esa-sceva/satcom-chunk-collection huggingface dataset:
# Trajectories and Resource Management of Flying Base Stations for C-V2X
## 4 Aerial Operation
### Radio Resource Scheduling
In fact, if the vehicle served by a TBS was inside the UAV coverage range (determined by the ATG channel model), its RRs were not reused by the UAV; instead, if it was outside the coverage range, the signal received by the UAV was negligible [45].
## 2 Related Work  
### IP Multicast  
The popular SBT-based multicast methods include Multicast OSPF (MOSPF) [22], Distance Vector Multicast Routing Protocol (DVMRP) [23], Protocol Independent Multicast-Dense Mode (PIM-DM) [24], and Protocol Independent Multicast-Source Specific Multicast (PIM-SSM) [25]. The ST-based multicast methods mainly include Core-Based Tree (CBT) [26] and Protocol Independent Multicast-Sparse Mode (PIM-SM) [27].  
PIM-SM is the most widely used IP multicast method, which constructs a shared tree rooted at the RP and uses the \"Pull mode\" to transmit multicast data. In PIM-SM, the multicast source sends the multicast packets to the RP, and then the RP forwards them to the receivers along the multicast tree. PIM-SM can also switch the shared tree to the source-based tree. PIM-SM can use the bootstrap mechanism to select a set of candidate RPs. Each router uses a hash function to map a multicast group address to a candidate RP. The hash function can uniformly and uniquely select a candidate RP for each multicast group to balance the load. But the limited number of RPs still face overload problems in scenarios where there are massive multicast groups such as the IoT, and the management between multiple RPs is very complicated.  
OCBT [28] has modified the basic CBT protocol with multiple cores to solve the looping problem. Since each core cooperates to form a tree, the behavior of this structure is no different from a single-core tree in terms of performance metrics such as delay, routing state, and fault tolerance. Multiple-Core Tree (MCT) [29] uses multiple active independent cores to construct multiple shared multicast trees to improve fault tolerance and avoid traffic concentration. In MCT, each core is the center of a separate multicast tree, and there is no coordination or dependency between these cores. MCT proposed two mechanisms: sender-to-all and member-to-all. For sender-to-all, each source sends data to all the cores and receivers join to only one of the cores. For member-to-all, each source sends data to just one of the cores and receivers join all of the cores.  
Many core/RP selection methods have been proposed to guarantee the QoS of some applications. An Adaptive Tabu Search based algorithm is proposed to find the best location of RP so that the cost and delay of the multicast tree can be minimized simultaneously [30]. To reduce multicast cost and end-to-end delay, VNS-RP is proposed based on VNS heuristic algorithm [6]. D2V-VNS-RPS algorithm is proposed based on VNS heuristic algorithm, which selects the RP router by considering tree cost, delay, and delay variation [31]. A static core selection approach that uses distance vector routing (DVR) as the unicast routing protocol is presented [32]. Two kinds of new RP selection schemes iRPSA and nRPSA are proposed to reduce the end-to-end delay by using RTCP report packet fields [33]. SDN can be used to improve multicast traffic manageability and enhance traffic engineering (TE) performance [34].
# A Novel Ring Spring Vibration Isolator for Metro Superstructure
## 2 Theoretical Derivation and Numerical Simulation of Ring Spring
### Analytical Analysis
#### 2.1.2 Stiffness  
During the loading and unloading process of the spring, the characteristic curves do not coincide due to the change in the direction of the friction force. When the ring spring receives the axial force \(F\), the deformation \(f\) will take \"+\" when loading and \"\(-\)\" while unloading, as in Equation (4) [8]:  
\[f=\frac{nF}{2\pi E\tan\beta\tan(\beta\pm\rho)}\bigg{[}\frac{D_{01}}{A_{1}}+ \frac{D_{02}}{A_{2}}\bigg{]} \tag{4}\]  
where \(n\) is the number of conical contact surfaces of the ring spring, \(E\) is the elastic modulus of the material (\(2.06\times 10^{5}\) MPa), \(D_{01}\) is the diameter of the center of the inner ring section, \(D_{02}\) is the diameter of the center of the outer ring section.  
The stiffness is the ratio of load increment to deformation increment of the ring spring, which is the load required to produce unit deformation. Then, it can be derived from the above formula, which takes \"+\" when loading and \"\(-\)\" when unloading:  
\[K=\frac{2\pi E\tan\beta\tan(\beta\pm\rho)}{n(\frac{D_{01}}{A_{1}}+\frac{D_{02} }{A_{2}})} \tag{5}\]
# Post Quantum Cryptographic Keys Generated with Physical Unclonable Functions
## 2 Lattice and Code-Based Post Quantum Cryptography
### NTRU Cryptography  
Cryptographic algorithms such as FALCON, which uses NTRU (_Nth_ degree of TRUncated polynomial ring) arithmetic, are also based on lattice cryptography. The parameters of the scheme include a large prime number N, a large number \(\mathbf{q}\) and a small number \(\mathbf{p}\) that are both used for modulo arithmetic. Two numbers \(\mathbf{df}\) and \(\mathbf{dg}\) are used to truncate the polynomials \(f_{(i)}\) and \(g_{(i)}\). The key generation cycle for client device (i), as shown in Figure 2, is the following:  
1. Generation of the two truncated polynomials \(f_{(i)}\) and \(g_{(i)}\) from seed \(a_{(i)}\) and seed \(b_{(i)}\).
2. Computation of \(Fq_{(i)}\), which is the inverse of polynomial \(f_{(i)}\) modulo \(\mathbf{q}\).
3. Computation of \(Fp_{(i)}\), which is the inverse of polynomial \(f_{(i)}\) modulo \(\mathbf{p}\).
4. Computation of polynomial \(h_{(i)}\): \(h_{(i)}\leftarrow\) p. \(Fq_{(i)}\cdot g_{(i)}\).
5. The private key \(Sk_{(i)}\) is \(f_{(i)}\cdot Fp_{(i)}\).
6. The public key \(Pk_{(i)}\) is \(h_{(i)}\).  
As the polynomials \(f_{(i)}\) and \(g_{(i)}\) are not always usable, they are subject to some preconditions such as invertible modulo \(p\) and \(q\). The client device needs to try several possible random numbers, and select the ones giving acceptable private keys. Once sufficient public and private keys are available, the encryption of the plaintext message \(m\), \(m\in\{-1,\,0,\,1\}^{\mathrm{N}}\) is done by finding the random polynomial \(r\), \(r\in\{-1,\,0,\,1\}^{\mathrm{N}}\), which uses a corresponding parameter \(d_{r}\), and calculating the ciphertext with the equation \(e\equiv r.h+m\) (mod \(q\)). To retrieve \(m\) from \(e\), we first calculate \(a\equiv f.e\pmod{q}\) and lift the coefficients of \(a\) to be between \(\pm q/2\). Then, \(a\) (mod \(p\)) is equal to m. [23].  
NTRU lattices can also be applied to DSA. This was originally introduced in NTRUSign, but NIST submissions such as Falcon expand on these algorithms [9]. Falcon utilizes the GPV framework applied to NTRU lattices; that is, the public key is a long basis for an NTRU lattice while the private key is a short basis. From here, the message m is sent a non-lattice point \(C\), utilizing a random value salt and hash function \(H\). Using the short basis, a user signs by finding the closest vector \(v\) to \(c\). The signature is (salt, \(s=c-v\)), verified by checking if \(s\) is short and \(H\) (msg \(||\) salt) \(-\) is a point on the lattice (verified using the long basis [24]).  
The cryptography based on NTRU is well known, and extremely well documented. The polynomial arithmetic truncating the N-th element is elegant and effective. Like other lattice algorithms under consideration by NIST, we are considering the NTRU as a strong candidate, both for DSA, and KEM.
# Optimization Methods of Compressively Sensed Image Reconstruction Based on Single-Pixel Imaging
## 6 Conclusions  
Based on the basic theory of compressive sensing, a single-pixel imaging system was built in our laboratory. We proposed the constraint condition of exponential decay to optimize the traditional wavelet decomposition basis, which effectively improved the sparse representation performance of discrete wavelet transform. At the same time, in order to facilitate DMD hardware implementation and engineering application of binary sensing matrix in our single-pixel imaging system, we proposed a new optimization algorithm based on the new mathematical model and the new initialization method to construct more optimized binary measurement matrices of better performance. Our methods not only effectively optimize the signal sparse decomposition and binary sensing matrices and improve the imaging methods of our single-pixel camera, but also greatly enhance image reconstruction accuracy and imaging quality. Our research may enlighten widely exploration in this direction of the field and have certain application value in expanding wider range researches. In the future works, we will make further efforts in the researches of scanning projection mode of DMD, signal sparsifying transform and signal reconstruction algorithms in compressive sensing and single-pixel imaging.  
[PERSON] made significant contributions to this study regarding conception, methodology and analysis, and writing the manuscript. [PERSON] contributed much to the research through his general guidance and advice, and optimized the paper. [PERSON] made research plans and managed the study project. [PERSON] gave lots of advice and help in conducting experiments. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the UCAS-Fraunhofer Joint Training Ph.D. Scholarship, and in part by the West Light Foundation for Innovative Talents, grant number: YA18K001.  
The authors are grateful to the Fraunhofer Institute for Computer Graphics Research IGD of Germany for full support.  
The authors declare that there are no conflicts of interests regarding the publication of this paper.
# Negotiation-Free Encryption for Securing Vehicular Unicasting Communication
## 4 Negotiation-Free Encryption Method
### Algorithm Implementation  
#### 4.2.1 Key Generation  
In VANET, the vehicle communications are confidential. Before passing the message, we must generate the encryption key to guarantee the security of the message. Therefore, the receiver is able to decrypt the encrypted data efficiently. Here, we use the receiver's state value \((Attr_{t_{i}})\) as the encryption key. To prevent a vehicle privacy leak, the vehicle cloud (VC) will hash the value before transmitting the state value and then sends the hash state value \((Hash_{Attr})\) back to the requesting vehicle. The key generation process is shown in Figure 2.  
1. \(V_{s}\) sends the request of Receiver's \(V_{r}\) status value to the \(VC\). We use public key of \(VC\) to encrypt \(Message_{req}\). The format of the request message is as follows: \[Ciphertext{(}V_{s}-to-VC\text{)}=Enc_{VC_{pk}}(ID_{s}+ID_{r})\] (1)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Definition** & **Description** \\ \hline \(ID_{s},ID_{r}\) & _Send message Vehicle ID_, \\  & _Receive message vehicles 1D_ \\ \(V_{s},V_{r}\) & _Send message Vehicle_, \\  & _Receive message vehicles_ \\ \(VC\) & _Vehicular Cloud_ \\ \(TS\) & _Timestamp_ \\ \((V_{s_{pk}},V_{s_{pk}}),(V_{r_{pk}},V_{r_{pk}})\) & _Sender\({}^{\prime}\)s Public and private keys_, \\  & _Receiver\({}^{\prime}\)s Public and private keys_ \\ \(VC_{pk},VC_{sk}\) & \(VC^{\prime}\)s _Public and private keys_ \\ \(Attr_{r_{i}}\) & _Receiver\({}^{\prime}\)s attribute i_ \\ \(Hash(Attr)\) & _The hash value of state property_ \\ \(En_{key}\) & _Use the key to encryption_ \\ \(Data\) & _Plaintext_ \\ \(key\) & _Sender used to encrypt data_ \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main symbol definition.  
Figure 2: Key Generation.  
2. The \(VC\) first decrypts the \(Ciphertext_{(V_{s}-to-VC)}\) with its own Secret Key. \[Message_{rec}=Dec_{VC_{sk}}(Ciphertext_{(V_{s}-to-VC)})\] (2) Then it find \(V_{r}\)'s real-time state \((Attr_{r_{1}},Attr_{r_{2}},Attr_{r_{3}},...)\) and hashes them. This hash value is the key which used to encrypt message when \(V_{s}\) transmits the data to \(V_{r}\). \[key=Hash(Attr_{r_{1}},Attr_{r_{2}},Attr_{r_{3}},...)\] (3)
3. The Cloud uses \(V_{s}\)'s public key to encrypt the \(key\). The encrypted data and the real time as time-stamp (\(TS\)) will be sent to \(V_{s}\). The format of the return message is as follows: \[Ciphertext_{(VC-to-V_{s})}=Enc_{V_{s_{pk}}}(ID_{s}+key+TS)\] (4)
4. \(V_{s}\) will use its own private key to decrypt the encrypted data and get the real key for encryption. \[key+TS=Dec_{V_{s,sk}}(Ciphertext_{(VC-to-V_{s})})\] (5)
# Adaptive Ransomware Detection Using Similarity-Preserving Hashing
## 3 Methodology
### Detection Mechanism  
In order to transform the hashing methods into a usable product, a comprehensive ransomware detection algorithm was developed around them. The algorithm is implemented in software and is run continuously to detect and provide alerts for potential ransomware attacks in a given directory. Let us elaborate; the algorithm is attached to a certain directory, and it proceeds to hash all files in all sub-directories recursively, comparing the hashes if previously hashed or storing them in cases of first discovery. If a change in hashes occurs, a counter is incremented, and if it exceeds a threshold, an alert is raised. However, if a full cycle through the tracked files takes place and the threshold is not exceeded, the changed files counter resets back to zero in order to reduce false positives and offer a more dynamic mechanism able to adapt to benign file changes. The full algorithm is described in Algorithm 1. Moreover, the detection algorithm, all three hashing techniques, and the custom ransomware were implemented as a Python module, which was written in a user-friendly and modular way to allow for extensions to any component, making it feasible to transform into a consumer product.  
Figure 3: SPH method.  
```
1:List of file partitions to track, Baseline hashes, Change threshold
2:\(total\_detected\gets 0\)
3:while True do
4:\(current\_hashes\leftarrow\) Hash all partitions
5:for each partition in tracked partitions do
6:\(changes\leftarrow\) Compare \(baseline[partition]\) with \(current\_hashes[partition]\)
7:\(total\_detected\gets total\_detected+changes\)
8:if\(total\_detected\geq threshold\)then
9: Raise alarm
10: Break
11:end if
12:endfor
13:\(total\_detected\gets 0\)
14:endwhile
```  
**Algorithm 1** Monitor Directory for File Change
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 6 Service-Oriented Middleware (SOM) Architectures Approaches for WSNs
### User  
In [45], the authors propose Ubiquitous SEvices on Mote (sensor) Environments (USEME), a new framework that uses Service-Oriented high-level programming models [45]. It also supports middleware development of Wireless Sensor and Actor Network (WSAN) applications [45]. Efficiency and scalability are realized through the middleware, which has various sensor nodes that can share a mutual behavior and control the use of services. The drawbacks of priority and deadline are consideredin this approach, which can deal with the real-time actions of the services requirements. This approach combines macro-programming with node-centric programming. Different prototypes are developed by using three motes: Crossbow family MicaZ motes, Imote2 (Crossbow Technology, Inc., Milpitas, CA, USA), and SunSPOT, as shown in Figure 5.  
The authors of [45] did not provide data on whether the architecture is a distributed or centralized model, or on the methods of used services. The proposed framework did not consider the accuracy and QoS constraints. The solution for this limitation is to provide an application designed to define a set of services, nodes, and events. This approach should be supported in real-time, which can allow the programmers to recognize (define) QoS among the services by using communication. The study in [46] uses the same techniques as above but focuses on middleware to support USEME. This Service-Oriented Framework is used to deploy lightweight services on the sensors and actors. Two different prototypes are used to implement this approach, which are SunSPOT devices and Imote2.Net from Crossbow. The middleware provides an easy way to address any differences in the nodes as they pertain to the dynamic and logical relationship between the services in the application. The features of this middleware make the network more secure, facilitate updates, and ensure controlled deployment.
# Electromagnetic Characteristics Analysis of a Tubular Moving Magnet Linear Generator System
## 3 Electromagnetic Analysis
### EMF Characteristics  
Considering that the linear generator system works in generating mode all the time during the stable running, its EMF characteristics are even more important than its force characteristics. Figure 7 shows the EMF characteristics of the linear generator simulated at the constant reciprocating motion speed of 1 m/s. The EMF coefficient fixed at 38 V/(m/s) during the middle 1/2 area of the stroke, and the EMF coefficient decreases linearly during the both ends of the stroke.  
The EMF characteristics can be explained by the flux linkage curve shown in the figure. When the current is 0 and the mover is in the midpoint of the stroke, the flux linkage in the coil of the linear generator is near to 0. The flux linkage changes linearly as the mover moves during the middle 1/2 area of the stroke, but the change rate decreases during both ends of the stroke. The EMF characteristics allows us to make full use of the motion to generate electricity.
## 7 IoMT Healthcare Services and Applications
### Personalized Healthcare  
Personalized healthcare involves the use of technology to individualize medical treatments for each patient [84; 85]. This is achieved by collecting data on a patient's medical history, lifestyle, and genetics and then using that data to create a customized treatment plan that is tailored to the specific needs of each patient. By utilizing this approach, healthcare professionals gain the ability to more accurately diagnose illnesses, identify potential risks, and design treatments that are tailored to the individual's unique needs. By implementing this personalized approach, healthcare professionals provide their patients with the most effective and individualized care possible, resulting in enhanced patient outcomes and satisfaction [86]. Thus, big data analytics play a crucial role in the implementation of individualized healthcare, both for individuals and populations [87; 88]. On the other hand, there are also some drawbacks to personalized healthcare. First, it can be expensive to implement, and second, it may lead to healthcare providers becoming too reliant on technology, which could ultimately compromise patient care.
## 4 Results
### Temporal Data Analysis  
In relation to the temporal distribution (by season) of the events for each of the basins (Mediterranean and Atlantic), the results are shown in Table 3. A difference can be seen between the distribution of events in the two basins. On the Atlantic coast, 71.5% of the events are concentrated during the winter months, while in the Mediterranean basin, the highest concentration of events (58%) occurs in late summer and autumn. It is worth noting that, during the last 5 years of the analysis, an increase of events during the winter has been detected in the Mediterranean basin.  
Figure 5: Impacts of Storm Gloria (January 2020) in Spain. Fatalities are located by provinces. Gloria set a milestone in the Mediterranean coast of Spain, breaking records of snow thickness, wave thickness and intense rainfall. Data from [36; 37].  
Figure 6 shows the evolution over time, for the period analysed, of the number of events recorded, fatalities and economic losses. Regarding the number of events, a slight increase has been observed over the last decade, with 2014 standing out with 20 significant events, followed by 2018 and 2019, with 13 and 12 events, respectively. In relation to fatalities and economic losses, Cyclone Xynthia in France (2010) represents a turning point in both parameters, with the year 2012 also standing out with 26 fatalities in only 4 events, all on the Andalusian Mediterranean coast (13 fatalities in the HLID of 28 September 2012; [38]). In the last years of the analysis (2018, 2019 and early 2020), there was also a slight increase in fatalities (57 victims) and economic costs, with 2019 standing out with almost 6563 M in economic losses.  
Finally, in relation to the causes of death, the main cause has been flood dragging and subsequent drowning (around 60% of the victims with data). The second cause has been in connection to falling trees and/or structural elements (walls, cornices, etc.). The rest of the victims have been due to rock falls and landslides, sea strikes and other, unidentified, causes.
# Optimizing Mobile Vision Transformers for Land Cover Classification
## 4 Results
(Class 12) increased from 48% to 74%, and Storage Tanks (Class 19) increased from 50% to 70%. Although the SWIN-Reg and SWIN-ShuffleNet models did not achieve the top accuracies, with SWIN-ShuffleNet reaching 58% on the 50-50 split, their designs highlight the trade-offs between accuracy and compactness of the model. Training loss outcomes can be seen in Figure 9.  
The results for the WHU-RS19 experiment group reveal that the MViT-Combined model on the 60-40 split was the highest-performing model, with a test accuracy of 86.32%. It outperformed the other splits using the same architecture. Moreover, these results were achieved with significant improvements in class-specific accuracies, with the highest overall accuracy and Kappa. The class-based performance for the 60-40 WHU-RS19 dataset is shown in Table 8. We also note that, like with AID and UC-Merced, using the MViT variants resulted in a significant accuracy increase for several classes, with MViT-Combined showing exceptional performance. In Classes 2 (Bridge), 5 (Farmland), 9 (Mountain), and 10 (Park), a comparison of the results between MViT and MViT-Combined shows increases from 80.77% to 90.48%, 72% to 85%, 56% to 90%, and 52% to 75%, respectively. The training and testing losses can be seen in Figure 6.0  
Figure 6.1 Training (**top row**) and testing (**bottom row**) losses for EuroSat datasets across multiple splits. Figure 6.2 Training (**top row**) and testing (**bottom row**) loss for AID datasets across multiple splits. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & **ViT** & **MViT** & **MViT-Depth** & **MViT-Combined** & **SWIN-Reg** & **SWIN-Conv** & **SWIN-ShuffleNet** \\ \hline  
Mean Acc & 61.08\% & 81.41\% & 80.71\% & 86.60\% & 51.22\% & 60.05\% & 61.45\% \\ \hline Overall Acc & 60.83\% & 81.31\% & 79.53\% & 86.32\% & 51.00\% & 59.95\% & 61.19\% \\ \hline Kappa & 0.5865 & 0.8027 & 0.7840 & 0.8556 & 0.4825 & 0.5769 & 0.5904 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracies of 60–40 split models on the WHU-RS19 dataset. The proposed model shows the highest overall accuracy (in red). Figure 9: Training (**top row**) and testing (**bottom row**) losses for UC-Merced dataset across multiple splits.