol, named an Inherently Void Avoidance Routing (IVAR) Protocol for UWSNs, has been proposed to handle the communication void problem [11]. This protocol does not require full location information as it uses local depth information and number of hops to the sink. It can inherently avoid the void nodes without using recovery mode. IVAR is composed of two phases, namely route discovery, and data forwarding. In the first phase, sink nodes broadcast a beacon message to identify the hop count for each node. Then each node exchanges depth information with its neighbors. In the second phase, once source nodes have a data packet to send, they embed their ID, depth, packet sequence number, and hop count with the packet and broadcast it to their one-hop neighbors. The receiver node discards this packet if its hop count is higher than the hop count embedded in the data packets. Otherwise, it calculates the fitness value based on depth, transmission range, and hop count using the following equation:  
\[\alpha=\frac{D_{s}-D_{r}}{R}\;,\;H_{s}>H_{r}\;,\;\alpha\in[-1,\;1] \tag{5}\]  
where \(D_{s}\) and \(D_{r}\) indicate the depth of sender and receiver, respectively. Also, \(R\) is the transmission range for each node. \(H_{s}\)_and_\(H_{r}\) indicate the hop count number for the sender and receiver, respectively. Then, the receiver node calculates the forwarding time based on the fitness value. The main advantage of IVAR is that it can handle the communication void without using void recovery mode. However, the main weaknesses of IVAR can be highlighted as follows. First, in the enhanced beacon part, the process of assigning a hop count should be updated within a short space of time due to the dynamic topology of the UWSNs, which increases the energy consumption and network overhead. Second, IVAR does not utilize residual energy and link quality in calculating the fitness value, which leads to an increase in energy consumption and a further reduction in the packet delivery ratio.
# Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data
## 2 Theory and Prior Studies
### Speed Limit Estimation Method
#### 2.2.2 Optimal Speed Limits  
This method was proposed in 1962 by [PERSON] to scientifically limit the vehicle travel speed so that it will be optimal from the social perspective. The speed limit that minimizes socio-economic costs is determined by considering the environmental cost, accident cost, travel time, fuel consumption, etc., so that the travel speed chosen will be optimal in terms of social costs and benefits.  
[PERSON] et al. [11] reported that the optimal speed limit for expressways in Iran from the social perspective was 10 km/h lower than the optimal speed limit from the driver perspective and suggested the following relationship:  
Optimal speed limit \(\leq\) maximum speed limit \(\leq\) design speed.  
Figure 2: Urban speed limits by country/area.
# Conceptual and Technical Design Aspects of Accelerators for External Injection in LWFA
## 2 Requirements for the Externally-Injected-Electron Bunch
### Summary of e-Bunch Requirements to address LWFA Challenges  
To summarize, we have introduced and motivated the following requirements for externally-injected electron bunches:  
* **Bunch duration** shorter than a few fs for reducing the impact of the curvature of the accelerating field on beam energy spread;
* **Bunch arrival time jitter** significantly smaller than the field wavelength (e.g., smaller than 10 fs for 1 ps field period) to have an acceptable shot to shot energy variation;
* Maximum **bunch charge** and its **stability** limited by the maximum tolerable beam loading effect and by the requirements above;
* **Beam energy** chosen to synchronize the beam with the field at the injection point (soft requirement), e.g., 100 MeV for few 10\({}^{16}\) cm\({}^{-3}\) electron plasma density. Working at fully relativistic beam energies helps also to reduce the space charge effect, thus allowing shorter bunch lengths for a fixed bunch charge;
* **Beam transverse size** of the \(\simeq\) um level to achieve proper transverse beam matching;
* **Beam emittance** as small as possible to reduce minimum achievable energy spread [30];
* **Pointing stability** needs to be limited to a few um because of the strong focusing of the fields inside the plasma bubble.
# Quality of Experience in 6G Networks: Outlook and Challenges
## 2 Quality of Experience in the Cellular Evolution
### The Fifth Generation--Generation Software
Meanwhile, especially with the postponed timeline for Releases 17 and 18, certain aspects of 5G and its evolution continue to be under development. Efforts on QoE in 5G networks continue to emerge and focus on extending the work on standardizing QoE mapping to network performance indicators. This standardization, however, can be problematic due to two reasons: legacy and applicability. The legacy aspect consists of transitioning a QoS point-of-view in network management to a QoE one. The QoS view limited the basis for network management to performance criteria (e.g., flow or packet priority, delay, jitter, bandwidth, etc.). This view materialized in the DiffServ/IntServ evolution and other instances, e.g., IEEE 802.11e. However, these performance aspects were optimized in isolation from the eventual user perception of the service delivery. This isolation became further detrimental as multimedia began to dominate Internet access. Expanding the basis of management to include a QoE view was therefore necessary. This expansion has long been made, but substantial handling of QoE like that of QoS parameters remains. The applicability aspect of QoE can be seen here. For example, the work presented in [40] investigates the use of drones in cellular networks, with the aim of \"enhancing QoE\". The work, however, does focus on enhancing video streaming performance. This objective is a fair QoS objective on its own, just not one that necessarily and sufficiently leads to enhancing QoE. The mixed QoS/QoE view persists in other areas, e.g., associating higher bps with increased QoE, hence automatically expecting better QoE as cellular networks evolve.
## 3 Results
### Extreme Learning Machine Model results  
#### 3.3.1 ELM Established by Temperature and Humidity Variables  
The ELM model is built with the full set of temperature and humidity variables as the independent variables. The determined optimal number of neurons for the model stands at 24, and the model results are shown in Figure 6.  
The R\({}^{2}\) of the model's training set is 0.925, with an RMSE of 0.704%, while the R\({}^{2}\) of the validation set is 0.924, with an RMSE of 0.817%, slightly higher than that of the training  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variable Names**} & \multicolumn{2}{c}{**Unnormalized**} & \multirow{2}{*}{**Standard Error**} & \multicolumn{2}{c}{**Standardization**} & \multirow{2}{*}{**VIF**} & \multirow{2}{*}{**Significance**} \\  & **Coefficient** & & & & & \\ \hline Constant & 22.952 & 0.718 & - & - & \textless{}0.001 \\ Upper outlet temperature & 0.143 & 0.069 & 0.592 & 8.800 & 0.041 \\ Lower outlet temperature & 0.114 & 0.010 & 0.740 & 2.060 & \textless{}0.001 \\ Drying section B temperature & \(-\)0.348 & 0.053 & \(-\)1.636 & 11.861 & \textless{}0.001 \\ Drying section C temperature & \(-\)0.183 & 0.089 & \(-\)0.751 & 7.270 & \textless{}0.001 \\ Lower outlet humidity & 0.075 & 0.029 & 0.090 & 1.630 & \textless{}0.001 \\ Humidity in drying section A & 0.012 & 0.026 & 0.056 & 3.160 & 0.742 \\ Humidity in drying section B & \(-\)0.057 & 0.017 & \(-\)0.535 & 3.740 & \textless{}0.001 \\ Humidity in drying section D & 0.010 & 0.025 & 0.085 & 4.342 & 0.701 \\ \hline \hline \end{tabular}
\end{table}
Table 6: UVE-MLR model coefficient table.  
Figure 6: Prediction model of moisture content of corn outlet based on ELM.  
set. The relatively high R\({}^{2}\) of the model's training set indicates that the model can fit the training data well and has strong prediction ability, while the prediction error is small; the close R\({}^{2}\) of the training set and the validation set indicates that the model is not overfitted. Compared with the Multiple Linear Regression model, the ELM model has more fitting advantages, and the prediction error is significantly reduced.
## 5 Conclusions  
Using a unique model generation technique, this paper offers numerical simulations of uniaxial compression experiments for SRMs with various high-volume fractions, having varying BMI strengths, BMI thicknesses, and rock block shapes. The above discussions lead to some conclusions as follows:  
(1) Due to the convenience of the model generated in this paper, this paper takes the weakest zones (BMI) into account in the modeling and is able to set the thickness of the BMI, which are ignored by many numerical simulation studies. The variation in the UCS for SRMs with a high VBP and BMI is investigated in this paper. The BMI, the weakest region in the SRM, likewise, rises as VBP does, which is represented in the strength characteristics of the SRM as the UCS falls as the VBP rises. Furthermore, when the VBP exceeds 60%, the UCS of the SRM drops even more significantly as block-to-block interlocking contacts are definitely assumed to be almost absent or very few.  
(2) As the weakest zones, the effect of the characteristics of the BMI is also taken into account. The overall UCS of the SRM increases with increasing BMI strength, but, at the same time, its decreasing trend levelled off with increasing volume fraction in each group. Meanwhile, a study of thickness, another characteristic of the BMI, showed that the overall strength of the SRM decreases as the thickness of the BMI increases, but the downward trend also flattens out.  
(3) Considering different rock block shapes, this paper finds that the SRM with polygonal rock blocks have the largest UCS for the same VBP, followed by ellipse, and, finally, round, with not much difference between round and ellipse.  
It is worth noting that this paper takes BMI into account in numerical simulations, which is the weakest zone in SRMs. Furthermore, the effect of the thickness of the BMI on the overall uniaxial compressive strength is investigated. Simultaneously, the method proposed in this paper is promising to be extended to three dimensions.  
Conceptualization, G.Z.; Methodology, Y.D.; Software, G.Z.; Formal analysis, Y.D.; Data curation, Y.D.; Writing--original draft, Y.D.; Writing--review & editing, G.Z.; Visualization, Y.C.; Supervision, Y.C. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the National Natural Science Foundation of China (Grant No. 51709089).  
The authors would like to thank the valuable suggestions from [PERSON] and [PERSON].  
The authors declare no conflict of interest.
## 4 Experiments
### Implementation Specification
#### 4.1.2 Training Details  
Our unsupervised network is trained end-to-end on monocular video streams from KITTI dataset [25] using TensorFlow framework [26] and Adam optimizer with \(\beta\)1 = 0:9 and \(\beta\)2 = 0:999. The KITTI dataset contains sequences captured from a moving vehicle. The initial learning rate is set to be 0.0002 and decrease gradually when there is convergence in training loss. The mini-batch size is 4. During training, we train our model at a reduced resolution of 416 \(\times\) 128 pixels over 400,000 iterations to make the computational burden decreased. Our experiments are performed on single NVIDIA Quadro M5000 GPU. We experimentally find that a weighting combination of \([\lambda_{fb};\lambda_{re};\lambda_{icp};\lambda_{mu};\lambda_{sm}]=[0.3;1;0.5; 0.5;0.8]\) in our final loss function results in a stable training.  
We evaluated our method with different test splits from training for each task on the popular KITTI dataset for the sake of fair comparison. The splits are same as Zhou [15]. Our joint training computes forwardly corresponding to each of these three tasks independently, and backpropagate through combined loss. Data augmentation is implemented as a widely used strategy to improve generalization of neural networks which is crucial to avoid over-fitting.
# Indoor Microclimate Analysis of the San Panfilo Church in Tornimparte, Italy
## 2 Materials and Methods
### Instrumentation  
The intensive measurements of temperature and relative humidity were made using a portable thermo-hygrometer (manufactured by Testo Spa, model 625, Settimo Milanese, Milan, Italy, Figure 3a), whereas the wall temperature was measured with a infrared thermometer (manufactured by Fluke, model 62 Mini, Everett, WA, USA, Figure 3b). The continuous measurements were collected using eleven thermo-hygrometers (manufactured by Onset, model HOBO-UX100-011, Bourne, MA, USA, Figure 3c) working at the frequency of one datum every 10 min and two thermo-hygrometers (manufactured by Testo Spa, models 177-H1 and 175-H2, Settimo Milanese, Milan, Italy, Figure 3d) at the frequency of 1 datum every 60 min. The temperature and relative humidity accuracy, resolution and range of the used sensors are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Instrument** & **Temperature Accuracy (°C)** & **Temperature Resolution (°C)** & **Temperature Range (°C)** & **Relative Humidity Accuracy (\%)** & **Relative Humidity Resolution (\%)** & **Relative Humidity Range (\%)** \\ \hline HOBO-UX100-011 & 0.21 & 0.024 & \(-\)20 to 70 & 2.5 & 0.05 & 1 to 95 \\ \hline Testo 625 & 0.5 & 0.1 & \(-\)10 to 60 & 2.5 & 0.1 & 0 to 100 \\ \hline Fluke 62 Mini & 0.5 & 0.2 & \(-\)30 to 500 & - & - & - \\ \hline Testo 177-H1/175-H2 & 0.5 & 0.1 & \(-\)20 to 70 & 3.0 & 0.1 & 0 to 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Instruments description.  
Figure 3: Instruments: (**a**) Testo 625 thermo-hygrometer, (**b**) Fluke 62 Mini infrared thermometer, (**c**) HOBO-UX100-011 thermo-hygrometer, and (**d**) Testo 175-H2 thermo-hygrometer.
## 2 Results and Discussion
### Binding of Two Novel Motifs and MybSt1 Motif to ANAC074
To study whether ANAC074 can bind to the promoters containing MybSt1, NRS1, or NRS2 element, the truncated promoter of _AT4G17500_ (no NACRS, NRS2 or MybSt1 element was included) containing \"TAGTT\" (NRS1p+), without \"TAGTT\" (NRS1p-) or with \"GGGGG\" (NRS1 ppm: completely mutated NRS1); truncated promoter of _ATIG74930_ (no NACRS, NRS1 or MybSt1 element was included) containing \"GAATC\" (NRS2p+), without \"GAATC\" (NRS2p-) or with \"TCCGA\" (NRS2 pm: completely mutated NRS2); truncated promoter of _AT4G11280_ (no NACRS, NRS1 or NRS2 was included) containing \"GGATA\" (MYBp+), without \"GGATA\" (MYBp-) or with \"TTCCG\" (MYBpm: completely mutated MybSt1), were respectively inserted into pHIS2 and hybridized with ANAC074 using Y1H assay. The results showed that ANAC074 binds to the truncated promoters with NRS1, NRS2, or MybSt1, but failed to bind with the truncated promoters without or with completely mutated NRS1, NRS2, or MybSt1 (Figure 3B). Coexpression of effector and reporter vectors in tobacco also confirmed that GUS activity was detected when coexpression of _ANAC074_ and _GUS_ gene driven by the truncated promoters with NRS1, NRS2, or MybSt1. However, GUS activity was not detected when coexpression of _ANAC074_ and _GUS_ gene was driven by the truncated promoters without or with completely mutated NRS1, NRS2, or MybSt1 (Figure 3C,D). Taken together, these results suggested that the promoters with NRS1, NRS2, and MybSt1 motifs were recognized by ANAC074. In the previous study, we found that _AT4G17500, AT1G74930_, and _AT4G11280_ were target genes regulated by ANAC069 under salt stress condition using Affymetrix Arabidopsis Gene Chips (GSE82274). In this study, ANAC074 binds to the NRS1, NRS2, or MybSt1 motifs in the promoters of _AT4G17500, AT1G74930_, and _AT4G11280_. ANAC069 and ANAC074 share down-stream target genes, indicating that there are similar regulatory mechanisms between ANAC069 and ANAC074 and that their transactivation functions are somewhat redundant. The function of ANAC069 in salt and osmotic stress has been confirmed [22], and whether ANAC074 also plays a role in abiotic stress requires further studies.