esa-sceva/satcom-chunk-collection huggingface dataset:
# The Principle and Downhole Testing of Water Injection Exploration in Depleted Reservoirs
## 1 Introduction
### Analysis and Discussion of Water Injection Results
#### 3.4.4 The Application Significance  
Through the water injection experiments, further confirmation has been provided regarding the phenomena of disturbance damage and stress reduction near the depleted zones (old tunnels). These phenomena leads to the observed decrease in water pressure and increase in permeability during the water injection process. Conversely, these phenomena can also be utilized to investigate disturbance damage and stress reduction, thereby facilitating the exploration of depleted zones and old water zones. The water injection experiments also demonstrate that the decrease in pressure and increase in permeability during the injection process are significant (e.g., pressure dropping from 10 MPa to 5 MPa), making them easily observable and of practical significance. This also indicates that the method proposed by the author and their team, based on the principle of stress seepage, for the advanced exploration of depleted zones through water injection, is feasible.
## 3 Proposed LSTM-GA and Optimized LSTM-GA Approach
\[t\geq T_{\max}\] (6) Alternatively, other termination conditions could be implemented based on a lack of improvement over successive generations. In a way, this guarantees that the algorithm converges to an optimal or near-optimal LSTM configuration for detecting GPS spoofing in UAVs. The pseudo-code of the proposed algorithm is provided in Algorithm 1. ```
0: Training dataset \(D\), population size \(P\), maximum generations \(G\)
0: Optimized LSTM model \(M^{*}\)
1:Data Pre-processing
2:\(D_{processed}\leftarrow\text{ScaleFeatures}(D)\)
3:\(D_{train},D_{test}\leftarrow\text{SplitDataset}(D_{processed})\)
4://Initial LSTM Model
5:\(M_{base}\leftarrow\text{BuildLSTM}(\text{default\_hyperparameters})\)
6:\(\text{Train}(M_{base},D_{train})\)
7:\(\text{base\_accuracy}\leftarrow\text{Evaluate}(M_{base},D_{test})\)
8://GA Initialization
9:population \(\leftarrow\) InitializePopulation\((P)\)
10:best_model \(\leftarrow\) null
11:best_fitness \(\leftarrow\) 0
12://GA Main Loop
13:for\(g=1\) to \(G\)do
14:for each individual \(i\) in population do
15:params \(\leftarrow\) DecodeHyperparameters\((i)\)
16:\(M_{i}\leftarrow\text{BuildLSTM}(\text{params})\)
17:\(\text{fitness}_{i}\leftarrow\text{EvaluateFitness}(M_{i},D_{train},D_{test})\)
18:if fitness\({}_{i}>\text{best\_fitness}\)then
19:best_model \(\leftarrow\)\(M_{i}\)
20:best_fitness \(\leftarrow\) fitness\({}_{i}\)
21:endif
22:endfor
23:selected \(\leftarrow\) SelectParents(population)
24:population \(\leftarrow\) Crossover(selected)
25:population \(\leftarrow\) Mutation(population)
26:endfor
27:return best_model
```  
**Algorithm 1** LSTM-GA hyperparameter optimization  
Figure 4 provides a high-level overview of the proposed framework, from data pre-processing and hyperparameter optimization to final GPS spoofing classification. On the left, raw UAV flight data and GPS signals enter a feature-extraction process, which then feeds relevant metrics (e.g., Doppler shifts, carrier-to-noise ratios) into the Genetic Algorithm's hyperparameter search. The GA iteratively refines candidate configurations for the LSTM network, optimizing parameters such as layer sizes and dropout rates. After convergence, the Optimized LSTM model is tasked with classifying spoofed versus authentic signals, delivering a robust defense against a range of potential spoofing attacks. This architecture underscores the synergy between genetic search methods and deep sequence models for enhanced detection accuracy and adaptability.
# An Optimized Hierarchical Cluster Formation Approach for Management of Smart Cities
###### Abstract
The third task is the selection task that occurs during every succeeding generation, a new generation is obtained through accepting members on the basis of their fitness. The solutions with higher fitness have a greater chance of being selected, this operation leads to better adoption of the best solution. The communication cost is the major energy consumer in nodes [15]. While the sensing and computational costs are fixed for each node, the communication cost is affected by many factors. These factors have many forms such as the number of transmitted data packets (network traffic load), the distance of transmission, and the bandwidth of the transmission link [16; 17]. Therefore, an effective energy optimization technique is always needed to minimize the energy consumption in the communication process in WSNs. To overcome the previous problems of LEACH, the present research proposes two improvements in the cluster formation phase, where SNs select their CHs. The first improvement is aimed at selecting the optimal CH of each SN based on many factors. The adequacy of the remaining energy compared to the transmission energy cost is the decisive factor in selecting relevant CHs. The present work also addresses the problem of selecting CHs based on only distance, which may cause dissipation of energy among SNs. The target of the second improvement is to optimize the cluster size to conserve the power of the CHs. The optimal cluster size is defined as the size by which the data are routed from the SN to the CH and subsequently to the BS using the minimal transmission energy that can keep the CH alive [18; 19]. In fact, identifying these factors for CH selection to minimize the consumed energy is one novelty of ICSI. Also, employing these factors in an optimized approach is another novelty of the proposed approach. Therefore, the significant contribution of this work is to introduce an intelligent cluster selection approach for IoT (ICSI) as an optimized version of the cluster formation step of LEACH. ICSI acts to diminish the power consumption and extend the WSN durability. The chief objectives of the new optimal model are listed as follows:  
1. To select the optimal CHs for each node and allocate the node to the cluster only if it meets the power requirements. 2. To optimize the cluster size by allocating the node to the cluster with the condition that it has an optimal node power factor (NPF) that can keep the CH alive. The following list summarizes the abbreviations that are used in this work.
# A Source Anonymity-Based Lightweight Secure AODV Protocol for Fog-Based MANET
## 3 Preliminary Knowledge and Analysis
### Secure AODV
#### 3.2.2 SAODV Digital Signature  
SAODV routing messages (\(RREQ\), \(RREP\), \(RERR\)) are digitally signed, in order to guarantee their integrity and authenticity. Except for the hop count and the hash fields, other fields in the message should not be changed in the transfer process. The node generates a routing message and signs the changeless part with its private key. The node receives this message and verifies the signature with the sender's public key. The type of key and sign are defined as natural numbers. The signature function and the validation function are expressed as a crypt and decrypt, respectively.  
[PERSON] and [PERSON] proposed and implemented an SAODV scheme, called A-SAODV (Adaptive SAODV), based on an adaptive scheme, and optimized the performance. This protocol is intended for multi-threads, including two execution threads. One is dedicated to cryptographic operations, avoiding the blocking of other message processing. The other is used to complete all of the other functions, such as routing message processing, SAOD routing table management, timeout management, SAOVD packet generation, and packet forwarding.  
A-SAODV adopts the adaptive reply decision scheme to optimize the SAODV double signature feature. The intermediate node can determine whether to respond to the \(RREQ\) message according to its own load state, and it won't reply to \(RREP\) when it is overburdened with dealing with the routing packet signature or authentication.  
A-SAODV adopts the adaptive reply decision scheme to optimize the SAODV double signature feature. The intermediate node can determine whether to respond to the _RREQ_ message according to its own load state, and it won't reply to _RREP_ when it is overburdened with dealing with the routing packet signature or authentication. A-SAODV also optimizes some other processes, except when adopting the adaptive reply decision scheme. For example, it uses a cache to store the new routing packets that have been signed and authenticated, adapts key ring management, and so on. All these optimizations have played a positive role in improving SAODV performance. However, there are some shortcomings in avoiding the routing group flooding, and reducing the node calculation and signature time.
# Stability of MSW Landfill Slopes Reinforced with Geogrids
## 3 Results and Discussion
### Reinforced Analyses
#### 3.2.3 Effect of Internal Friction Angle on the Reinforced Slope  
FEM analyses were carried out for different \(\phi\) values of the MSW landfill to investigate the effect of \(\phi\) on slope stability. In the analyses, the other parameters were chosen as c = 10 kPa, \(\psi\) = 0\({}^{\circ}\), E = 6500 kN/m\({}^{2}\), \(\
u\) = 0.45, \(\gamma\) = 9 kN/m\({}^{3}\), \(\beta\) = 26.56\({}^{\circ}\) (1V/2H), and N = 4.  
Table 11 and Figure 17 show the FS values for different internal friction angle values obtained from the FEM analyses.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**Cohesion, c (kPa)**} & \multicolumn{2}{c}{**Factor of Safety (FS)**} \\ \cline{2-3}  & \multicolumn{2}{c}{**FEM**} \\ \hline
5 & 1.33 \\
10 & 1.84 \\
15 & 2.32 \\
20 & 2.78 \\
25 & 3.22 \\
30 & 3.65 \\
35 & 4.09 \\
40 & 4.53 \\
45 & 4.96 \\
50 & 5.38 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Analysis results for different cohesion values (N = 4).  
Figure 16: Variation in FS with c for the reinforced slope.  
From the analyses performed for investigating the effect of \(\phi\) on the reinforced slope stability, it is clear that the FS of the reinforced slope increases approximately linearly with the increase in the \(\phi\) value, similar to the cohesion effect.  
Depending on the increase in the \(\phi\) and c values of the MSW landfill, the shear stresses, displacement, and strain values occurring in the slope decrease, and the FS of the slope increases. In addition, with the increase in the shear strength parameters, the interaction between the geogrid reinforcements and the MSW landfill material increases, which allows for higher FS values.
# A Survey of Candidate Waveforms for beyond 5G Systems
## 2 Waveform Key Performance Indicators for 5G and beyond Communications
It mainly depends on the the number of operations required at the transmitter or receiver, which may include windowing, filtering operations, as well as interference cancellation algorithms. The overall system complexity will influence the cost and the energy efficiency of the system and can represent a bottleneck upon selecting and determining the most suitable waveform candidate to be implemented for a certain type of applications [4]. 8. High flexibility, reliability and MIMO friendless: The ideal waveform should also be able to support the coexistence of different numerologies and multi-numerology to enable several services, while allowing dynamic allocation of bandwidth for these (numerologies/services) [9]. An extremely high reliability is also necessary. This means that the evaluated BER performances for the chosen waveform should be better, or at least, similar to previous standard waveforms. The new waveform should support and be extended to MIMO (especially massive-MIMO), without requiring much additional effort. 9. Filtering/Windowing: The waveform should allow a filtering and/or windowing operation to be performed in both the transmission and reception stages, in order to manage the OOB emissions and latency. On the one hand, a wide filter bandwidth, which results in shorter filter length (in time domain), can control the system latency. However they are not very efficient at lowering the OOB emissions. On the other hand, a narrow filter implies very low OOB emissions but results in a long filter length (in time domain) which increases the system latency. Hence, there must be a trade-off between low OOB emissions and low latency [6].
# Secure Utilization of Beacons and UAVs in Emergency Response Systems for Building Fire Hazard
## 6 Discussion
### Privacy Considerations  
Recently, many studies related to location privacy have gained much attention, because users may want to use location-based services without revealing their location. Location privacy in wireless sensor networks including IoT environments may be classified into content privacy and contextual (e.g., the traffic) privacy. Several approaches for protecting content-oriented privacy have been designed in location tracking systems, which can decide the users' positions for location-based services [38; 39; 40]. [PERSON] et al. [38] utilize a location broker residing at the middleware layer to hide users' positions. [PERSON] et al. [39] agitate the sensed location information by using the k-anonymity criterion. [PERSON] et al. [40] presented the location privacy protection method through a mist router and a handle-based virtual circuit routing protocol. In addition, to protect the receiver's (i.e., the sink) location, privacy is important, because the receiver collects data from all sensors. [PERSON] et al. [41] first introduced the issue of how to hide the location of the sink (e.g., the base  
Figure 12: Accuracy of proximity calculations. The interval between smart sensor and beacon is (**a**) 3 m, (**b**) 4 m, (**c**) 5 m, (**d**) 3–5 m.  
station) and presented techniques of multi-path routing and fake message injection. [PERSON] et al. [42] proposed a location-privacy routing protocol incorporating fake packet injection.  
In our UAV-EMOR system, the beacon sensors are used to implement rescue and evacuation functionality. Using Bluetooth technology, such beacons constantly transmit the unique ID and real-time location of the occupants to all smart sensors and indoor-UAVs. Since the building management system can guide the occupants to the evacuation route based on these real-time location data and unique ID, it is useful for evacuating occupants in an emergency situation. The control server of the UAV-EMOR system allows smart sensors and indoor UAVs to enable Bluetooth and receive beacon signals from users only when the emergency situation happens. The system may be connected at all times, but the control server does not collect or store the users' location information at normal times because it has no need to ask for the information. Still, the use of location information can introduce potentially unresolved privacy concerns [43]. Therefore, we need to consider the location privacy issue; simply put, we can make the security policy for our UAV-EMOR to update the beacon ID periodically. To more actively protect the location privacy, we can apply the above mentioned studies about location privacy protection in wireless sensor networks to the UAV-EMOR system.
# Automatic Vertical Parking Reference Trajectory Based on Improved Immune Shark Smell Optimization
## 3 Improved Shark Optimization Algorithm  
### Shark Optimization Algorithm  
The shark optimization algorithm is an intelligent optimization algorithm, proposed by [PERSON] et al. in 2014, to simulate shark foraging behavior. It has good global convergence performance [18].  
In the shark population, every shark at any location moves at a speed toward the location with the stronger odor, as follows:  
\[\left[\begin{array}{cccc}V_{1,1}^{1}&V_{2,1}^{1}&\cdots,V_{i,1}^{1},\cdots&V_ {NP,1}^{1}\\ V_{1,2}^{1}&V_{2,2}^{1}&\cdots,V_{i,2}^{1},\cdots&V_{NP,2}^{1}\\ \vdots&\vdots&\vdots&\vdots\\ V_{1,ND}^{1}&V_{1,ND}^{1}&\cdots,V_{i,ND}^{1},\cdots&V_{NP,ND}^{1}\end{array} \right], \tag{12}\]  
where \(V_{ij}^{k}\) is the \(j\)-th dimensional speed for the \(i\)-th shark at the \(k\)-th iteration, \(i=(1,2\ldots NP)\), \(j=(1,2\ldots ND)\), \(k=(1,2\ldots k_{\max})\), \(ND\) is the dimension of the decision variables, \(NP\) is the shark population scale, and \(k_{\max}\) is the maximum number of iterations.  
When sharks swim, there is inertia, and their speed is bounded; so, the speed of every dimension is  
\[\left|V_{i,j}^{k}\right|=\min((\eta_{k}\cdot R1\cdot\frac{\partial(OF)}{ \partial x_{j}}\Bigg{|}_{x_{i,j}^{k}}+\alpha_{k}\cdot R2\cdot V_{i,j}^{k-1}), \left|\beta_{k}\cdot V_{i,j}^{k-1}\right|), \tag{13}\]  
where \(x_{i,j}^{k}\) denotes the \(j\)-th dimensional location of the \(i\)-th shark at the \(k\)-th iteration; \(OF\) denotes the objective function; \(\eta_{k}\in[0,1]\) denotes the gradient coefficient; \(\alpha_{k}\in[0,1]\) denotes the weight coefficient; \(R1\in[0,1]\) and \(R2\in[0,1]\) are random numbers in the iterative calculation process; \(\beta_{k}\) denotes the speed limit rate of the \(k\)-th generation.  
The shark updates its location through forward movement. The location \(Y_{i}^{k+1}\) of the forward movement is updated as follows:  
\[Y_{i}^{k+1}=X_{i}^{k}+V_{i}^{k}\cdot\Delta t_{k}, \tag{14}\]  
where \(X_{i}^{k}\) is the original location of the \(i\)-th shark of the \(k\)-th generation, and \(\Delta t_{k}\) represents the time interval of the -th generation.  
In addition to forward movement, sharks also make rotational movements within a small range along the search path. The location \(Z_{i}^{k+1,m}\) of the rotational motion is updated as  
\[Z_{i}^{k+1,m}=Y_{i}^{k+1}+R3\cdot Y_{i}^{k+1}, \tag{15}\]  
where \(m\) denotes the number of local searches for individual sharks, \(m=(1,2\ldots M)\), \(M\) denotes the number of rotational movements, and \(R3\in[0,1]\) denotes the random number in the iterative calculation process.  
If the shark finds a stronger smell location in its rotary motion, it will continue the search toward that location. The formula for updating the location of the continued search is  
\[X_{i}^{k+1}=\text{arg }\max\Big{\{}OF(Y_{i}^{k+1}),OF(Z_{i}^{k+1,1})\cdots OF(Z_{ i}^{k+1,M})\Big{\}}. \tag{16}\]  
The shark updates its location by moving forward and rotating to obtain the best possible solution [19].
## 3 Results and Discussion
### FTIR Analysis
The bands that arise in the 900-800 cm\({}^{-1}\) spectral area are indicative of the prevalence of saccharides, and they are attributed to glycosidic bonds [74; 75]. These bands are accompanied by the appearance of the bands at 1450-1400 cm\({}^{-1}\) in the most affected cultures, such as Cu 10 ppm, Ni 5 and 10 ppm, and Mix 10 ppm, and they are again characteristic of saccharides [73; 74]. This behavior was also reported regarding _A. platensis_ cultures treated with HCI, presenting pH 14 [76], with the Cu-treated cultures [45], and also by polysaccharides extracted from _A. platensis_[71]. They are produced and accumulate on the cell wall to suppress the toxic effects from the metals as a cell defense mechanism. The metal ions are accumulated onto the cell walls by electrostatic interaction. This procedure is considered very rapid, and this step is followed by the transportation of the metals into the cytoplasm [1]. The accumulation of saccharides on the  
Figure 7: ATR-FTIR spectra of _A. platensis_ biomass collected from all cultures on Day 7 of cultivation: (**a**) Cu-MT; (**b**) Cd-MT; (**c**) Ni-MT; (**d**) Pb-MT; (**e**) Zn-MT; (**f**) Multi-MT. Control culture spectrum is also shown for comparative reasons. cell walls is supported by the FPA analysis performed on the Control sample (Figure 8); the FPA chemical images of a single filament representing the spatial distribution of the bands at 1643 (C=O of amide I), 1728 (C=O of lipids), and 834 cm\({}^{-1}\) (of saccharides) are presented. This confirms that the proteins are in the cell interior, while both the saccharide and lipid contents are mostly located in the cell walls. It should be mentioned that polysaccharides extracted from _A. platensis_ have grown interest because of their biological properties against toxicity [75]. This cell defense mechanism--i.e., the accumulation of saccharides in the cell membrane--is further supported by the behavior of the Control sample on Day 0 of cultivation, as presented in Figure 6, where the emergence of the band at 837 cm\({}^{-1}\) is noted, indicating that the microcalga is stressed due to its adaption to the new environment caused by the addition of new cultivation medium. Finally, all Pb-treated cultures, and the Cd 1 ppm culture seem to be the least affected, after the cultivation period.
## Appendix B Spearman Correlation Values, Oxford Government Responses \begin{tabular}{l l l l} \hline \hline
**(a) New Cases Endpoint.** & & & \\ \hline \multirow{2}{*}{**Oxford Government Response**} & \multicolumn{3}{c}{**Correlations with New Cases**} \\ \cline{2-4}  & **First Wave** & **Second Wave** & **Third Wave** \\ \hline School closing & \(2.717\) & \(0.152\) & \(1.020\) \\ France & \(0.564\) & \(0.307\) & \(-0.447\) \\ Germany & \(0.637\) & \(0.130\) & \\ Italy & \(0.355\) & \(-0.413\) & \(0.462\) \\ United Kingdom & \(0.628\) & \(0.128\) & \(0.495\) \\ USA & \(0.533\) & & \(0.510\) \\ \hline Workplaces closing & \(3.251\) & \(1.509\) & \(0.462\) \\ France & \(0.658\) & \(0.664\) & \(0.381\) \\ Germany & \(0.382\) & \(0.343\) & \(-0.393\) \\ Italy & \(0.580\) & \(0.624\) & \(0.507\) \\ United Kingdom & \(0.903\) & \(0.228\) & \(-0.175\) \\ USA & \(0.728\) & \(-0.350\) & \(0.142\) \\ \hline \multirow{2}{*}{Cancel public events} & \(2.556\) & \(0.436\) & \(0.234\) \\ France & \(0.323\) & \(0.436\) & \\ Germany & \(0.601\) & & \\ Italy & \(0.358\) & & \\ United Kingdom & \(0.629\) & & \\ USA & \(0.648\) & & \(0.234\) \\ \hline \multirow{2}{*}{Restrictions on gatherings} & \(2.995\) & \(1.408\) & \(0.432\) \\ France & \(0.461\) & & \\ Germany & \(0.486\) & \(0.381\) & \\ Italy & \(0.678\) & \(0.843\) & \\ United Kingdom & \(0.596\) & & \(0.006\) \\ USA & \(0.774\) & & \(0.426\) \\ \hline \multirow{2}{*}{Public transport closing} & \(2.538\) & \(1.286\) & \\ France & \(0.437\) & & \\ Germany & \(0.511\) & & \\ Italy & \(0.758\) & \(0.775\) & \\ United Kingdom & \(0.629\) & & \\ USA & \(0.714\) & & \\ \hline \multirow{2}{*}{Stay at home requirements} & \multirow{2}{*}{\(3.658\)} & \(2.462\) & \(0.398\) \\ France & \(0.604\) & \(0.646\) & \\ Italy & \(0.769\) & \(0.678\) & \\ United Kingdom & \(0.711\) & \(0.815\) & \\ USA & \(0.878\) & \(0.402\) & \(-0.168\) \\ \multicolumn{2}{l}{} & \(0.696\) & \(-0.079\) & \(0.566\) \\ \hline \multirow{2}{*}{Restriction on internal movement} & \multirow{2}{*}{\(3.125\)} & \(0.445\) & \(-0.049\) \\ France & \(0.506\) & \(0.132\) & \(0.187\) \\ Italy & \(0.461\) & \(0.068\) & \(-0.407\) \\ United Kingdom & \(0.702\) & \(0.815\) & \\ USA & \(0.725\) & \(-0.057\) & \\ \multicolumn{2}{l}{} & \(0.731\) & & \(0.171\) \\ \hline \hline \multicolumn{2}{l}{International travel control} & \(1.295\) & & \(-0.268\) \\ France & \(0.490\) & & \\ Germany & \(0.823\) & & \\ Italy & \(-0.146\) & & \\ United Kingdom & \(-0.352\) & & \(-0.268\) \\ USA & \(0.480\) & & \\ \hline \hline \end{tabular} 
\begin{tabular}{l l l l} \hline \hline
**(a) New Cases Endpoint.