esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The lithium-ion battery (LIB) has become the primary power source for new-energy electric vehicles, and accurately predicting the state-of-health (SOH) of LIBs is of crucial significance for ensuring the stable operation of electric vehicles and the sustainable development of green transportation. We collected multiple sets of charge-discharge cycle experimental data for LiFePO\({}_{4}\) LIB and employed several traditional machine learning models to predict the SOH of LIBs. It was found that the RF model yielded relatively superior predictive results, confirming the feasibility of applying the RF model to SOH prediction for the electric vehicle LIB. Building upon this foundation, further research was conducted on the RF improved model for LIB SOH prediction. The PSO algorithm was employed to adaptively optimize five major parameters of the RF model: max_depth, n_estimators, max_features, min_samples_split, and min_samples_leaf. This adaptation addresses the issue of prediction errors that stem from human experience to optimize parameters in the RF model. The results indicate that the RF improved model proposed in this paper can further improve the prediction accuracy of LIB SOH. Its model evaluation index also outperform others, demonstrating the effectiveness of this approach in the management of LIB SOH for new-energy electric vehicles. This contributes significantly to urban environmental protection and the development of green transportation.  
new-energy electric vehicles; lithium-ion battery; state-of-health prediction; random forest model; particle swarm optimization +
Footnote †: journal: Applied Sciences
## 5 Numerical Example  
The efficiency of the proposed method is demonstrated in a numerical example aimed at the design of a porous concrete composition.  
Condition: Calculate the composition of coarse concrete prepared on granite screenings of 2.5-5 mm fraction with 28-day compressive strength of 11 MPa. The bulk density of the 2.5-5 mm fraction is 1.55 kg/dm\({}^{3}\). The density of cement is 3.1 kg/dm\({}^{3}\).  
1. According to Table 6, the required strength can be obtained with a CPLT of 150 mm.
2. Determine the strength equation corresponding to \(\delta=150\) mm: \[fc=445\cdot(W/C)-496\cdot(W/C)^{2}-89\] By solving the quadratic equation, calculate the W/C: \[11=445\cdot(W/C)-496\cdot(W/C)^{2}-89;\] \[-496\cdot(W/C)^{2}+445\cdot(W/C)-100=0.\] \[W/C=0.45\]
3. The specific surface area of the aggregate grains is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cement Paste Layer** & & **Dependence of** & **Possible Compressive** \\
**Thicknesses on Aggregate** & & **Density on Strength** & **Strength Values, MPa** \\
**Grains \(\delta\), \(\mu\)m** & & & \\ \hline
100 & \(f_{c}=-415.2\cdot(W/C)^{2}+375.7\cdot(W/C)-78.4\) & \(\rho=1674+13\cdot f_{c}\) & 3–6 \\ \hline
125 & \(f_{c}=-283.2\cdot(W/C)^{2}+248.8\cdot(W/C)-45.6\) & \(\rho=1645+18\cdot f_{c}\) & 6–9 \\ \hline
150 & \(f_{c}=-496\cdot(W/C)^{2}+444.9\cdot(W/C)-88.8\) & \(\rho=1763+5\cdot f_{c}\) & 9–12 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Dependencies of porous concrete strength on W/C at different cement paste layer thicknesses on aggregate grains.  
\[S=\frac{6.35\cdot k}{1000}(100)=\frac{6.35\cdot 2}{10}=1.27\;\mathrm{m}^{2}/ \mathrm{kg}\]
4. The required volume of cement paste will be \[V_{c,p}=\delta\cdot S=0.150\times 1.27=0.1905\;\mathrm{m}^{3}\]
5. Cement consumption: \[C=\frac{0.1905}{1/3.1+0.45}\times 1000=246\;\mathrm{kg}\]
6. Water content: \[W=C\cdot(W/C)=246\times 0.45=110\;\mathrm{L}\]
7. The consumption of granite screenings will be equal to its bulk density: \[\mathrm{GS}=1550\;\mathrm{kg}\]
8. The approximate value of concrete density is calculated by the corresponding equation (Table 6): \[\rho=1763+5\cdot f_{c}=1763+5\times 10=1813\;\mathrm{kg}/\mathrm{m}^{3}\]  
The final porous concrete composition (for 1 m\({}^{3}\) of mix) is as follows: 246 kg of cement, 110 L of water, 1550 kg of granite screenings with a fraction of 2.5-5 mm.  
The above mix design method, like any other concrete mix design method, gives a preliminary mix proportion. The calculated values should then be tested and adjusted to obtain the final mix design values.
# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
## 3 System Model and Problem Formulation
### Resource Constraints  
We denote \(\mathcal{E}_{1}\) as the upper limit of energy consumption for completing a data processing task among all participating LEO satellites, while \(\mathcal{E}_{2}\) represents the upper limit of energy consumption for any individual satellite involved in the task processing. \(\mathcal{P}_{m}\) denotes the maximum operating power of the satellite. The energy and power constraints can be expressed as follows:  
\[\sum_{i=1}^{N}r_{m}C_{0}^{2}D_{i}\alpha_{0,i}+\sum_{i=1}^{N}\sum _{k=1}^{M}\frac{P_{k,i}\alpha_{k,i}D_{i}}{R_{k,i}B}\leq\mathcal{E}_{1} \tag{14}\] \[\sum_{i=1}^{N}r_{k}\alpha_{k,i}D_{i}C_{k}^{2}\leq\mathcal{E}_{2}\] (15) \[\sum_{i=1}^{M}P_{k,i}+r_{m}C_{0}^{3}\leq\mathcal{P}_{m}\qquad \qquad\qquad\qquad\qquad, \tag{16}\]  
where constraint (14) ensures that the total energy consumption is smaller than \(\mathcal{E}\), constraint (16) indicates that the maximum power of the MS is less than \(\mathcal{P}_{m}\), and constraint (33) expresses that the maximum working power of the SS is not higher than \(\mathcal{P}_{s}\).
# Comparative Analysis of Classifiers for Classification of Emergency Braking of Road Motor Vehicles
## 6 Discussion  
In a real-world driving situation, the most critical error might be in false positive cases, i.e., when automated braking is activated in an incorrectly classified emergency braking situation. It may seem that this error is a potentially dangerous one, as it may increase the likelihood of rear-end collision with the vehicle(s) traveling behind. However, our system is supposed to activate the emergency braking for a very brief period of time that is equivalent to the average delay of the driver's response. We assume that the driver behind respects the common 3 s rule, which claims that each driver should always keep a safe distance in order to have enough time to respond to problems in front of his/her car.  
As can be seen from Table 14, in the case of a wrongly classified situation the amount of braking would be insignificant, and would never cause a dangerous situation on the road. In cases of actual emergency braking situations, however, the driver would press the brake pedal just before the disengagement of the (brief) automated braking. This will ensure both early (due to the automated braking) and continuous (due to the driver's input) braking of the car in actual emergency braking situations.  
Nevertheless, our best classifier made several mistakes: 5 FN, 7 FP. Obviously, FN will not cause inconvenience to the driver; however, FP may lead to uncomfortable driving. It is probable that the incorrect classification is a result of either wrong sample marking by the expert in the testing set or wrong sample marking in the training set which caused the XGBoost classifier to learn on noisy samples. Another possible reason for the mistakenly classified samples might be the prevalence of similar driving style samples in the training dataset. For instance, if one driver prefers a fast driving style, the way he or she releases the accelerator pedal will definitely differ from the quiet driving style. This can result in \"uncertain\" output of the XGBoost classifier. For example, several instances from the training and testing sets have the output value close to 0.5, which indicates that XGBoost \"doubts\". A way of improving this possible drawback will be investigated in further research.
###### Abstract  
Nowadays there is a growing demand for small- and medium-sized enterprises (SMEs) to improve their level of digitalisation. This situation becomes even more critical in cases when SMEs act in the role of a subcontractor of large enterprises who demand the utilisation of certain digital operations. This paper aims to identify the essential Industry 4.0 attributes for the requirements of SMEs that enterprises can purchase to deploy an adequate solution with a view of increasing their competitiveness in the market. By analysing research articles and statistical data from the worldwide Web of Science database, we identify the major Industry 4.0 attributes for SME: Internet of Things (IoT), Big Data, Artificial Intelligence (AI), Cloud Computing, Simulation and Cybersecurity. Based on the review results and a survey by the European Commission, we propose devices primarily designed to implement AI tasks in industrial environments that meet the essential attributes for SMEs and have low entry costs. The subject of IoT is thoroughly addressed. Its subsets and the relationship between Industrial Internet of Things (IIoT) and Artificial Intelligence of Things (AIoT) are introduced and described. The characteristics of the listed devices as related to usability in the identified attributes are verified. Therefore, the description of the devices is provided with respect to their usability in SMEs. The main purpose of this paper is to identify attributes for SMEs and to develop strategic plans for the digitalisation requirements, particularly in the development of Artificial Intelligence as part of the implementation of the IoT pillar.  
Industry 4.0; Artificial Intelligence; Big Data; Smart Factory; resource-limited devices; AIoT; deep learning; edge AI Article
# A Military Object Detection Model of UAV Reconnaissance Image and Feature Visualization
## 3 UAVT-YOLOv5 Model
### Loss Function  
In YOLOv5, the loss function shown in Equation (2) consists of the classification loss \(L_{cls}\), location loss \(L_{loc}\), and object confidence loss \(L_{obj}\), which measure the distance between the anchor box classification and the ground-truth class, the predicting box and labeled box, and the possibility of the anchor box being an object, respectively.  
\[Loss=\lambda_{1}L_{cls}+\lambda_{2}L_{loc}+\lambda_{3}L_{obj}. \tag{2}\]  
Both \(L_{cls}\) and \(L_{obj}\) used binary cross-entropy loss, \(L_{cls}\) only computes the loss of positive samples, while \(L_{obj}\) computes the loss of all positive and negative samples. \(L_{obj}\) used CIoU loss computing for the loss of positive samples. \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) are the weighting factors, which were set as that in YOLOv5 in this work. The object confidence loss \(L_{obj}\) was obtained as  
\[L_{obj}=\beta_{1}L_{obj}^{s}+\beta_{2}L_{obj}^{m}+\beta_{3}L_{obj}^{l}. \tag{3}\]  
Figure 8: The configuration of the modified network.  
where \(L_{obj}^{s}\), \(L_{obj}^{m}\), and \(L_{obj}^{l}\) denote the loss of small, medium, and large-scale feature maps, where \(\beta_{1}\), \(\beta_{2}\), and \(\beta_{3}\) are the weighting factors, which were set as 4.0, 1.0, and 0.4, optimized in COCO dataset [15]. In this work, we add a new loss item \(\beta_{4}L_{obj}^{n}\) to Equation (3) for the newly added feature map, and \(\beta_{1}\), \(\beta_{2}\), and \(\beta_{3}\), and \(\beta_{4}\) are set based on the UAVT-3 database. The four factors were first set as 4.0, 1.0, 0.4, and 0.2 by borrowing the idea in YOLOv5 that as a small object is difficult to detect, it should be given a larger weighting in the loss. Moreover, we also take the training sample distribution into consideration due to that the class with fewer samples is more difficult to recognize. The final weights were obtained by \(\beta_{i}\times r_{i}\), where \(r_{i}\) was the ratio factor obtained by following  
\[r_{i}=norm(1/R_{i}), \tag{4}\]  
where \(R_{i}\) denotes the sample ratio of \(i_{th}\) scale in k-means clustering, and \(norm()\) denotes the normalization function.
# Diversity of Collagen Proteins and Their Biomedical Applications in Drug Delivery
## 2 Diversity of Collagens
### Mammalian Collagen
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Supra Structure** & **Types** & **Polypeptide Chains** & **Function** & **References** \\ \hline \multirow{10}{*}{Fibril} & I & \(\alpha_{1}\)(I) \(\times\) 2 + \(\alpha_{2}\)(I); \(\alpha_{1}\)(I) \(\times\) 3 & & [43] \\  & II & \(\alpha_{1}\)(II) \(\times\) 3 & & [44] \\  & III & \(\alpha_{1}\)(III) \(\times\) 3 & & [45] \\  & V & \(\alpha_{1}\)(V) \(\times\) 2 + \(\alpha_{2}\)(V); \(\alpha_{1}\)(V) + & Providing three-dimensional & [45] \\  & & \(\alpha_{2}\)(V) + \(\alpha_{3}\)(V); \(\alpha_{1}\)(V) \(\times\) 3 & scaffolds for tissues and organs & [46] \\  & XI & \(\alpha_{1}\)(XI) + \(\alpha_{2}\)(XI) + \(\alpha_{3}\)(XI) & & [47] \\  & XXIV & \(\alpha_{1}\)(XXIV) \(\times\) 3 & & [48] \\  & XXVII & \(\alpha_{1}\)(XXVII) \(\times\) 3 & & [49] \\ \hline \multirow{10}{*}{Fibril} & IX & \(\alpha_{1}\)(IX) + \(\alpha_{2}\)(IX) + \(\alpha_{3}\)(IX) & Forming a molecular bridge to & [50] \\  & XII & \(\alpha_{1}\)(XII) \(\times\) 3 & enhance the organization and & [51] \\  & XIV & \(\alpha_{1}\)(XIV) \(\times\) 3 & stability of ECM & [52] \\  & XVI & \(\alpha_{1}\)(XII) \(\times\) 3 & Promoting the adhesion and & [53] \\  & XX & \(\alpha_{1}\)(XX) \(\times\) 3 & interactions between collagens & [54] \\  & XXI & \(\alpha_{1}\)(XXI) \(\times\) 3 & Regulating the properties & [55] \\  & XXII & \(\alpha_{1}\)(XXII) \(\times\) 3 & of collagens & [56] \\ \hline Microfibril & VI & \(\alpha_{1}\)(VI) + \(\alpha_{2}\)(VI) + \(\alpha_{3}\)(VI) & Interacting with fibrils & [58] \\ \hline \multirow{2}{*}{Anchoring fibrils} & VII & \(\alpha_{1}\)(VII) \(\times\) 3 & Guaranteeing the integrity and & \\  & & & stability of the & [59] \\  & & & basement membrane & \\ \hline \multirow{2}{*}{Transmembrane collagen} & XIII & \(\alpha_{1}\)(XIII) \(\times\) 3 & \multirow{2}{*}{Participating in cell adhesion,} & [60] \\  & XVII & \(\alpha_{1}\)(XVII) \(\times\) 3 & migration, and & [61] \\  & XXIII & \(\alpha_{1}\)(XXIII) \(\times\) 3 & immune response & [62] \\  & XXV & \(\alpha_{1}\)(XXV) \(\times\) 3 & & [63] \\ \hline \multirow{2}{*}{Basement membrane collagen} & IV & \(\alpha_{1}\)(IV) \(\times\) 2 + \(\alpha_{2}\)(IV); \(\alpha_{3}\)(IV) & \multirow{2}{*}{Enforcing tensile strength and} & [64] \\  & & + \(\alpha_{4}\)(IV) + \(\alpha_{5}\)(IV) + \(\alpha_{6}\)(IV) & & anchoring to the & [65] \\  & XVIII & \(\alpha_{1}\)(XVIII) \(\times\) 3 & laminin network & [66] \\ \hline \multirow{2}{*}{Short chains collagen} & VIII & \(\alpha_{1}\)(VIII) \(\times\) 2 + \(\alpha_{2}\)(VIII) & \multirow{2}{*}{Involving in the calcification} & [67] \\  & X & \(\alpha_{1}\)(X) \(\times\) 3 & & [68] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The genetic diseases caused by glycine mutations in collagen triple helix [69].
# Portable Optical Fiber Bragg Grating Sensor for Monitoring Traffic Density
## 3 Experimental Measurement of Transport Vehicles
### Traffic Density Monitoring
#### 3.1.2 Traffic Density at the Main Entrance to the VSB-TUO Campus  
Traffic density was measured at the main entrance to the VSB-Technical University of Ostrava (GPS: 49\({}^{\circ}\)49\({}^{\prime}\)50.1\({}^{\prime\prime}\) N 18\({}^{\circ}\)09\({}^{\prime}\)38.1\({}^{\prime\prime}\) E). Measurements were conducted over 12 h (from 06:00 to 18:00). Sensor measurements recorded the crossing time, type of car (passenger, truck, or trailer) and the number of axles. All instances of passes were also manually recorded.  
The portable strip was placed in the center of the road, which had a width of 5.55 m, so that passing cars in both directions always approached the sensor strip with at least one side of the vehicle. The placement of the sensor is shown in Figure 8.  
Figure 9a shows a passing passenger car, Figure 9b shows a passenger car with a two-axle trailer and Figure 9c shows a three-axle truck. Distinguishing individual axles makes it possible to differentiate each pass with respect to the time intervals between individual axles and thus allow traffic density to be calculated.  
A total of 1518 cars passed during the measurement period (1515 passenger cars, 2 passenger cars with a two-axle trailer and 1 truck with three axles). Only 1502 cars were correctly detected using the sensor strip. The remaining 16 passes were not detected because the vehicles passed the sensor strip outside the active area. The overall efficiency of detecting cars was 98.946%. The density of operation at hourly intervals is shown in Figure 10.  
Figure 8: Measuring station at the entrance to the VSB-TUO campus.  
Figure 7: Traffic density on the local road between 10:00 and 17:00.  
Traffic density measurements for passing cars were analyzed using binary classification (whether a significant point falls under True Positive (TP), False Positive (FP) or False Negative (FN)) [32]. By determining TP, FP and FN, the Sensitivity (SE), Accuracy (ACC), Positive Predictive Value (PPV), Harmonic Means of Precision and Sensitivity (F1) and False Negative Rate (FNR) can be calculated [33].  
Figure 10: Traffic density at the main entrance to the VSB-TUO site between 06:00 and 18:00.  
Figure 9: The response of the FBG portable sensor to a passing passenger car **(a)**; a passenger car with a two-axle trailer **(b)**; and a three-axle truck **(c)**.  
\[SE=\frac{TP}{TP+FN}, \tag{5}\]  
\[ACC=\frac{TP}{TP+FP+FN}, \tag{6}\]  
\[PPV=\frac{TP}{TP+FP}, \tag{7}\]  
\[F1=2\cdot\frac{PPV\cdot Se}{PPV+Se}=2\cdot\frac{TP}{2\cdot\overline{TP+FP+ FN}}, \tag{8}\]  
\[FN=\frac{FN}{FN+TP}. \tag{9}\]  
The results of the statistics are summarized in Table 3.
# Formation Mechanism and Control Technology of an Excavation Damage Zone in Tunnel-Surrounding Rock
## 3 Blasting and Vibration Tests
Figure 2: Vibration velocity monitoring points in operating tunnel above excavation face. The maximum vibration velocity is 3.15 cm/s when the nonelectric detonators are used, and the maximum vibration velocity is reduced to 1.51 cm/s after the electronic detonator test, as shown in Figures 3 and 5, respectively. The maximum vibration velocity caused by electronic detonators is about 52.1% lower than that caused by nonelectric detonators, and the decrease is obvious. An important factor affecting the damage of surrounding rock is the maximum vibration velocity generated by cutting blasting. However, the maximum vibration velocity is randomly generated in a blast hole when using electronic detonators. Therefore, vibration reduction measures such as drilling empty holes to reduce cut blasting do not have advantages in the use of electronic detonator initiation. Another important reason is that the nonelectric detonator initiation delay accuracy is not easy to control. The delay parameter error of the design based on the burning velocity of the explosive is large, which makes it necessary to detonate multiple holes at the same time. On the contrary, the delay accuracy of electronic detonators has been significantly improved. It can realize the sequential initiation of single hole without considering the influence of cumulative error. In this way, the amount of explosive detonated at the same time is significantly less than that of nonelectric detonator. The technical improvement has led to the control of the possible higher vibration velocity from the source. The vibration velocity monitoring results of the two tests are analyzed. It is found that the peak particle velocity produced by cutting blasting is the largest in the nonelectric detonator test. The vibration velocities generated by subsequent blastholes are significantly reduced. The continuous high vibration velocity of electronic detonators may have greater adverse effects on the surrounding rock. Therefore, the method of monitoring the loose range of surrounding rock on site is adopted to explore this hypothesis.
## 3 5G Technology Enablers for V2X Use Cases
### The Evolution of V2X Communications
#### 3.1.5 3 GPP Release 17  
Release 17 investigates the enhanced support of V2X operation for pedestrian UE (i.e., UE for vulnerable road users), e.g., V2X communication with power efficiency, according to the vehicular services requirements defined in TS.22.185. [5] and TS.22.186. [28].  
Pedestrian UE often has a lower battery capacity and limited radio capability compared to vehicular UE, which can affect its power consumption and ability to send/receive V2X messages. To address this, the use of Discontinuous Reception (DRX) has been identified  
Figure 3: C-V2X deployment timeline for different types of use case groups [121].  
as a mechanism for power saving in sidelink communications. The objective is to support power-saving requirements for pedestrian UE in V2X and other use cases. For example, periodic broadcasts of road safety messages may require different DRX cycles for vehicular UE and pedestrian UE. Pedestrian UE with limited power capacities would only turn on their radio interface and perform PC5 transmission/reception during the on-duration of the DRX cycle. However, the use of DRX for pedestrian UE can result in longer delays or unreliable transmissions if not coordinated properly, especially when there are multiple PC5 communication sessions with different peers or groups. Different V2X services also have varying QoS requirements and traffic patterns, which need to be coordinated to maintain the QoS and power efficiency.  
To support NR PC5 power efficiency for pedestrian UE, several issues need to be addressed. These include studying the applicability of NR PC5 DRX or other mechanisms to V2X operations for pedestrian UE, considering the impact on the V2X layer and the 5G Core Network (5 GC). This involves studying how the 5 GC can authorize and provision the DRX mechanism for pedestrian UE without degrading the QoS. Additionally, the coordination of requirements from different V2X services and determination of how the UE activates or deactivates the DRX mode over PC5 are important aspects to consider [75; 76].