esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Observation of Various Soliton Molecules in Different Fibre Lasers
### Net Normal Dispersion Regime  
[PERSON] et al. studied an ytterbium-doped fibre laser operating in net normal dispersion regime [20]. The cavity is designed carefully with total cavity GVD of +0.047 ps\({}^{2}\) such that the pulses are always positively chirped when propagating along the whole cavity and are shortest in pulse duration near the beginning of the fibre just after the intra-cavity grating pairs. A self-similar propagation of the pulses inside the cavity can then be guaranteed. By intentionally choosing the orientation of the intra-cavity polarization controllers they were able to observe bound states of two and three parabolic pulses in a self-similar regime, for the first time. Figure 11a,b shows the optical spectrum and corresponding autocorrelation trace of one two-pulse bound state, which confirm generation of parabolic pulses by fitting them with different profiled functions.  
By tailoring dispersion condition of a fibre laser and especially controlling spectral filtering bandwidth, gain-guided solitary pulses can be formed [41; 42]. Different from self-similar evolution which generates linear-chirp parabolic pulses [43], gain-guided fibre lasers are capable of producing pulses with nonlinear frequency chirp. Despite of different pulsing dynamics, bound states of gain-guided solitary pulses have also been observed by [PERSON] et al. [19], featuring periodic modulation of optical spectrum under envelope with steep borders and relatively flat top, as shown in Figure 12a. The ratio between the measured pulse separation and the pulse width is 34.4 (Figure 12b), which goes far beyond the usual direct pulse-pulse interaction between solitons. By conducting numerical simulations, the authors have nicely revealed interesting properties of such bound solitons that both pulse separation and relative phase difference remain fixed along the entire cavity, although strong  
Figure 10: Experimental observation of a stationary soliton pair at \(\sim\)1940 nm: (**a**) optical spectrum; (**b**) optical autocorrelation trace (inset: magnified view of the central peak fitted by \(\mathrm{sech}^{2}\) profile); (**c**) pulse train on oscilloscope; (**d**) radio-frequency (RF) spectrum (inset: RF spectrum over a 1-GHz span). (Reproduced with permission from [23]. © The Optical Society of America (Washington, DC, USA), 2016).  
stretching and compression of one individual soliton take place when it propagates inside the laser. Simulation results indicate that at the position where the pulse is longest, partial overlap of the temporal profile of the two bound solitons is present, enabling a direct strong interaction between two pulses.
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## 5 Downlink Transmission  
### Coherent Downlink Transmission  
Each downlink resource block allocates \(\tau_{c}-\tau_{p}\) time slots for downlink data transmission. In the CF-MIMO system, coherent joint transmission is implemented, where every Access Point (AP) concurrently transmits the same data symbol to all User Equipments (UEs) utilizing Maximum Ratio precoding. The signal transmitted by AP \(l\) at time instant \(n\) is defined as [4]:  
\[x_{l}[n]=\sqrt{\frac{p_{d}}{K}}\sum_{i=1}^{K}\hat{h}_{il}[n]\sqrt{\frac{{{\mu} _{il}}}{q_{i}[n]}}, \tag{98}\]  
where \(q_{i}[n]\sim\mathcal{CN}(0,1)\) represents the symbol transmitted to UE \(i\), which remains consistent across all APs. Here, \(p_{d}\) represents the maximum downlink transmission power for a single AP, and \(0\leq\mu_{il}\leq 1\) values are power control coefficients selected to satisfy the downlink power constraint, ensuring \(\mathbb{E}\big{[}|x_{t}[n]|^{2}\big{]}\leq p_{d}\).  
The received signal at the \(k\)-th UE at time \(n\) is described by Equation (99), where  
\[\text{SINR}_{coh,k}[n]=\frac{\rho_{k}^{2}\Big{|}\sum_{l=1}^{L}\sqrt{\mu_{kl} \text{tr}(Q_{kl})}\Big{|}^{2}}{p_{d}\sum_{i=1}^{L}\sum_{j=1}^{L}\mu_{il}\text{ tr}(Q_{il}R_{kl})+\rho_{k}^{2}p_{d}\sum_{i\in\Pi_{k}\backslash\{k\}}\Big{|}\sum_{l=1} ^{L}\sqrt{\mu_{il}}\text{tr}(\bar{Q}_{kil})\Big{|}^{2}+\sigma_{d}^{2}}. \tag{99}\]  
In this expression, \(\rho_{k}\) denotes the effective SINR for UE \(k\), and \(\sigma_{d}^{2}\) represents the noise variance.
# Application of a Novel High-Order WENO Scheme in LES Simulations
## 1 Introduction
The structure of this paper is outlined as follows: Section 2 constructs the high-order finite-volume WENO-ZQ scheme and integrates it into a three-dimensional LES CFD solver. The computational performance of the WENO-ZQ scheme for viscous flow fields is then systematically evaluated using multiple classical numerical experiments and compared with the classical WENO-JS scheme. Section 3 applies the fifth-order WENO-ZQ scheme to the prediction of flat-plate turbulent boundary layers, supersonic compression corner flows, and typical high-load, low-pressure turbine cascade transition and separation flows with high-fidelity. The advantages of the WENO-ZQ scheme in LES are further validated by comparing the results with those from the open literature. Finally, Section 4 concludes with some final remarks.
## 5 Conclusions  
This paper proposed an HDS adapted for suspension bridge corrosion protection. A numerical model of HDS was first established. Then, the effects of the main operating parameters on HDS were analyzed and the dehumidification performance of HDS and DWDS was further compared to illustrate the superiority of HDS to apply on a suspension bridge. In addition, the air supply parameter was discussed, and a low-energy operation strategy of HDS in summer cases was proposed. Finally, limitations and adaptations of HPDS and DWDS on suspension bridges were discussed. The key findings are listed below. HDS realizes the utilization of waste energy from suspension bridges, enhancing the system's efficiency. Its \(SMER\) reaches 3.16 kg kW\({}^{-1}\) h\({}^{-1}\) in a high-temperature and -humidity environment (35 \({}^{\circ}\)C, 30.82 g kg\({}^{-1}\)) of the suspension bridge. In the same inlet air cases, HDS has greater dehumidification capacity than DWDS, and this advantage is enlarged with the increment of inlet air temperature and moisture content. In addition, HDS can strengthen dehumidification ability by cutting down the evaporation temperature and raising the regeneration temperature to meet the peak moisture load of the suspension bridge. Considering the anti-corrosion effects, energy consumption and drying time, the authors recommend that the moisture content corresponding to the atmospheric temperature and RH of 45% be used for air supply on a suspension bridge. HPDS has poor adaptability to temperatures below 20 \({}^{\circ}\)C, while DWDS has poor adaptability to some high temperatures of 24-40 \({}^{\circ}\)C and high humidity of 19-30 g kg\({}^{-1}\). None of them can meet the air supply requirements of the suspension bridge's main cable alone. Concepts: X.Z. ; writing--original draft: [PERSON] and Y.S. ; writing--review and editing: J.L. All authors have read and agreed to the published version of the manuscript. This research was funded by [National Natural Science Foundation of China] grant number [52142803]. Some or all data, models or code that support the findings of this study are available from the corresponding author upon reasonable request. We appreciate the contributions of [PERSON] and [PERSON] to this study. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Points** & **Temperature/\({}^{\circ}\)C** & **RH/\%** & \
\begin{tabular}{c} **Moisture** \\ **Content/g kg\({}^{-1}\)** \\ \end{tabular} &
\begin{tabular}{c} **Enthalpy/kJ** \\ **kg\({}^{-1}\)** \\ \end{tabular} \\ \hline
1 & 35.0 & 90.0 & 32.7 & 119.2 \\
2 & 88.7 & 2.4 & 10.0 & 119.2 \\
3 & 120.0 & 2.4 & 32.7 & 209.9 \\
4 & 46.5 & 90.0 & 63.0 & 209.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Inlet and outlet status parameters. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**
# Internet of Things (IoT) and the Energy Sector
## 3 Enabling Technologies
### Sensor Devices
In energy consumption, these sensors are used to reduce the energy consumption in buildings. For example, by using PIR sensors, the presence of humans inside spaces can be detected. If there is no movement detected in the space then the light control of the space turns off the light, i.e., smart control of lighting. In this fashion, the electricity consumption of the buildings are reduced [50]. Similarly, this can be applied for air conditioning systems which consume nearly 40% of the energy in buildings [48]. _Proximity sensors_ are utilized to detect the presence of nearby objects without any physical contact [51]. The example application of proximity sensors is in wind energy production. These sensors provide longevity and reliable position sensing performance in wind turbines. In wind turbines, the applications of proximity sensors include blade pitch control, yaw position, rotor, and yaw brake position; brake wear monitoring; and rotor speed monitoring [52].
## 3 Results  
### Spatial Distribution of Cropping Pattern  
In the present study, an algorithm was prepared on Google Earth Engine for the identification of cropping patterns at the spatial resolution of 10 m for the year 2020-2021. From the analysis of satellite images, the calculated area of Kharif, Rabi, and double crops were 877.6 km\({}^{2}\), 3174.2 km\({}^{2}\), and 886.6 km\({}^{2}\), respectively.  
The area calculated accounted for 73.44% and 26.56% of the Kharif and Rabi crops, respectively. Only 7.42% of the total cropped was depicted under the double-cropped area. Geographically, the Kharif crop was most commonly seen in the northeastern, eastern, and entire southern periphery of the study area. On the other hand, the availability of the Kharif crop was relatively lower in the northwestern and southwestern segments (Figure 7). In the case of the Rabi crop, the concentration was significantly lower than it was for the Kharif crop. It was primarily observed in the northwestern and central portions of the region, with a small patch in the northeastern and a few segments in the southwestern part of the region (Figure 8). The double cropping region of the study area was most concentrated in a few pockets in the northeastern segments, a continuous patch in the northeastern portion, and the southwestern periphery of the study area (Figure 9).  
Figure 7: Kharif crop distribution (Datum: WGS84).  
Two different kinds of ground-based references were collected to validate the cropland extent and the cropping intensity. Information concerning the cropping pattern at the village level and the total area planted with crops was gathered from every tehsil land record office in the study region. The area under Kharif, Rabi, and double crop were compared with the computed values and represented in Figure 10.  
Figure 8: Rabi crop distribution (Datum: WGS84).  
Figure 9: Double crop distribution (Datum: WGS84).
## 3 Results and Discussion
### Stage II
#### 3.2.4 Bulk Density  
Higher bulk density is considered as more desirable while managing powders in the industry, as it corresponds to less air filling the empty spaces between particles in the bed. It lowers the risk of oxidation and decreases the costs of packaging and transportation [27]. Regardless of IAH and IAT, the highest level of FR led to the production of powders of the highest bulk density (Figure 5). Moreover, the variant of the lowest IAH at the lowest IAT had the highest both \(d_{L}\) and \(d_{T}\). Bulk density can be related to many different factors, including the presence of air filling the spaces between the particles, the air trapped within the particle, the inter-particle forces, particle size and morphology, and moisture content. Evaporation rate thus plays an essential role in shaping bulk density, as it influences the formation of the shell on the droplet [27,28].  
Figure 5: (**A**) loose bulk density (\(d_{L}\)); (**B**) tapped bulk density (\(d_{T}\)) of MD powders spray dried at different inlet air temperature (80–120 \({}^{\circ}\)C), inlet air humidity (0.1–10 g/m\({}^{3}\)) and feed rate (0.16–0.83 mL/s) (Stage II); blue areas on the figure indicate the experiments, where not-evaporated water was observed inside the drying chamber. The gradient of blue, orange and red color indicate the increasing inlet air temperature, while blue color demonstrates the lowest level, orange the middle level and red the highest level of inlet air humidity.  
However, spray drying using the medium IAH (1.1-1.3 g/m\({}^{3}\)) at 100 \({}^{\circ}\)C led to the production of powders of not significantly different \(d_{L}\), regardless of FR. A similar relationship was observed for \(d_{T}\), when the lowest IAH (0.1-0.3 g/m\({}^{3}\)) at 120 \({}^{\circ}\)C was applied. These findings suggest that increased FR in these conditions did not influence the evaporation rate to the extent that it would disrupt the formation of a shell on the droplet, impacting at the same time the bulk density. According to the literature, higher MC decreases powders' bulk density, however this relationship is dependent on the interparticle forces [28]. Evidence from this study suggests that MC did not influence the bulk density of powders, as neither \(d_{L}\) nor \(d_{T}\) increased with decreased MC, which was confirmed by the PCA (Figure A2).
# Multi-Mode Lithium-Ion Battery Balancing Circuit Based on Forward Converter with Resonant Reset
###### Abstract
Additionally, the use of numerous MOSFETs in the switch matrix in [37] not only requires complex driver circuits, increasing circuit costs, but also adds complexity to circuit control. Although the structure of the switch matrix is simplified in [38], there is a possibility of circuit malfunction when cells within the same group are involved in balancing, indicating a lower robustness of the circuit. In general, the MC2 MC balancing circuits proposed so far have improved balancing speed, but there is still room for optimizing balancing power, circuit cost, design, and control. Compared with the aforementioned converters, transformer-based balancing circuits offer advantages such as simplified control and easy isolation [39]. In this article, a lithium-ion battery active balancing circuit based on a forward converter with resonant reset and a relay matrix is proposed. The MC2 MC balancing method is realized by the proposed balancing circuit. The balancing main circuit utilizes the forward converter. It has a simple control method and enables high-power balancing. Compared to the switching matrix consisting of a MOSFET, the switching matrix consisting of relay is easier to drive and control. As a result, the costs of the balancing circuit are reduced. The contributions of this paper are as follows: The proposed balancing circuit based on a forward converter with resonant reset is an improvement and optimization of the work presented in [35], while also referencing the switch matrix proposed in [36] for circuit design. These works simplify the structure of the balancing circuit and increase the balancing power. Furthermore, an \"adaptive selection mode based on the state of high energy battery\" balancing strategy is proposed. This strategy provides the proposed balancing circuit with multiple balancing modes and flexible balancing paths to deal with unbalancing states, thereby improving the balancing speed. The remaining sections of this paper are organized as follows. Section 2 presents the balancing circuit structure and operating principle of the proposed topology, along with parameter analysis. Section 3 verifies the proposed balancing circuit through simulation of a six-cell battery circuit and analyzes the balancing performance. Section 4 introduces the \"adaptive selection model based on the state of high energy battery\" balancing strategy. Section 5 describes the experimental setup, where an eight-cell battery pack configuration is selected to validate the effectiveness of the proposed balancing circuit and strategy. The discussion and comparison of the balancing circuit is presented in Section 6. Finally, Section 7 provides the conclusions of this paper.
# Towards a Reference Architecture for Cargo Ports
## 4 Patterns in a Cargo Port
### Cargo Port Drayage
#### 4.1.3 Problem  
Drayage companies take containers in and out of warehouses, rail terminals, ocean ports, and harbors. Drivers arriving at the entrance gate are anticipating one of the following transactions: picking up a container or dropping off a container. There may be several variations in this process, such as dual transactions, trouble window transactions, and equipment issues. It requires extra time to inspect and accept a container, to receive instructions on where to take the container within the terminal, and to leave the load and either start another transaction or leave. Depending on the type of transaction, gate processing time and associated queueing vary.  
The solution to this problem is guided by the following forces:  
* Flexibility: several internal and external roles may be involved, i.e., truck operators, storage area workers and supervisors, crane operators, etc. Resources and devices used, as well as their corresponding operations, must be flexible to accommodate this variety of roles [47];
* Usability: the software used to identify the individuals entering the port, their trucks, and their container contents should be easy to use by roles that do not have technical backgrounds;
* Alerting: any attempt to deviate from the normal operations of the terminal must produce an alert and should be logged;
* Logging: any activity should be recorded and logged for future auditing. In general, every visit should be logged to keep track of any access to the facility. All containers must be registered and logged;
* Location tracking--we need to be able to find every container. A container in the wrong place can delay operations.
## 3 T2G Communication Security, Vulnerabilities, and Threats
### Attack Classification  
To standardize the categorization of the aforementioned attack types, the primary threats faced by the T2G system can be classified into four major categories based on attack behaviors and targeted objectives: communication disruption, data manipulation, temporal control, and behavioral deception, as illustrated in Figure 2.  
Figure 2: Attack classification of T2G.
## 2 Materials and Methods
### Model Training Software  
The model training software encompasses the processing of collected data and the training of the machine learning model, XGBoost. This stage details the steps necessary for data modeling and analysis.  
Data loading and preprocessing: Initially, the spectral data collected by the data collection software are loaded and subjected to preprocessing. These data contain the spectral characteristics of milk samples across various wavelengths. These characteristics are used to predict target variables such as protein and fat content. During the preprocessing stage, missing data points are checked, irrelevant data are cleaned, and the data are converted to an appropriate format.  
Data splitting: Once the preprocessing step is complete, the dataset is divided into training and testing sets. This step is essential to accurately assess the model's performance. Typically, 80% of the data are used for training the model, and the remaining 20% are used for testing. The training set guides the learning process of the model, while the testing set is used to evaluate the model's generalization ability.  
Model initialization and training: The XGBoost model is then initiated. This model is a popular machine learning algorithm known for its high performance in classification and regression tasks. It is trained on the training set, attempting to reduce the error rate with each iteration to achieve optimal results.  
Model evaluation and optimization: After training, the model is evaluated on the testing set. The model's performance is measured by comparing the predicted values to the actual values. At this stage, error metrics (e.g., mean squared error--MSE) and accuracy scores are calculated. If the model's performance is not at the desired level, hyperparameter optimization can be conducted to enhance performance.  
Model saving: Finally, the trained model and its best hyperparameters are saved. This model will later be run on the edge sensor node to make real-time predictions. Before the model is loaded onto the microcontroller, it is ensured that its size and processing requirements are compatible with the microcontroller.
## 4 Experiments
### Ablation Study
#### 4.4.1 Binary-Channel Verification for Bounding Box Refinement  
We show the effectiveness of our binary-channel verification (BCV) model within our proposed method based on the aforementioned baseline in Figure 7, denoted by BCV. We select the _MOTA_ and _IDF1_ metrics to evaluate the overall performance and the ID preserving ability. For both metrics, BCV shows significant improvements of performances on the DPM detector, while there is a relatively small cap between using the detection only and our BCV model on the FRCNN and SDP detectors. The reason for this is most likely the different quality of detection results provided by each detector. The DPM detector tends to have much more noise and usually produces partial detection under complex scenes, such as crowded and occluded environments for objects. On the other hand, BCV is able to increase both metrics for our baseline method, which is the single object tracker using only, where detection is only used to retrieve the object when occlusion happens. By introducing BCV, a clear increase of _IDF1_ and _MOTA_ shows that detection can help the tracker not only to better recognize each object during tracking, but also to optimize the training samples for the single object tracker that may lead to maintaining the identity of each object as long as possible. In this way, detection and the single object tracker may easily complement each other to achieve a better performance than using only one of them.  
Figure 6: Visualization of selected sequences from the MOT17 benchmark dataset.