esa-sceva/satcom-chunk-collection huggingface dataset:
# A Next-Generation Core Network Architecture for Mobile Networks
## 6 Re-Direction
### Scenario with eNodeB
#### 6.1.1 EPS Bearer Update  
When using a legacy eNodeB, we need to act within the constraints of the current 3 GPP standards. In particular, the authors in [24] define how a UE registered with an MME can move to a different MME belonging to the same MME pool area, and load re-balancing between MMEs is performed.  
Regarding load balancing, a weight factor proportional to the probability of being selected by an eNodeB is associated with each MME. The higher the weight factor, the larger the number of UEs that will associate with that MME. In case the MME cannot handle any UE traffic, the weight factor is equal to zero. Each time an MME has to be removed from a MME pool area (i.e., for maintenance purpose), the connected UEs must be off-loaded to other MMEs of the same pool area. This is performed by initiating a _S1 Release_ procedure specifying \"load balancing with TAU required\". This procedure triggers a Tracking Area Update (TAU) at the UE side making the eNodeB select a new MME based on the weight factors of all MMEs in the pool. A new EPS bearer is then established between the UE and the new P-GW.  
In our scenario, in order to execute such a procedure, the MMEs of either SN and VCNs have to belong to the same pool. When a SN has to \"redirect\" a UE to a VCN, it starts a load re-balancing procedure, and the VCN sets the weight factor for its MME to a high value, while all other weight factors are kept unchanged. This weight factor is kept high until the UE is properly re-connected. For further improving the efficiency of this procedure, the SN may set a different MME pool for each VCN so that, at any time, in the pool, there are only the SN MME and one VCN MME. In this case, the SN MME would have to be part of multiple MME pool areas. The same procedure can be used for re-directing back the UE from the VCN MME to the SN MME.  
Figure 16: Bearers in an LTE network.
# Energy Harvesting Methods for Transmission Lines: A Comprehensive Review
## 2 Ultra-Low-Power Technology for Energy Harvesting Wireless Sensor Networks  
Although there is no exact definition of ultra-low-power (ULP) in the field of wireless sensors, it usually refers to designs that consume, on average, in the order of fractions of \(\mu\)W to fractions of mW. Most EH sources fall within the ULP category because they can generate as few as tens of \(\mu\)W [23]. Batteryless WSNs require the harvesting of energy from ambient sources in their application volume. However, due to the small volume of EH systems, very little ambient energy is available, requiring ULP circuits. In [24], it is stated that micro-power EH from indoor small-size solar cells, piezoelectric, thermoelectric (gradient temperature 1-10 \({}^{\circ}\)C), or RF Wi-Fi harvesters can scavenge power at ULP levels (some \(\mu\)W). However, power at \(\mu\)W level is not enough to supply IoT using commercial integrated circuits, so in addition to increasing the amount of energy harvested, reducing system power consumption to ULP levels is also required [24]. For example, according to [25], WSNs typically require 1-5 \(\mu\)W in standby mode, 0.5-1 mW in active mode, and 50 mW while transmitting. Similar values are provided in [21].  
ULP technology aims to extend the lifetime of WSNs through significant energy savings [26]. The basic diagram of a ULP WSN is the same as shown in Figure 2a. Since WSNs are constrained in terms of processing capacity, memory and energy, and power usage, energy management is one of the challenges that must be addressed for successful WSN deployment [27]. In particular, energy-efficient wireless communication modules play a critical role in developing battery-powered and batteryless WSNs. Different approaches can be applied to address these issues at both the physical and network levels without sacrificing important metrics, such as transmission range, latency, or immunity to interference [28]. By combining energy-efficient wireless protocols with low-power communication architectures, operation below \(10^{-4}\) W can be achieved [28]. Communication modules typically operate discontinuously at low duty cycles, hibernating between consecutive active states to save energy [19]. This strategy, based on periodically alternating between sleep and active modes, allows a large reduction in the average power of the power-hungry radio [29]. In [23], it is reported that by combining different techniques, such as short frame sizes, optional and synchronous receptions, asynchronous transmissions, and a light architecture, it is possible to reduce power consumption by 1 to 2 orders of magnitude compared to Bluetooth Low Energy (BLE).
## 1 Introduction
[4] revealed gas flow mechanisms in a shale matrix combined with the effects of surface diffusion and the thickness of the adsorbed gas layer; [PERSON] et al. [21] found that shale reservoirs are highly stress-dependent, which significantly affects gas transport mechanisms; and [PERSON] et al. [22] investigated the gas flow mechanisms in multiscale fractures in tight reservoirs while considering the coupled effects of adsorption/desorption and stress-sensitivity. Few previous studies have attempted to model all gas flow regimes in multiscale shale reservoirs while considering multiple effects as reservoir pressure changes [6]. For this reason, more research is needed to bridge this knowledge gap. From the stated above, it is obvious that gas flow in shale reservoirs is influenced by many factors. There is an insight into the coupled effects of multiscale pores, adsorption/desorption, and stress-sensitivity on gas flow in shale reservoirs need to be carried out. In this study, a dual fracture model will be adopted for describing gas flow in shale reservoirs. Gas flow in shale reservoirs will be divided into two sections: gas flow in shale matrix and fractures, respectively. The fractal theory will be used to obtain the pore size distribution of shale matrix, and the effect of multiscale pores on gas flow mechanism. Langmuir adsorption model will be applied for describing the surface diffusion in the shale matrix, and evaluating the influence of the adsorbed layer on effective pore diameter. Then the effect of adsorption/desorption on gas flow mechanism would be obtained. The effect of stress-sensitivity on gas flow in shale reservoirs will be also considered. Further, individual gas flow models for various gas flow regimes in shale matrix and fracture will be proposed, respectively. Finally, a comprehensive gas flow model for describing gas flow in shale reservoirs with the coupled effects of multiscale pores, adsorption/desorption, and stress-sensitivity under a dynamic change in reservoir condition will be obtained by combining with those individual gas flows models.
# Flying Watchdog-Based Guard Patrol with Check Point Data Verification
## 3 Proposed Flying Watchdog Architecture
### Data Communication via Air
The outcome of this  
Figure 6: Block diagram of laser data communication. calibration yields a discernible threshold value, and the associated algorithm is elucidated in Equation (6). \[\overline{\
abla}_{\text{si}}=\frac{\sum\text{X}_{\text{si}}(\text{i})}{\text{N}}= \frac{\sum((\text{a.t})+\text{b})}{\text{N}(\text{t})} \tag{6}\]  
\[\text{ADC}_{\text{si}}=\frac{1}{\text{V}_{\text{i}}}(2^{\alpha}.\overline{ \
abla}_{\text{si}}) \tag{7}\]  
Solar irradiance exhibits constant fluctuations, resulting in variations in the solar panel's input voltage \((\text{V}_{\text{si}})\). Equation (6) is a tool to compute the average input voltage from the solar panel (\(\overline{\
abla}_{\text{si}}\)), determined by the number of iterations or time-based iterations. Each input voltage measurement during an iteration \((\text{X}_{\text{si}})\) across the total iterations (N) contributes to calculating the average solar panel input voltage. Alternatively, one can compute the average value over a specific period, using N(t) to denote the number of iterations at a given time (t), where 't' is a continuous variable representing time. This time range typically spans from t = t1 to t = t2. In this context, 'a' symbolizes the rate of change, while 'b' represents the initial number of iterations at t = 0. The resultant average input voltage exists as an analog voltage value, which undergoes translation through an analog-to-digital converter. Equation (7) is employed to calculate this average input voltage. The onboard ADC microcontroller Arduino offers a 10-bit ADC value (\(\alpha\)), supplied with a 5-volt input voltage \((\text{V}_{\text{i}})\). It is important to note that fluctuations in the input voltage originating from the solar panel will impact the ADC value, introducing variability in the readings. \[\overline{\
abla}_{(\text{si}+\text{laser})}=\frac{\sum\text{X}_{(\text{si}+ \text{laser})}(\text{i})}{\text{N}}=\frac{\sum((\text{a.t})+\text{b})}{\text {N}(\text{t})} \tag{8}\]  
\[\text{ADC}_{(\text{si}+\text{laser})}=\frac{1}{\text{V}_{\text{i}}}.\Big{(}2 ^{\alpha}.\overline{\
abla}_{(\text{si}+\text{laser})}\Big{)} \tag{9}\]  
The laser beam received by the solar panel induces fluctuations in irradiance levels. To ensure precise readings, it is essential to calculate the average value based on the number of iterations or within a specified time frame, as outlined in Equation (8). Furthermore, the average change in the solar panel's input value impacts the ADC (Analog-to-Digital Converter) reading, which can be computed using Equation (9). The data communication protocol's validation phase entails retrieving data through a serial monitor interface. Moreover, the examination also considers the potential influence of ambient lighting and the spatial separation between components on the data retrieval process.
# A Global Overview of SVA--Spatial-Visual Ability
## 3 Constructs of Spatial-Visual Ability
Several studies of spatial ability have reached consensus on three subconstructs, irrespective of the multidimensional character of spatial ability, which can be categorised in a plethora of ways, though the nomenclature might vary slightly. The three subconstructs are mental rotation (MR), spatial-visualisation (SV), and spatial perception [5; 19]. Interior design and architecture are fundamentally problem-solving processes in one form or another. As such, spatial design necessitates the synthesis of spatial cognition with the design process involved in creative problem-solving. Furthermore, [PERSON] (1985) noted that the strategies required to solve spatial-visualisation problems may or may not include mental rotation and spatial perception; the former two involve \"single-solution strategies\", whereas spatial-visualisation allows for the possibility of \"multiple-solution strategies\". In lieu of developing a domain-specific (for interior design) tool for assessment of spatial ability with all-inclusive credibility, reliability, and environmental information, [PERSON] and [PERSON] [5] focused on two subconstructs--mental rotation (MR) and spatial-visualisation (SV), omitting spatial perception since it requires one to move and navigate using their body, making it difficult to identify [19]. They defined mental rotation (MR) as _\"the ability to mentally rotate 3D spatial forms and visualize them rapidly\"_. Spatial-visualisation was further expanded into three types on the basis of various visualisation modes frequently utilised in spatial-design representation and interpretation: (1) SV LA (2D 3D)--_\"the ability to read 2D information and convert it into 3D and find correct location for the correct viewpoint with respect to the orientation of the test takers' own bodies\"_; (2) SV I (2D to 3D)--_\"the ability to read 2D information (floor plan drawings) and expand it into volumetric forms, mentally trying out various possibilities\"_; and (3) SV II (3D to 2D)--_\"the ability to read volumetric information and translate it into 2D and find the correct location for the viewpoint by compressing complex volumetric information and converting it into 2D information\"_. These subconstructs were verified following extensive revisions between 2012 and 2021, with comprehensive findings over the years. For the purpose of this study, the above subconstructs as defined by [PERSON] and [PERSON] in multiple studies published by them [5; 13; 21; 22], were used as the foundational framework for further investigations.
## 4 Conclusions  
Using phase separation technology, accompanied with low reaction pressure in the liquid phase growth, Na\({}_{2}\)Ti\({}_{7}\)O\({}_{15}\) NWs were successfully prepared. The NTO NWs had good morphology, good dispersion and a large aspect ratio. The HRTEM analysis revealed that the NW structure had Na ion channels along the growth direction of (020), and the special tunnel structure will greatly help the diffusion of Na ions for high-performance SIBs. Further in situ TEM mechanical properties showed that the NTO NWs had a large elastic strain of over 2.56% and a relatively flexible Young's modulus of 26.5 GPa, which may be propitious to the advancement of the high reversible capacity and excellent stability of SIBs. Moreover, the as-prepared NTO NWs also showed good gas sensitivity. Our work not only suggests a potential anode material for high-performance SIBs, the capacity and stability of which are promoted by the stripe NTO NWs with an oriented tunnel structure and high mechanical stability, but also presents a good gas sensing material that can be used in flexible wearable devices.  
Conceptualization, L.-Y.L.; methodology, L.-Y.L. and K.W.; formal analysis, L.-Y.L. and Y.D.; investigation, M.-L.S.; data curation, L.-Y.L., Y.D., B.Z., N.-N.J. and Z.-H.Z.; writing--original draft preparation, L.-Y.L.; writing--review and editing, L.-Y.L. and M.-L.S.; visualization, L.-Y.L.; supervision, M.-L.S.; project administration, M.-L.S.; funding acquisition, M.-L.S.  
Figure 5: Self-assembly gas-sensitive testing system. (**a**) Schematic model; (**b**) Testing chamber of the gas sensor; (**c**) Circuit diagram of the gas-sensitive test.  
Figure 6: Gas sensitivity of the NTO NWs at 500 ppm and 350 \({}^{\circ}\)C by different gas. (**a**) NH\({}_{3}\); (**b**) NO.  
**Funding:** This work is supported by the National Natural Science Foundation of China (Grant Nos. 51621003, 11374028, 11774017 and U1330112), the National Key Research and Development Program of China (Grant No. 2016 YFB0700700), and the Scientific Research Key Program of Beijing Municipal Commission of Education (KZ201310005002).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
on sensors to control temperature and air quality within barns. In this case, a cyber-attack could cause the mortality of thousands of birds. Similar sensitivities are in greenhouse growing conditions where a breach in cyber-security could result in a devastating amount of profit for the farming operation. [2]. Security is a highly-challenging aspect of SF [2; 32; 33] and PA [34; 35; 36] due to different properties of IoT devices including heterogeneity, mobility, and resource limitations. This exposes SF and PA to a broad range of cyber-threats. Although the literature comes with several research reports focusing on security-related issues in SF and PA, to the best of our knowledge, there is no systematic review on these issues. A survey in this area, along with a future road map, can improve public awareness and pave the way for further research. In this paper, we first discuss security aspects of SF and PA. Next, we review state-of-the-art attacks on SF and PA, and study the security aspect(s) violated by each of them. In reviewing literature for examples of specific attacks on SF and PA, we identify the general security violations of each. In many cases, these attacks are on IoT technologies that support SF and PA functions thus countermeasures suggested here focus on these identified threats, rather than on novel SF/PA specific mitigation techniques. According to our studies on security aspects and attacks, we present a taxonomy on cyber-attacks to SF and PA. Our taxonomy is based on the relation between attacks and the stages of Cyber-Kill Chain, which is a methodology for analyzing the chronology of complex cyber-attacks. As a chosen class of threats for further study, we discuss the anatomy as well as the behavioral characteristics of Advanced Persistent Threats (APTs). Furthermore, we review risk mitigation strategies and countermeasures against cyber-threats to SF and PA. On the basis of the above discussions, we suggest some topics for future research on security of SF and PA. The main contributions of this paper can be explained as follows. * We itemize the security aspects of SF and PA and establish a map between attacks and security aspects. * We use CKC [37] for the first time to present a systematic taxonomy on cyber-threats to SF and PA.
* We study the anatomy as well as the behavioral characteristics of APT. * Finally, we develop a future road map to highlight some related emerging areas that still need to be studied in future research. The rest of this paper is organized as follows: Section 2 presents a review on existing reviews to highlight our motivations for the work of this paper. We examine cyber-attacks on SF and PA in Section 3. CKC-Based taxonomy on security threats to SF and PA are presented in Section 4. Moreover, APTs are specifically studied in more detail in Section 5. We study threat mitigation strategies and countermeasures in Section 6. Section 7 presents the future road map and; Section 8 concludes the paper.
## 2 Methods of Calculation
This equation is solved as  
\[\mathbf{u_{T}}(\xi,\omega)=\mathbf{K_{TS}}^{-1}(\xi,\omega)\mathbf{F_{T}}( \omega)=\mathbf{N_{TS}}(\xi,\omega)\mathbf{F_{T}}(\omega) \tag{11}\]  
for the displacements \(\mathbf{u_{T}}(\xi,\omega)\), and the force on the soil \(F_{S}(\xi,\omega)\) can be calculated as  
\[F_{S}(\xi,\omega)=K_{S}(\xi,\omega)u_{1}(\xi,\omega) \tag{12}\]  
The soil force distribution along the track (as well as any displacement distribution) can then be achieved by the Fourier integral  
\[F_{S}{}^{\prime}(y,\omega)=\frac{1}{2\pi}\int_{-\infty}^{+\infty}F_{S}(\xi, \omega)\mathrm{exp}(i\xi y)d\xi \tag{13}\]  
Figure 1: The vehicle–track–soil model consisting of the wheelset, rails, rail pads, sleepers, under-sleeper pads, the ballast, an under-ballast mat, an under-ballast plate, and the soil. The total force that acts on the track-soil interface is calculated as the integral over the infinite track length  
\[F_{S}(\omega)=\int_{-\infty}^{+\infty}{F_{S}}^{\prime}(y,\omega)dy={F_{S}}^{ \prime}(\xi=0,\omega)=F_{T}(\omega)H_{TS}(\omega) \tag{14}\]  
This soil force can easily be obtained as the transformed integrand at \(\xi=0\) without any integration. The total force transfer \(H_{TS}(\omega)=F_{S}/F_{T}\) of the track is one part of the dynamic mitigation effect. The displacements of the track under the axle load can be calculated by the Fourier transformation of the first element of the inverse stiffness matrix \(\mathbf{N_{TS}}\) as  
\[u_{T}(\omega)=\frac{F_{T}(\omega)}{2\pi}\int_{-\infty}^{+\infty}{N_{\text{TS, 11}}(\xi,\omega)d\xi}=\frac{F_{T}(\omega)}{K_{T}(\omega)} \tag{15}\]  
where the dynamic stiffness \(K_{T}(\omega)\) of the track has been introduced. The track model is combined with a vehicle model. A single rigid wheel mass \(\text{m}_{\text{W}}\) is used throughout this paper. The dynamic stiffness \(K_{V}(\omega)=-m_{\text{W}}\omega^{2}\) of the vehicle is introduced into the vehicle-track interaction analysis with the unsprung or wheelset mass \(m_{\text{W}}\). A force \(F_{V}\) acting on the vehicle yields a force \(F_{T}\) acting on the track giving the vehicle-track force transfer  
\[H_{VT}(\omega)=\frac{F_{T}}{F_{V}}(\omega)=\frac{K_{T}(\omega)}{K_{T}(\omega)+ K_{V}(\omega)} \tag{16}\]  
which is the second part of the dynamic mitigation effect. The total force transfer \(H_{VS}(\omega)\) is the product of the force transfer \(H_{TS}(\omega)\) of the track-soil system and the force transfer \(H_{VT}(\omega)\) of the vehicle-track interaction  
\[H_{VS}(\omega)=\frac{F_{S}}{F_{V}}(\omega)=\frac{F_{T}}{F_{V}}(\omega)\frac{F_ {S}}{F_{T}}(\omega)=\frac{K_{T}(\omega)}{K_{T}(\omega)+K_{V}(\omega)}\frac{F_{ S}}{F_{T}}(\omega)=H_{VT}(\omega)H_{TS}(\omega). \tag{17}\]  
The \"static\" mitigation effect is calculated with the force distribution on the soil in Equation (13) by transforming it in a force distribution in time and, finally, in the corresponding Fourier spectrum where the latter are dependent on the train speed.
# Cooperative Transmission Mechanism Based on Revenue Learning for Vehicular Networks
## 3 Joint Cell Anti-Jamming Map Based on Dual Connection
### Maximum Clique Search Algorithm for Inter-Cell Interference Graph  
In Figure 3, the inter-cell interference graph constructed by the honeycomb structure is represented by a planar graph. We define the maximal clique here as the corresponding correlation graph if it is not contained by any other clique, i.e., it is not a proper subset of any other clique [12]. Because of its honeycomb structure, the size of the node groups connected pairwise in this kind of graph is at most 3. That is, the size of the maximal clique is at most 3 and each mesh represents a maximum clique. In other words, the corresponding maximal clique can be obtained by searching all the mesh in the graph.  
When searching the maximum clique of the graph, which means searching the mesh, we first need to obtain the position coordinates of all the base stations and construct the interference graph _G(V,E)_. In graph_G_, node set \(V\) is composed of each base station, while edge set \(E\) is composed by connecting adjacent nodes with interference relations. Then, the above nodes are selected one by one as the origin of the plane rectangular coordinate system to form the plane rectangular coordinate system, and all nodes connected with the origin are added to node set _Cvi_. By calculating the anti-clockwise angle between the nodes  
Figure 4: Cell cluster scheduling algorithm conflict graph.  
Figure 3: Intercell interference graph clustering.  
in _Cvi_ and the horizontal coordinate axis, the corresponding included angle set _Avn_ can be obtained. In this way, we can derive _Con(Cv1, Cv2... The Cvn)_ and _Ang(Av1, Av2,...,Avn)_. By sorting _Con_based on _Ang_, we can obtain all maximal cliques of the interference graph. The process of the algorithm is detailed by Algorithm 1.  
```
1:Input:\(G(Vn,Em)\), \(Con(C_{V1},\)\(C_{V2},...,C_{Vn})\), \(Ang(A_{V1},\)\(A_{V2},\)\(...,A_{Vn})\)
2:for\(i=1\) to n do
3:\(K=\text{length}(C_{Vi})\)
4:while\(K\geq 1\)do
5:for\(j=1\) to \(K\)do
6:if\(A_{Vi}\) (\(j\))\(>\)\(A_{Vi}\) (\(j+1\)) then
7:temp = \(A_{W}\) (\(j+1\))
8:\(A_{Vi}\) (\(j+1\)) = \(A_{Vi}\) (\(j\))
9:\(A_{Vi}\) (\(j\)) = temp
10:temp = \(C_{Wi}\) (\(j+1\))
11:\(C_{Vi}\) (\(j+1\)) = \(C_{W}\) (\(j\))
12:\(C_{Vi}\) (\(j\)) = temp
13:endif
14:endfor
15:\(K=K-1\)
16:endwhile
17:endfor
18:for\(i=1\) to \(n\)do
19:for\(j=1\) to \(\text{length}(C_{Vi})\)do
20: Put \(\{V_{i},V_{j},V_{j+1}\}\) into _Ci_
21:endfor
22:endfor
23:Delete the repeating items of the _Ci_
24:Output: _Cti_
```  
**Algorithm 1** Cluster Searching Algorithm for Interference Graph