esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### M5 Prime Model Tree  
The M5p model, which is based on linear regressions and decision trees, was first developed in 1992. A binary decision tree consists of the primary terminal node with extra leaf nodes, which provide a connection between input (independent) and output (dependent) parameters [67]. It is essential to bear in mind that decision trees are generally applied for categorical data, although it is also appropriate for quantitative type data [68]. The M5p model can be summarized in two main steps: (a) splitting input data to create a decision tree; it is reached when defining the standard deviation of each subset to find an appropriate primary node (parent node). Because of this step (splitting), the SD of the child node is smaller than the parent node; (b) testing each node in the decision tree to diminish the error. The standard deviation is calculated as:  
\[SDR=sd\left(T\right)-\sum\frac{\left|T_{j}\right|}{\left|T\right|}sd\left(T_{ j}\right) \tag{3}\]  
where \(sd\) represents the standard deviation, \(T\) is a set of examples that reach the primary node, and \(Tj\) represents the subset of patterns that possess the \(j\)th outcome of the potential set.  
Thus, as stated above, based on different processes of splitting the input data, the most probable error-reducing node is chosen. For the overfitting problem in decision trees, pruning techniques were used for omitting subtrees. This pruning technique is based on methods of linear regression functions. One of the strengths of this model over the M5 rule model is its efficiency in learning and treating problems with high complexity. One of the features of this model is that its regression functions do not have many variables. The M5p model has widespread applications in engineering, medical, and agricultural disciplines [69].
## 2 Literature Review
### Hybrid Methods  
Hybrid methods are a combination of data-oriented and network-oriented methods with the intention of achieving greater precision in the search for NTLs. In this way, the total energy consumed can be estimated through some equipment installed at a location in the network. After the TLs have been calculated, it is verified whether there are NTLs at that location. If the existence of losses is established, a classification technique (supervised or unsupervised) is applied with the aim of directly targeting the offenders [3, 16]. The accuracy of these methods increases, but the disadvantages remain the cost and implementation of equipment in the electrical network [16].  
An approach associated with these methods is to detect fraudulent customer transformers based on a static state estimation model, which uses voltage and power measurements from smart meters [86]. In case of detecting theft, the automatic disconnection of switches in the distribution network is carried out to identify the fraudulent consumer through the analysis of the variation in energy consumption of all consumers. In this model, the use of smart meters is essential to obtaining state variables at the transformer level and then deriving the estimated load curves. The scheme proposed by [PERSON] et al. also involves monitoring at the transformer level in areas with a high probability of energy theft, in order to identify changes in the consumption patterns of suspected customers. Detection is based on analyzing the demand behavior of customers who are suspected of fraudulent consumption [87].  
The SVM classification technique is employed in the proposal by [PERSON] et al. The method involves installing smart meters throughout the electrical network and comparing the energy consumed by users with the energy recorded by the smart meters. If no NTLs are detected, the total consumption of customers plus TLs should match the readings from the smart meters. If there is a discrepancy, the area should be inspected [36]. The proposal by [PERSON] et al. also requires measurement devices on each transformer in a smart grid. Although the use of hardware increases the cost, the results presented yield a high number of false positives, increasing the cost due to the need for field inspections and the use of information technology equipment to assess the veracity of energy theft [88]. In addition to the required equipment, a strategy was proposed to detect NTLs by using a multivariate control chart that establishes a reliable region for monitoring the measured variance. After the detection of NTLs, a path search procedure based on the A-Star algorithm is capable of locating the consumption point with the NTLs.
# Massive MIMO Indoor Transmissions at 38 and 65 GHz Applying Novel HBF Techniques for 5G
## 2 Description of the Experimental Setup  
### Scenario  
Measurements were carried out in a laboratory at the Universidad Politecnica de Cartagena, with a volume of 8 \(\times\) 4.8 \(\times\) 3.5 m\({}^{3}\). Figure 1 shows a scheme of the laboratory and where the positions selected for the transmitter (Tx) and receiver (Rx) are located in it. The lab is furnished with desks, chairs, shelves, and closets, and is equipped with electronic devices such as computers.  
In the measurements, eleven Rx positions have been selected to study a wide range of distances within the scenario. The distance between each Rx position and the Tx is indicated in Table 2. In order to obtain multiple antennas, the Tx antenna was located in an XY positioning system, implementing a 6 \(\times\) 6 virtual uniform rectangular array (URA). Thus, 36 (6 \(\times\) 6) complex channel transfer functions (CTFs) have been measured for each Tx position. For Rx, likewise, a 1 \(\times\) 5 uniform linear array (ULA) was used. With this configuration, 36 \(\times\) 5 mMIMO measurements were performed. The separation between antenna elements for both the URA Tx and the ULA Rx was set at 3 mm for the 38 GHz band (<40 GHz/2) and 2 mm for the 65 GHz band (<065 GHz/2). The Tx and Rx antennas were located at heights of 1.647 m and 1.544 m, respectively.
# A Blockchain-Based Decentralized Public Key Infrastructure Using the Web of Trust
## 8 Discussion  
Our proposal seeks to create a robust digital certification system with a decentralized PKI on the blockchain, addressing the single-point-of-failure issue and diminishing the risk of malicious attacks. Table 2 concisely outlines the methods employed to achieve our proposed objectives and the existing challenges we successfully tackled. As we can observe from Table 2, the distributed trust model, inspired by the WoT approach, empowers authorized multiple introducers to validate and sign X.509v3 certificates. Involving multiple signatures to issue certificates enhances security, as breaching multiple ring members simultaneously becomes highly improbable. When establishing a distributed key ring, incorporating introducer depth limits the key-ring size and also prioritizes introducers who are in closer proximity to the ring owner. The decentralized nature of the blockchain removes the need for a central authority, further reducing single-point-of-failure risks. Blockchain's inherent immutability safeguards data integrity, ensuring that records are tamper-proof and publicly auditable. This transparency, coupled with the involvement of multiple introducers, streamlines the revocation process and strengthens the system against attacks. In our detailed analysis, we meticulously presented our goals and accomplishments in Table 2. By comparing the features and limitations of existing schemas in Section 2, we identified a common trend among many blockchain-based systems, as detailed in Table 3, utilizing the following metrics:  
* **Trust Model:** Evaluates the trust relationship for deciding certificate legitimacy. * **Certificate Format:** Specifies the proposed certificate format, like X.509 or PGP. * **Auto-Scalable:** Indicates whether the system features auto-scalability for large networks. * **Certificate Transparency:** Reflects the visibility of issued certificates. * **Browser Side Unchanged:** Describes the minimal modifications required for browsers and servers to align with the proposed schema. * **Decentralized Trust:** Involves distributed trust calculation across multiple servers. * **Advanced Verification:** Utilizes strong authentication methods for precise identity verification. * **Prohibits Malicious Certification:** Offers enhanced security and a robust verification system. * **No Centralized CA:** Operates without a centralized certificate authority. * **Monitors CA Activity:** Allows domains or external systems to oversee all CA operations. It is evident that most blockchain-based systems proposed for decentralized PKI approaches can achieve the fundamentals; however, they have not yet successfully prevented malicious certification. Additionally, some still rely on a central CA with limited interoperability. Through our security analysis and achievements, our system successfully addresses existing PKI vulnerabilities, including preventing malicious certification, swiftly detecting misconduct, and expediting the revocation process. These advancements significantly boost security levels compared to other studies in this domain.
# Investigation on Reference Frames and Time Systems in Multi-GNSS
## 5 Discussion  
The observed correspondences between the discontinuities in the time offset series and receiver firmware updates suggest a relationship between the two events. In order to better investigate this relationship, the information about receiver set up and the time offsets relative to Septentrio have been summarized in Table 9. In this table the marker name of a station is followed by a lowercase letter if at least one discontinuity in time offset series is observed. The lowercase letter indicates a continuity period of time offsets, so a station name associated with the same letter has the same time offsets.  
For each station every row corresponds to a new receiver set up or new time offsets relative to Septentrio, or both the events: in this case the discontinuity of time offsets corresponds to an update of receiver set up. These correspondences are highlighted by using a bold font.  
A total number of 19 discontinuities have been identified. Among these, 11 are connected to receiver updates and six are due to unknown causes. Two of the 11 discontinuities connected to a receiver update involve also the antenna: COMOc and GANPc, as it can be noticed in Table 1. The other two discontinuities are linked to changes in antenna configuration: TLSEb shows a discontinuity on 11 March 2016, when the alignment from Nord was changed from 0 to \(-140\) degrees, WTZZb shows a discontinuity on 13 July 2015, when the ARP (Antenna Reference Point) Up was changed from 0.045 to 0.284 m.  
In Table 9 there are 92 receiver firmware updates, only 11 of these are connected with a discontinuity in time offset. So most of the receiver updates do not imply a discontinuity in time offset. Conversely more than half of the discontinuities in time offsets are connected to an update of receiver firmware. For these reasons it is necessary to keep monitoring both the configuration changes of receivers and the time series of time offsets.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multicolumn{2}{c}{**STATION**} & \multicolumn{4}{c}{**ECECIVIVE**} & \multicolumn{4}{c}{**CAILIRATION [ns]**} \\ \hline
**ID** & **FROM** & **TO** & **RECIVIVE** & **TYPE** & **TERWARE** & **4_GCP_ & **_d_GPCA_** & **4_GPC_ & **4_GPC_ \\ \hline
**BDNSt** & 11 January 20
## 3 Results
### Spatial Coverage and Distribution of Land Cover Categories
Pastures (Cultivated grass, Silvopastures, and _Pennisetum_ patches) constitute about 10.5% of the study area (Table 11, Figure A3). The vast majority (5620 ha) of pastures are located within the agricultural regions of all islands, compared to 733 ha found in the surrounding park areas. The agricultural area of Santa Cruz has the most extensive pastures (3726 ha), representing 32% of the agricultural zone's surface area. Food crops (permanent crops, transitory crops, and _Coffea_ patches) constitute about 8% of the study area across all islands (Table 11, Figure A4). Most food crops (4612 ha) are located within the agricultural areas, compared to 302 ha of GNP areas classified as food crops. Furthermore, most food crops (3914 ha) are categorized as permanent rather than transitory crops. Floreana's agricultural area has the highest percentage of its agricultural area covered by permanent and transitory crops (27%, 74 ha), but Santa Cruz has the largest extension (2549 ha) covered by food crops of all islands. The estimates from Table 11 are conservative because the mixed forest and pioneer vegetation categories cover about 9.6% of the surface area, and these categories are, by definition, a mixture of other categories (Table 11, Figure A5). These vegetation types are about the same surface area (2926 ha) within the agricultural zones than in the surrounding park areas (2900 ha). San Cristobal is the island with the most amount of vegetation within its agricultural area categorized as mixed forest or pioneer (1175 ha).
# A GIS Partial Discharge Defect Identification Method Based on YOLOv5
## 3 GIS Defect PD Pattern Recognition Based on YOLOv5 Algorithm
### YOLOv5 Detection Algorithm
#### 3.1.4 Activation Function and Loss Function  
In the backbone feature extraction network of the YOLOv5 algorithm, the Leaky_relu activation function in the CBL convolution module in YOLOv4 is changed to the SiLu activation function [22]. The expression of the SiLu activation function is shown in Equation (1).  
\[\left\{\begin{array}{l}f(x)=x\cdot\text{sigmoid}(x)\\ \text{sigmoid}(x)=x\cdot\frac{1}{1+e^{-x}}\end{array}\right. \tag{1}\]  
Figure 6: Focus Network Structure.  
Figure 7: PANet network structure.  
where \(x\) is the characteristic parameter. The SiLu activation function is a modified version of the Sigmoid and ReLu activation functions. The SiLu activation function is upper bound-free, smooth, and non-monotonic, and operates better than the ReLu activation function on deep models.  
The selection of the loss function is crucial for evaluating the accuracy of target detection. The intersection over union loss (IoU Loss) [23] function used in the earlier YOLO series networks is simple in design, and only calculates the ratio of the intersection area and the union area of the prediction frame to the true frame, which often does not fully reflect the relative positions of the prediction frame and the true frame. Therefore, after introducing the two parameters of center distance and aspect ratio at the YOLO_Head position, the complete IoU loss (CIoU Loss) is formed as shown in the following equations:  
\[\text{CIoU}=\text{IoU}-\frac{D_{2}^{2}}{D_{\mathcal{C}}^{2}}-\alpha v \tag{2}\]  
\[\alpha=\frac{v}{(1-\text{IoU})+v} \tag{3}\]  
\[v=\frac{4}{\pi^{2}}(\arctan\frac{\omega_{gt}}{h_{gt}}-\arctan\frac{\omega}{h} )^{2} \tag{4}\]  
where \(D_{2}\) is the distance between the center point of the prediction frame and the target frame, \(D_{\mathcal{C}}\) is the diagonal length of the minimum external rectangle, \(\omega_{gt}/h_{gt}\) and \(\omega/h\) are the aspect ratios of the prediction frame and the target frame.
# Cost Analysis and Optimization of Modern Power System Operations
## 8 Conclusions  
This study proposes an optimization-based approach for solving the unit commitment (UC) problem in both traditional and modern power system networks. The Mixed Integer Programming (MIP) technique is utilized due to its high modelling accuracy, global optimality, and flexibility in handling system constraints. The problem is implemented using GAMS software over a 24 h operational horizon, and simulations are conducted across four distinct scenarios. Comparative analysis across these case studies highlights the economic and operational benefits of transitioning to flexible and renewable-integrated power systems.  
Figure 15: Generation schedules vs. load demand—Case 1 and Case 2 (same load).
# Sand and Dust Storms: Impact Mitigation
## 4 SDS Hazards and Their Mitigation
### SDS Source Mitigation
#### 4.1.2 Controlling Wind Erosion on Rangeland  
Methods for controlling wind erosion on rangelands are largely comprised of preventive measures designed to reduce the pressure of grazing. Livestock may be excluded from pastures (either permanently, for a few years, or seasonally), a ban often enforced with fencing. Alternatively, authorities may seek to reduce stocking rates, frequently by putting a cap on livestock densities with the introduction of prescribed carrying capacities per hectare in areas where grazing is allowed, although it should be noted that the notion of carrying capacity is a controversial one [80; 81]. All of these measures have been introduced in various parts of China's rangelands in recent decades as policy instruments designed to improve the environment by restoring grassland ecology, combatting desertification and reducing dust storms [82].
## 1 Introduction  
Orthotropic steel decks (OSDs) have been widely used in steel structure bridges due to their light weight, strong load-bearing capacity, convenient construction and other advantages [1, 2]. However, due to their complex structure and large number of welds, OSDs can be prone to fatigue cracking under repeated cyclic vehtabic leloads [3, 4]. Fatigue cracks can occur in various forms in OSDs, such as in the diaphragm-to-rib weld, the rib-deck weld, and the arc notch [5]. Among them, fatigue cracks formed in diaphragm-to-rib welds have attracted a lot of attention in recent years.  
As fatigue cracking in OSDs has become a continuous and serious problem, related research on the mechanism of fatigue cracking and maintenance methods has been carried out constantly. At present, several maintenance methods have been developed by researchers to repair fatigue cracks in OSDs, for instance, cold reinforcement [6, 7], drilling stop-holes [8, 9], weld repair [10, 11], and impact crack closure retrofit [12, 13, 14]. Cold reinforcement is used to bond or bolt steel or fiber reinforced polymer (FPR) plates to cracked zones to bear loads together, thereby reducing the stress distribution and hindering crack propagation. Cold reinforcement methods were evaluated via fatigue testing, numerical simulations, and case studies. The results indicated that the methods of bonding and bolting steel plates obviously improved the local stiffness and decreased the local stresses [6]. An improved method combining drilling stop-holes and bonding carbon fiber-reinforced polymer (CFRP) strips was proposed and proved markedly effective at improving the fatigue life [15]. Compact tension (CT) models under different methods were established for type I, II and III fatigue cracks; maintenance effects were then compared, and the results showed that steel plate reinforcement had a better effect [16]. However, reports on the influence of steel plate size and shape on the maintenance effect and the stress distribution in non-reinforced parts of component are still scarce.  
In this paper, numerical methods were used to analyze the variations in stress intensity factors at the crack tip and stresses on the non-reinforced side after reinforcement via different-shaped steel plates, in relation to diaphragm-to-rib fatigue cracks on a real steel bridge. Based on the numerical simulations, the reinforcement scheme using a polygonal steel plate was determined and field maintenance was conducted. The maintenance effect was evaluated via analyzing strain data and stress distribution. The results could provide a reference for the repairing of diaphragm-to-rib fatigue cracks in OSDs.
## 4 Results
### Analysis of Long-Term Prediction Result
In order to further assess the model's capacity for fitting data and confirm the claim that FPHFA can provide a more accurate representation of sudden change points, as shown in Figures 11 and 12, when the concentration of PM\({}_{2.5}\) is not stable, especially when the value is higher than 400, the outcomes of the compared model cannot follow the actual values, and the error is visibly larger. This reveals that it is challenging for the model to provide  
Figure 11: Comparison between actual value and predicted PM\({}_{2.5}\) value throughout the course of a month (16 December 2016–15 January 2017) for prediction steps of 24 h and 96 h, in experiments on the Beijing dataset, with different models (LSTM, GRU, CNN-LSTM, DAQFF, and FPHFA). **(a1)** LSTM model for prediction 24 h ahead; **(a2)** LSTM model for prediction 96 h ahead; **(b1)** GRU model for prediction 24 h ahead; **(b2)** GRU model for prediction 96 h ahead; **(c1)** CNN-LSTM model for prediction 24 h ahead; **(c2)** CNN-LSTM model for prediction 96 h ahead; **(d1)** DAQFF model for prediction 24 h ahead; **(d2)** DAQFF model for prediction 96 h ahead; **(e1)** FPHFA model for prediction 24 h ahead; **(e2)** FPHFA model for prediction 96 h ahead. a reliable prediction of PM\({}_{2.5}\) concentration for such horizon values. Furthermore, we find that in comparison to the other models, FPHFA can predict high PM\({}_{2.5}\) concentrations with accuracy, giving a high consistency between predicted and observed values. In combination with the experimental outcomes in Figures 11 and 12, we can clearly see that, in general, the mutation points of PM\({}_{2.5}\) concentration appear at higher concentrations and in smaller numbers. This phenomenon causes the problem of inadequate learning of prediction models, and it is challenging for the models to learn the change patterns of PM\({}_{2.5}\) concentration under sudden changes. This is why most deep learning models yield poor fits to the data in the presence of sudden changes in PM\({}_{2.5}\) concentration. Figure 12: _Cont._  
In conclusion, for the proposed FPHFA, the long-term prediction efficacy is greater than for other deep learning models. The long-term predicted PM\({}_{2.5}\) concentration of the FPHFA model is well matched with the actual values, which means that FPHFA can usefully study the spatial correlation and long-term time-dependent features of PM\({}_{2.5}\) time series data.