esa-sceva/satcom-chunk-collection huggingface dataset:
# Repositioning Bikes with Carrier Vehicles and Bike Trailers in Bike Sharing Systems
## 3 Problem Formulation
Once the distribution of bikes across the stations for time step \(t+1\) was obtained, we utilized this information to compute the repositioning strategy for trailers and vehicles for time step \(t+1\). This iterative process continued until we reached the last decision epoch. 3. Customers can rent a bike for 30 min or more, and they have to know in advance at which station they will return the bike. On the other hand, they return their bikes to the nearest available station if the destination station is full, and they leave the system if they encounter an empty station. The goal of our DRRPVT approach is to maximize the expected profit over the entire time horizon. Let \(U\) denote the sum of revenue of hired bikes and the fuel cost of vehicles and the value of bike trailers. We provide an optimization model for a given DRRPVT. Specifically, we provide a mixed integer linear programming (MILP for short) that computes a profit-maximizing repositioning and routing solution. The objective is shown in Equation (1):  
\[\max_{y,z,a,b}U =\max_{y,z,a,b}\sum_{s,s^{\prime},t}R^{t}_{s,s^{\prime}}\times x^ {t}_{s,s^{\prime}}-\sum_{t,p,s,s^{\prime}}b^{t}_{s^{\prime},p}\times\hat{P}_{ s,s^{\prime}-\sum_{t,p,s,s^{\prime}}P_{s,s^{\prime}}\times z^{t}_{s,s^{\prime},p}} \tag{1}\] \[s.t.\text{ \it constraints C1-C15 which depends on }y,z,a,b.\]  
**Objective:** To represent the trade-off between lost demand (or alternatively the revenue from customer trips) and the value \(\hat{P}\) of bike trailers and the value \(P\) of using carrier vehicles, we employ the dollar value of both quantities and combine them into the overall profit at any decision epoch in Expression (1). The **notation** used in the formulation are shown:\(y_{s,\vartheta}^{+,t}\) denotes the number of bikes picked up from station \(s\) by vehicle \(v\) at decision epoch \(t\). \(y_{s,\vartheta}^{-,t}\) denotes the number of bikes dropped at station \(s\) by vehicle \(v\) at decision epoch \(t\). \(z_{s,\vartheta^{\prime},}^{t}\) denotes whether vehicle \(v\) picks up bikes from station \(s\) at decision epoch \(t\) and drops off at station \(s^{\prime}\) at decision epoch \(t+1\). \(a_{s,\vartheta}^{+,t}\) denotes the number of bikes picked up from station \(s\) by bike trailer \(v\) at decision epoch \(t\). \(a_{s,\vartheta}^{-,t}\) denotes the number of bikes dropped off at station \(s\) by bike trailer \(v\) at decision epoch \(t\). \(b_{s,\vartheta^{\prime}}^{t}\) denotes a binary decision variable that is set to be 1 if bike trailer \(v\) picks up bikes from station \(s\) in at decision epoch \(t\) and returns bikes to station \(s^{\prime}\) in at decision epoch \(t+1\) else 0 otherwise. \(x_{s,\vartheta^{\prime}}^{t}\) denotes the number of hired bikes moving from station \(s\) at decision epoch \(t\) to station \(s^{\prime}\) at decision epoch \(t+1\).
# A Virtual Network Resource Allocation Framework Based on SR-IOV
## 4 Basic Idea and Implementation
### Status Monitor Module
#### 4.4.1 I/O Status Monitor Module  
To be able to distinguish between I/O-intensive VMs, we should first obtain the I/O status information of the VM. We observed that I/O-intensive VMs have similar properties to I/O-intensive processes in Linux [33], that is, the CPU usage time of I/O-intensive VMs is always shorter, but it takes longer to wait for some I/O events to block. To this end, we use the virtualization management tool _Libvirt_ to obtain the resource usage of each VM, and use these data to determine the I/O-intensive state of the VM.  
_Libvirt_ is a widely used open source virtualization management and monitoring tool that can be used to manage almost all major virtualization platforms including KVM, Xen, etc., and _Libvirt_ provides an interface for acquiring VMM data. The VM in KVM is a _qemu-kvm_ process of the OS at runtime and is scheduled by the process scheduler like other processes of the OS. CPU-intensive VMs require a large amount of CPU for calculations, while I/O-intensive VMs always use less CPU time because of the long wait times for waiting for certain I/O events. Therefore, we determine the type of VM by calculating the CPU usage of each VM. The CPU usage of the I/O-intensive VM will be much smaller than that of the CPU-intensive VM in the same sampling period.  
The library function provided in _Libvirt_ cannot directly obtain the CPU use of the VM, but the CPU time difference cpu_time_diff that the VM runs during the sampling period can be obtained through the virDomainGetInfo interface, and the CPU use is calculated by dividing the actual CPU running time of the host. which is:  
\[cpu\_time\_diff=cpuTime_{now}-cpuTime_{ago} \tag{1}\] \[cpu\_usage=100\times cpu\_time\_diff/(time\times nr\_cores \times 10^{9}) \tag{2}\]  
In (1), \(cpuTime_{now}\) is the total running time of the VM at the current moment, and \(cpuTime_{ago}\) is the running time of the VM before the sampling period. In (2), \(time\) is the sampling period and \(nr\_cores\) is the number of server CPU cores. The unit of \(cpuTime_{now}\) and \(cpuTime_{ago}\) is nanoseconds, and the unit of \(time\) is seconds, so the denominator is multiplied by \(10^{9}\) when calculating \(cpu\_usage\). We define the I/O factor that determines the I/O density of the VM:  
\[io\_degree=100\times(1-cpu\_usage) \tag{3}\]  
The greater the I/O density of the VM, the larger the I/O factor \(io\_degree\). Therefore, the _I/O Status Monitor module_ can determine the I/O-intensive state of the VM through \(io\_degree\).
## 5 Conclusions  
In this paper, we propose BBDetector, a more precise and scalable TPL detection method for target binary executables with fine-grained function-level features. There are a lot of works related to TPL detection. Most existing methods only consider syntactic  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Binary**} & \multirow{2}{*}{**Libs Linked**} & \multicolumn{4}{c}{**BBDetector**} & \multicolumn{4}{c}{**B2 SFinder**} \\ \cline{3-8}  & & **TP** & **FP** & **FN** & **TP** & **FP** & **FN** \\ \hline busybox & 3 & 2 & 0 & 1 & 2 & 0 & 1 \\ openssl & 5 & 3 & 0 & 2 & 1 & 0 & 4 \\ openvpn & 5 & 3 & 1 & 2 & 0 & 0 & 5 \\ sqlite3 & 3 & 1 & 0 & 2 & 0 & 0 & 3 \\ ssldump & 2 & 2 & 1 & 0 & 0 & 0 & 2 \\ tcpdump & 3 & 2 & 1 & 1 & 1 & 0 & 2 \\ vim & 4 & 3 & 1 & 1 & 0 & 0 & 4 \\ Total & 25 & 16 & 4 & 9 & 4 & 0 & 21 \\ \hline \hline \end{tabular}
\end{table}
Table 7: TPL detection on obfuscation ubuntu binary dataset.  
features, and a few rely on expensive graph matching and function semantic features that access the same data. Compared to these TPL detection methods, the innovation points of our work is embodied in two aspects: more precise and scalable. On one hand, in addition to syntactic features, we also consider fine-grained function-level semantic features, which can more accurately represent a target binary program or a TPL. On the other hand, to make TPL detection based on semantic features more scalable, we design a function vector similarity search method to seek anchors functions (each pair of anchor functions are considered as matched functions) and the candidate TPLs. Then, we carry out TPL detection based on these candidate libraries and anchor functions. The experiment results demonstrated that BBDetector outperforms the existing optimal TPL detection methods B2 SFinder and ModX in effectiveness, efficiency and obfuscation-resilient capability.  
Conceptualization, X.Z. and J.W.; Data curation, Z.F. and X.Y.; Methodology, X.Z. and Z.F.; Investigation, X.Z. and J.W.; Writing--original draft preparation, X.Z.; Writing--review and editing, X.Z., X.Y., J.W. and S.L.; Supervision, J.W. and S.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Foundation Strengthening Key Project of the Science & Technology Commission 2019-JCJQ-ZD-113; in part by the National Key Research and Development Program under Grant 2022 YFB3305203; in part by the National Natural Science Foundation of China under Grant U2133208 and Grant 62101368; and in part by the Basic Research Program of China under Grant 2021-JCJQ-ZQ-082; and in part by the Sichuan Youth Science and Technology Innovation Team under Grant 2022 JDTD0014.  
Not applicable.  
The calculated data presented in this work are available from the corresponding authors upon reasonable request.  
The author would like to thank the anonymous reviewers for their valuable comments on our paper.  
The authors declare no conflict of interest.
# Handover Management for D2D Communication in 5G Networks
## 1 Introduction
This set will be updated at every time interval. Second, in order to let both users of the same D2D pair connect to the same eNB as much as possible and further reduce the number of handovers, we will make a handover decision for each D2D pair by jointly considering the RSRPs observed by both users to the original serving eNB and the target eNB. The main contributions of this paper can be summarized as follows:  
* Our approach ensures a more stable connection. In a realistic environment, due to the mobility of UE and the shadow fading, it is possible that the received signal strength is unstable. It results in frequent handovers, increased latency, and throughput degradation. In our proposed method, the historical moving directions and the RSRPs of both devices of the same D2D pair will be taken into account to pick up the handover target eNB. * For finding the best target eNB for a D2D pair to jointly hand over two devices, the proposed method uses the RSRPs of both devices of the same D2D pair to define a utility function, which serves as a criterion used to choose the best handover target eNB. * Our scheme satisfies the transmission quality of each device and reduces the number of unnecessary handovers. This is because we make a handover decision for the same D2D pair by the connection quality and stability. The rest of this paper is organized as follows. In Section 2, we provide a survey of related work in the literature. Then, our system model and the problem formulation are described in Section 3. The proposed approach is presented in Section 4. Subsequently, we analyze the performance of our proposed method, present the numerical results, and discuss the comparison with other approaches in Section 5. Finally, some conclusions will be drawn in Section 6.
## 2 Power Quality Disturbance Feature Extraction
### Feature Selection to Select the Best Feature  
The combination of different features has different effects on the classifier. In order to verify that the proposed feature is an effective feature and find the best feature combination, the existing research adopts the multi-objective feature selection method. The H-ELM classifier proposed in this paper has a high calculation speed, and the difference in the number of classification features is less than 10, which has little effect on the classification speed of the classifier.  
In this paper, the particle swarm optimization algorithm is used to optimize the error of the classifier to select the best feature combination. The features selected by PSO-SVM meet the classification accuracy requirements of H-ELM. The main idea of PSO is to select the subset as a search optimization problem, generate different combinations, evaluate the combination and compare with other combinations. This makes the selection of the best feature subset an optimization problem. The algorithm used in this paper uses SVM classification accuracy as the objective function as shown in Equation (10), where \(a_{i}\) is the SVM classification accuracy.  
\[Q_{fitness}=a_{i} \tag{10}\]  
For the fitness definition, the classification accuracy \(a\), representing the percentage of the example of the correct classification, is evaluated by Equation (11). The number of examples of correct and incorrect classifications is represented by \(c\) and \(u\), respectively.  
\[a=\frac{c}{c+u}\times 100\% \tag{11}\]  
The classification accuracy of SVM is the objective function, the classification error is the lowest, and the most suitable feature combination is selected. The best feature combination selected by the PSO single-objective optimization feature selection is shown in Table 2.  
The feature selection algorithm used in this paper is offline. The feature selection is first performed to find the best combination of features. Then, the feature extraction is performed for the feature quantity to be extracted with the selected best feature, which reduces the computational complexity of the feature extraction and the running time of the algorithm. The selected features are related to the data set and related to the parameter selection of the feature selection, but a common result is that the feature combinations selected by this method have better classification accuracy.
## 3 System Architecture
### Physical Layer (WSN)
For secure transmission of information between the sensor nodes and the control units, both RSA and Diffie-Hellman, based on the elliptic curve cryptography, have worked optimally in WSNs [23].
## 4 Basic Principle of Thermal Parameter Inversion
### Parameter Selection
The chief parameters of thermal performance during the calculation of the temperature field of the concrete are final adiabatic temperature rise \(\theta_{0}\), temperature rising law parameters \(n\), surface heat transfer coefficient \(\beta\), thermal conductivity \(\lambda\), thermal diffusivity \(\alpha\), density of concrete \(\rho\), and specific heat \(c\). It merits attention that the number of inversion parameters has a substantial impact on the accuracy and speed of inversion. The more parameters there are, the more inappropriateness is increased and the lower the accuracy is. Therefore, the inversed thermal parameters should be preferentially selected to be sensitive to external factors and more crucial for the computation of the concrete temperature field. The concrete density \(\rho\), specific heat \(c\), and thermal diffusivity \(\alpha\) vary little with external factors, and more accurate results can be obtained through laboratory tests. Moreover, the thermal conductivity \(\lambda\) can be established based on the formula \(\alpha=\lambda/c\rho\). Considering that concrete is a material of poor thermal conduction, the final adiabatic temperature rise is regarded as a thermal parameter that varies considerably after the pouring block is affected by various external factors. In addition, \(\theta_{0}\) is also affected by the variety of concrete cement, the amount of cement, the variety and dosage of mixed materials, etc., and there are differences between the laboratory test environment and the onsite construction conditions, so it is necessary to invert \(\theta_{0}\). In Equations (12) and (13), the temperature rising law parameter \(n\) also exerts a significant influence on temperature calculation. After the completion of pouring, in order to ensure its quality, it is necessary to implement thermal insulation coverage on the surface or adopt a watering spray to maintain appropriate temperature conditions. For the block surface with different thermal insulation measures, we should use the different surface heat transfer coefficient \(\beta\) to simulate, and \(\beta\) needs to be inverted. In summary, we select the final adiabatic temperature rise \(\theta_{0}\), surface heat transfer coefficient \(\beta\), and temperature rising law parameter \(n\) as the inversion parameters.
## 4 Results from the Literature Review
### The Use of Blockchain  
Blockchain technology enables decentralized data storage and transfer without intermediaries or a central system. As a digital ledger, blockchain is made up of interconnected blocks that record transactional information, with each block containing data from the previous one, forming a secure chain [167]. In logistics and transport, blockchain allows for fast, transparent tracking of goods flow, reducing risks of fraud, counterfeiting, and administrative errors. Its transparency, traceability, and reliability allow companies to manage resources effectively, reduce costs, and maintain stability in times of economic uncertainty [168]. This technology has also demonstrated its value in health crises, such as during the COVID-19 pandemic, by enabling secure tracking of medical supply chains, equipment, and patient records [169]. Blockchain further supports eco-friendly practices in supply chains by enabling tracking of location and pollution data (primarily CO2), which enhances transparency in corporate sustainability efforts [170]. Blockchain's role in humanitarian aid is also significant, providing precise tracking of deliveries to at-risk areas and ensuring resources are fairly distributed to those in need [171; 172]. In military logistics, blockchain can offer secure, transparent tracking of supplies and equipment, reducing the risks of errors, delays, and other logistical challenges that could affect mission success [173].  
Last-mile logistics represents the final step in delivering goods from a warehouse or distribution center to the end consumer. This area faces numerous challenges today, including growing volumes of goods, time constraints, rising costs, an aging workforce, and sustainability concerns [174]. Emerging solutions include autonomous vehicles, alternative transport methods, and distributed warehousing systems. Technologies such as UAVs and small unmanned ground vehicles (SUGVs) reduce delivery costs through automation, route optimization, and lower expenses for wages, fuel, and vehicle maintenance, supporting the financial stability of businesses [35]. The use of UAVs, light electric freight vehicles (LEFV), and alternative transport modes, such as electric cargo bikes or tricycles, also helps alleviate urban congestion and reduce carbon emissions, advancing sustainable development goals. Compared to traditional delivery vehicles, these options require less road space and offer greater ease of access and parking [175; 176]. Studies further underscore the importance of last-mile logistics innovations for public health. Solutions such as delivery networks of cooperating unmanned ground vehicles (UGVs) [177] or the transport of medicines, food, and healthcare supplies by drones or autonomous robots have proven invaluable during public health crises. During the COVID-19 pandemic, these technologies enabled safe, rapid, and contactless delivery of essential goods, reducing infection risks and maintaining supply chain continuity despite movement restrictions [178; 179].
## 2 Materials and Methods
### Vis-NIR Spectroscopy  
The diffuse vis-NIR reflectance of air-dried and 2 mm sieved soil samples was measured in the laboratory. Before spectral measurements, the samples were placed in Petri dishes and exposed to air for two days at a room temperature of about 24 degrees to remove any potential residual moisture; further grounding was carried out on each sample to lessen anisotropic scattering.  
An ASD FieldSpec Pro spectroradiometer (Analytical Spectral Devices Inc. 2013, Malvern, UK) was used for spectral measurements, with a sampling interval of \(\leq\)1.5 nm for the 350-1000 nm spectral region and 2 nm for the 1000-2500 nm one. Reflectance spectra were acquired with the help of a contact probe equipped with a halogen lamp (2901 A) 10 K) as an illuminant. A Spectralon(r) white panel (Diesseinstrument srl, Melzo (MI), Italy) was used to calibrate the instrument. In order to reduce the instrumental noise, four measurements recorded for each soil sample were averaged. The noisy portions of the spectra, between 350 and 399 and between 2451 and 2500 nm, were removed, leaving only the portion between 400 and 2450 nm.  
The reflectance spectra of each group resulting from the application of PCA and Cluster analysis to the chemical variables previously discussed were averaged and transformed into their absorbance; second derivatives of absorbance spectra were then calculated. Both mean and second derivative spectra were visually analysed.
## 4 Performance Analysis
### Simulation Environment  
For the performance analysis of the reliability in lossy WSNs, we measured the data deliver rate, the protocol overhead, and the average transmission delay time during simulations with _ns_-2 simulator [30]. We performed five simulations to investigate the diverse effect of lossy WSNs under different network conditions. In the first simulation, one source and one sink are deployed to compare grid topology with random topology. The second simulation is performed to analyze the difference between the different position of a sink and multiple sources as compared with the first simulation.  
In the first simulation, 100 sensor nodes are located in the 200 m \(\times\) 200 m network topology for the grid topology. 200 sensor nodes are deployed in the same network size as a uniform random distribution for a random topology. A sink and a source are placed at each corner during the first simulation.  
For the analysis with multi sources and one sink, the network topology is the same as the grid topology, but the number of sources and a sink position is different. The shape of MCP depends on the position of sources and sinks. There are three different types of MCP in multi source experiments.  
Figure 4: The topology used in the simulation (i.e., error rate: 10%). **(a)** Grid topology, **(b)** Random topology.  
DD implemented the 802.11b MAC model to emulate realistic sensor radio [12] in the _ns_-2. We followed the same radio model and energy model used in DD. The transmission range of a node was only changed to 30 m to maintain the macroscopic connectivity of a sensor field. Other parameters were not modified to exclude any influence on the original DD. The detailed simulation parameters are shown in Table 2.
# Fine-Grained Detection of Pavement Distress Based on Integrated Data Using Digital Twin
## 3 Methodology
### Digital Twin-Based Generation of Integrated Dataset for Pavement Distress
#### 3.1.3 Integrated Dataset Generation with Different Virtual-to-Real Ratios  
The virtual camera in UE4.26 is used for virtual-image acquisition based on the digital twin model, maintaining the same acquisition angle (orthophoto) and image output frame rate as the UAV. The sizes of the virtual pavement images are adjusted into 640 pixels \(\times\) 640 pixels. Finally, six integrated datasets are constructed based on the real and virtual data of asphalt pavement distress, each including 0, 100, 200, 300, 400, 500 virtual distress images and 100 original real distress images, respectively, as shown in Table 2.