esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 9 Discussion  
The review showed that an IoT solution in the context of Early Warning can be very effective in the tasks of data collection, transmission, and disaster prediction, all the while retaining cost-effectiveness. As such, Wireless Sensor Networks, Cloud solutions, Machine Learning, and other components of the Internet of Things should be used when deploying Early Warning Systems, or integrated into already existing ones. This would allow a denser presence of alert systems on the territory and would therefore provide timely warnings and essential data and instruments to the authorities, assuring economical and societal benefits by reducing risks associated with disastrous events. Moreover, many of the requirements of an IoT-based Early Warning system can be completely or partly satisfied by adopting an Edge Computing solution, possibly alongside a Cloud one. This would bring the system resources closer to the end devices of the network with the aim of reducing latency, number of transmissions, and data processing at centrals servers. The review showed some already existing trends in this regard, with examples of Edge solutions, on-site predictions and Edge AI implementations, as well as other technological trends such as the more and more frequent adoption of low-cost MEMS systems and Machine Learning prediction algorithms.  
The article also showed how an accurate choice of the network topology, or the implementation of mesh and adaptive solutions can be effective in satisfying other constraints such as coverage, reliability and battery management. The selection of the right communication protocol is also essential, both for the energy consumption and the coverage aspects. Ad hoc solutions such as LoRa or other Unlicensed protocols can be effective in areas where there is not good cellular coverage, but in this case the limits due to the shared radio resource need to be taken into account. Non-terrestrial-networks are also an important alternative to consider, since they could provide a more reliable back-end than terrestrial networks, which are more prone to failure in case of natural disasters, and can generally provide better coverage in areas that do not have good cellular infrastructures.  
Some gaps in the recent literature were also found, particularly the lack of fault tolerant solutions, both in terms of communications and sensing node failures, and the use of non state-of-the-art communication protocols, since, for example, some solutions still used older cellular technologies such as GSM.  
The recommendations provided aim at filling these gaps and giving a meaningful contribution to the future research on EW systems, particularly regarding topics such as very low-latency communication, real-time monitoring and control of environmental parameters, very low-cost sensors, and high density sensor networks. Moreover, another important recommendation concerns the potential benefits of widespread sensor networks, since density can provide better fault tolerance but also improve the effectiveness of predictions and event detection.
## 2 Pre-Scheduling Mechanism
### Handover Prediction  
Handover determination generally depends on the degradation of the Reference Signal Receiving Power (RSRP) from the base station (eNodeB). When the threshold value is reached, a handover procedure is triggered. Many works have focused on handover decisions [11; 12; 13; 14; 15; 16]. In this paper, user measures RSRP periodically with neighbor eNodeBs. In addition, we use exponential smoothing (ES) to remove high-frequency random noise (Figure 1), where \(\alpha\) is a smoothing constant. Then, we incorporate a linear regression model with RSRP values to predict time-to-trigger (TTT) for handover.  
The linear regression equation can be simply expressed as follows:  
\[\hat{P_{i}}=a+bt_{i},\ i=1,\ 2,\ \ldots,\ n \tag{1}\]  
where \(\hat{P_{i}}\) is the predictive value of RSRP at time \(t_{i}\), and \(a\) and \(b\) are coefficients of the linear regression equation. Then, we use the least squares (LS) method to deduce \(a\) and \(b\). The method of LS is a standard solution to estimate the coefficient in linear regression analysis.  
Let the sum of the residual squares be \(S\), that is  
\[S=\sum_{i=1}^{n}\left[P_{i}-(a+bt_{i})\right]^{2} \tag{2}\]  
Figure 1: Exponential smoothing (\(\alpha\) = 0.2).  
where \(P_{i}\) is the measured value of RSRP at time \(t_{i}\). The least squares method is to try to find the minimum of \(S\), and then the minimum of \(S\) is determined by calculating the partial derivatives.  
\[\text{Let}\left\{\begin{array}{l}\dfrac{\partial p}{\partial a}=\sum_{i=1}^{n }2\left[P_{i}-(a+bt_{i})\right](-1)=0\\ \dfrac{\partial p}{\partial b}=\sum_{i=1}^{n}2\left[P_{i}-(a+bt_{i})\right](- t_{i})=0\end{array}\right. \tag{3}\]  
Finally we can get  
\[\left\{\begin{array}{l}a\ =\overline{P}-b\overline{t}\\ b\ =\ \dfrac{\sum_{i=1}^{n}t_{i}P_{i}-n\overline{T}\overline{P}}{\sum_{i=1}^{n}t_{ i}^{2}-n\overline{T}^{2}}\end{array}\right. \tag{4}\]  
where \(\overline{T}=\dfrac{\sum_{i=1}^{n}t_{i}}{n}\) and \(\overline{P}=\dfrac{\sum_{i=1}^{n}P_{i}}{n}\). If there are several neighbor eNodes, we select the eNodeB with the maximum variation of RSRP (maximum slope) as target eNodeB. In Figure 2a, we can see that while RSRP\({}_{\text{SeNB}}=\text{RSRP}_{\text{TeNB}}\), the handover procedure is triggered. We have trigger time \(t_{t}=\dfrac{a_{1}-a_{2}}{b_{2}-b_{1}}\).
# Evaluating the Effects of Deep Excavation on Nearby Structures Through Numerical Simulation
## 4 Scenarios Simulation Under Adjacent Building Conditions
### Effect of Changing the Distance Factor of Adjacent Buildings
#### 4.1.1 MC Model  
This section uses the MC model to analyze the impact of varying the distance between the adjacent building and the diaphragm wall, comparing the distances of 1 He, 2 He, and 3 He. The analysis was conducted with fixed loads of 5 F (91 kPa) and 12 F (195 kPa), and fixed basement depths of 0 He, 0.25 He, 0.5 He, 0.75 He, and 1.0 He. The results show that at a distance of 1 He and a depth of 0 He with a load of 5 F, the maximum wall deformation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Soil Model** & **Load Factor (Weight)** & **Distance Factor (Dis)** & **Depth (Df)** \\ \hline \multicolumn{3}{c}{5F (91 kPa)} & \multicolumn{2}{c}{0 He} \\ MC & 91 kPa/24 (kN/m3) = 3.8 m & 1 He & 0.25 He \\ HS & Slab thickness d = 3.8 m & 2 He & 0.5 He \\ HSS & 12F (195 kPa) & 3 He & 0.75 He \\  & 195 kPa/24 (kN/m3) = 8.125 m & 3 He & 0.75 He \\  & Slab thickness d = 8.125 m & 1.0 He \\ \hline \hline \end{tabular} Note: He is the final excavation depth within the site, which is 16 m.  
\end{table}
Table 4: Numerical analysis of the maximum lateral deformation of the diaphragm wall.  
Figure 9: The illustration diagram of the distance factor. (**a**) One times the excavation depth (1 He); (**b**) two times the excavation depth (2 He); (**c**) three times the excavation depth (3 He).  
was 5.13 mm; with a load of 12 F, it was 5.61 mm. At a distance of 2 He and a depth of 0 He, with a load of 5 F, the maximum wall deformation was 5.12 mm; with a load of 12 F, it was 5.16 mm. At a distance of 3 He and a depth of 0 He with a load of 5 F, the maximum wall deformation was 4.83 mm; with a load of 12 F, it was 5.1 mm. This indicates that when the distance between the adjacent building and the diaphragm wall is 1 He, it has a more significant impact on wall deformation. When the distance exceeded 2 He, the impact on the wall began to converge. Figure 10 illustrates the extent of the distance factor's influence on the diaphragm wall under fixed depth and load conditions.
# Cooperative Buffering Schemes for Time-Shifted Live Streaming of Distributed Appliances
## 4 Experimental Results
### Computation Reduction of Cooperative Buffering Schemes  
Figure 15 shows the computational complexity of the \(D\_DP\) and \(R\_RP\)-based schemes in terms of the number of operations required to evaluate the criteria when a tracker server determines the target time range to be buffered and when the buffer size is 300 s. We varied the entire time-shifting hours provided by all participating PVRs from 50 to 250 min, \(r_{b}\) value from 0.4 to 1, and \(N_{ts}\) between 1 and 3. We can see that the number of operations is only 9 when \(r_{b}\) is 1 and the time-shifting hours are 50 min and 124 when \(r_{b}\) is 0.4 and the time-shifting hours are 250 min. This is because the operations should be performed for more time ranges as time-shifting hours are extended or \(r_{b}\) decreases. This implies that we can control the complexity by adjusting \(r_{b}\). Moreover, we can further reduce the complexity by employing more tracker servers. It can be seen that, when \(N_{ts}\) becomes 3, the number of operations decreases to one third in all cases because the evaluation loads are distributed to more tracker servers.  
Figure 16 demonstrates the number of the aggregate buffermap update operations per second according to \(N_{p}\) while varying \(I_{u}\) and \(N_{ts}\). As expected, we can see that the number of operations per second is only 14 when \(N_{p}\) is 500 and \(I_{u}\) is 9, while 208 when \(N_{p}\) is 2500 and \(I_{u}\) is 3. This indicates that as \(N_{p}\) decreases and \(I_{u}\) increases, the total number of the update operations for the aggregate buffermap can be reduced significantly by extending the interval of the operations. It can be also seen that, with the increased \(N_{ts}\), the complexity can be further reduced to a similar degree.  
Figure 15: The number of criteria determination operations according to time-shifting hours with a 300 s buffer.  
Therefore, we can see that our proposed schemes can provide the time-shifted live-streaming services in real time by adaptively adjusting their computational complexity, i.e., by extending the intervals of target time ranges and the aggregate buffermap update operations, and increasing the number of tracker servers, depending on request arrival rates and computation capacity of tracker servers.
###### Abstract  
Traditional authentication techniques, such as cryptographic solutions, are vulnerable to various attacks occurring on session keys and data. Physical unclonable functions (PUFs) such as dynamic random access memory (DRAM)-based PUFs are introduced as promising security blocks to enable cryptography and authentication services. However, PUFs are often sensitive to internal and external noises, which cause reliability issues. The requirement of additional robustness and reliability leads to the involvement of error-reduction methods such as error correction codes (ECCs) and pre-selection schemes that cause considerable extra overheads. In this paper, we propose deep PUF: a deep convolutional neural network (CNN)-based scheme using the latency-based DRAM PUFs without the need for any additional error correction technique. The proposed framework provides a higher number of challenge-response pairs (CRPs) by eliminating the pre-selection and filtering mechanisms. The entire complexity of device identification is moved to the server side that enables the authentication of resource-constrained nodes. The experimental results from a 1 Gb DDR3 show that the responses under varying conditions can be classified with at least a 94.9% accuracy rate by using CNN. After applying the proposed authentication steps to the classification results, we show that the probability of identification error can be drastically reduced, which leads to a highly reliable authentication.  
DRAM latency-based PUF; IoT; authentication; convolutional neural network +
Footnote †: journal:  
0021  
Deep PUF: A Highly Reliable DRAM PUF-Based Authentication for IoT Networks Using Deep Convolutional Neural Networks
## 3 Mathematical Approach Using Homomorphic Encryption and Zero-Knowledge Proofs
### Encrypting Data with Homomorphic Encryption  
Assume we use a homomorphic encryption scheme HE, such as CKKS or BFV, which supports arithmetic operations on encrypted data, Figure 1. The client \(C_{i}\) encrypts their data before sending it to the server.  
The client encrypts \(X_{i}\) using their public key:  
\[X_{i}^{enc}=HE.Enc(X_{i},pk_{i})\]  
where \(pk_{i}\) is the public key of the client \(C_{i}\).  
The client trains their model up to layer \(L_{cut}\) and sends the encrypted data to the server:  
\[Z_{i}^{enc}=HE.Enc(f(X_{i};\theta_{i}),pk_{i})\]  
where \(f(X_{i};\theta_{i})\) represents the output from the part of the network processed by client \(C_{i}\).  
The server receives \(Z_{i}^{mc}\) and performs further computations on the encrypted data, without decrypting it:  
\[y_{S}^{mc}=HE.Eval(Z_{i}^{mc};\theta_{S})\]  
where \(HE.Eval\) is a function that performs encrypted operations on \(Z_{i}^{mc}\) using the model parameters on the server \(\theta_{S}\).  
The server sends the encrypted response \(y_{S}^{enc}\) back to the client, and the client decrypts it to obtain the results:  
\[\theta_{S}=HE.Dec(y_{S}^{enc};\theta_{S})\]  
where \(sk_{i}\) is the secret key of client \(C_{i}\).  
This approach ensures that the server does not have access to the client's data \(X_{i}\), and the client does not have access to the server's parameters \(\theta_{S}\). All operations are performed on encrypted data.
## 3 Results and Discussion  
### Influence of Bacterial Inoculum Densities  
For the best discrimination of bacterial communities, it is of fundamental importance to choose the shortest incubation time at which _AWCD_ reaches a peak value before the following constant phase. Figure 2A shows the _AWCD_ values measured every 12 h during a 144-h incubation from different soil dilutions. In our case the dilutions examined were 10\({}^{-5}\), corresponding to \(\sim\)10\({}^{4}\) colony forming units (CFUs) mL\({}^{-1}\) measured by plate counting, 10\({}^{-4}\) and 10\({}^{-6}\). It appears that the dilution with 10\({}^{-6}\) dilution contained a relatively low number of bacteria (\(\sim\)10\({}^{3}\) CFUs mL\({}^{-1}\)) causing an increasing _AWCD_ trend over time, without reaching a constant phase before 144 h and high variability among replicates, as showed by the high values of standard deviation (Figure 2A). On the other side, the 10\({}^{-4}\) dilution likely had too many bacteria (10\({}^{5}\) CFUs mL\({}^{-1}\)) and _AWCD_ reached a peak after only 48 h, followed by a slight decline likely due to substrates or triphenyl formazan degradation, without a clear constant phase (Figure 2A). Instead, the dilution of 10\({}^{-5}\) (\(\sim\)10\({}^{4}\) CFUs mL\({}^{-1}\)) reached a constant phase at 96 h and so it was chosen as the time for measuring _AWCD_ values (Figure 2A). Similar trends were observed both for \(H^{\prime}\) (Figure 2B) and \(E\) values (Figure 2C), that had a constant phase starting from 96 h in the dilution of 10\({}^{-5}\) (\(\sim\)10\({}^{4}\) CFUs mL\({}^{-1}\)), with low values of standard deviation from 96 to 120 h of incubation (Figure 2B,C). The common pattern of the graphs of Figure 2 likely depends on the fact that the calculation of _AWCD_, \(H^{\prime}\) and \(E\) are all based on the values of \(c_{i}\), as previously explained.  
Comparing our results with those of other studies [10; 11; 12; 13; 14; 15; 16] using our protocol, we managed to obtain more stable growth curves for _AWCD_, particularly in the constant phase and relatively low values of SD (Figure 2A). Another important remark is that the inoculum could be relativized simply resuspending soil mass, in order to not to artificially homogenize differences that are intended to be detected among the different soil samples of the same experiment [17; 18; 19; 20]. In order to avoid this possible artefact, in our case, the same dilution (leading to \(\sim\)10\({}^{4}\) CFUs mL\({}^{-1}\)) was chosen for all the samples analysed in the same experiment.
## 2 Materials and Methods
### Methods
#### 2.3.3 Ecosystem Water Use Efficiency and Ecosystem Resilience  
The eWUE was extracted from available daily global GPP and annual ET datasets, generated using MOD17 algorithm. The eight-day GPP product was processed by the MOD17 algorithm considered vegetation productivity which is directly related to the absorbed solar energy. The algorithm estimated the fraction of photosynthetically active radiation (FPAR) multiplied from MOD15 and daily PAR at pixel level from GMAO NASAwith a set of biome-specific radiation use efficiency (\(\varepsilon\)) to produce the daily absorbed photosynthetically active radiation (APAR) and predict the daily GPP [42]. The daily GPP were aggregated to form the annual GPP, and the mean annual GPP values were evaluated in comparison to GPPEC from global FLUXCOM observations, Carbon fluxes developed based on model tree ensemble ([[https://www.bgc-jena.mpg.de/geodb/projects/Data.php](https://www.bgc-jena.mpg.de/geodb/projects/Data.php)]([https://www.bgc-jena.mpg.de/geodb/projects/Data.php](https://www.bgc-jena.mpg.de/geodb/projects/Data.php))) using R2 coefficient and RMSE error. Likewise, the mean annual ET datasets were compared with the monthly evapotranspiration product of the USGS early warning system for the African continent ([[https://earlywarning.usgs.gov/fews/product/66](https://earlywarning.usgs.gov/fews/product/66)]([https://earlywarning.usgs.gov/fews/product/66](https://earlywarning.usgs.gov/fews/product/66))), which was averaged as annual ET (2000-2014) for the Horn of Africa. Finally, we generated the annual ecosystem water use efficiency (eWUE) from the ratio of average annual MODIS GPP to the average annual ET for the last 15 years.  
The trend of the eWUE and drought patterns from the SPEI dataset were detected using a linear regression model similar to the MODIS NDVI trend method (Section 2.3.1). The eWUE trend result was aggregated into different land cover types (CCI-LC) and Agro-ecological zones of the area of interest. Finally, the ecosystem resilience to drought was calculated using the dimensionless ecosystem resilience index (eRd) from the ratio of mean values of multi-annual eWUE to the annual eWUE of the driest year as initially defined by [PERSON] and [PERSON] [43] and further applied in other studies [44; 45]. The driest year (2009) of high drought severity in the Horn of Africa was identified from the spatial and temporal patterns of the high-resolution annual SPEI images, this was also checked and matched with the EM-DAT record of drought [46]. The eRd was finally classified into four major classes as shown in Table 1.
# Simulation of a Narrowband Power Line Communications System over Medium Voltage
## 5 Simulation Results and Discussion
### Maximum Reach of NB-PLC Signals via the MV Grid
As to \(D_{max}\), which is a crucial parameter, the simulation results show that NB-PLC signals are capable of transmission over greater distances compared to BB-PLC systems, where a repeater is necessary practically every 500-700 m [6]. Finally, the simulation results are in line with previous experimental data on NB-PLC systems. Successful NB-PLC transmission has been demonstrated over medium voltage power lines that are 1.05 km and 1.4 km long [20; 21]. The simulation results presented here suggest that those lengths can be extended to even longer distances.
# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 5 Methods
### ResTA Spectrum Feature Extraction Network
The output of the triplet attention module is obtained by weighting the attention tensors from the three branches and is expressed as  
\[\theta_{att}=\alpha\theta_{att-1}+\beta\theta_{att-2}+\gamma\theta_{att-3} \tag{7}\]  
The weighted aggregation of outputs from the three branches is conducted, introducing attention mechanisms into the network. The multilayer stacking of triplet attention enables ResTA to adaptively optimize features, expanding the receptive field with almost no introduction of additional parameters. ResTA correlates the three dimensions of channels, frequencies, and amplitudes in the spectrogram, selectively processing spectrogram features. Features with attention will be beneficial for subsequent signal detection and parameter estimation tasks.
## 6 Performance Evaluation
### Evaluation for the Designed Protocol
#### 6.1.6 The Whole Content Transmission Delay  
In Figure 5d, we compare the whole content transmission delay issue, which is the main performance metric for the user's satisfaction. As can be seen from this figure, our proposed LBRP can generate the smallest end-to-end delay for the whole content dissemination. For the three other protocols, without the consideration of either speed or position information, the delay performance can be affected to a certain extent. The detailed reason for the presented results lies in the previously-illustrated performance metric comparison in Figure 5a-c.
## 3 Experimental and Simulation Setup  
In our research, both simulation and real-world experimentation serve complementary purposes to comprehensively explore vehicular communication and edge computing within a 5G network while considering varying communication distances between OBUs and RSUs. The purpose of the simulations is to replicate controlled virtual environments where various scenarios can be tested under specific conditions, providing insights into system behavior and performance in idealized settings. On the other hand, the purpose of real-world experiments is to validate the simulation results and capture the nuances and challenges of actual deployments while considering factors such as signal interference, environmental conditions, and hardware limitations.
# A Theoretical Method and Model for TBM Tunnelling Trajectory Adjustment
## 5 Discussion  
### Existence of the Solution Space of \(\alpha_{1}\) and \(\alpha_{1}\)  
In the proposed model, three constraints are developed to limit the selection range of \(\alpha_{1}\) and \(\alpha_{2}\). In general, there is a solution space of \(\alpha_{1}\) and \(\alpha_{2}\) that satisfies the three constraints at the same time. However, when the bias distance and angle reach a high level, the three constraints cannot be satisfied at the same time, and the proposed method is not suitable for this situation. In this section, the above cases and the applicability of the model will be researched.  
According to the three stages of the proposed trajectory adjustment method, the bias distance reaches its maximum at the end of the first stage, and the maximum is not permitted to exceed 60 mm. Based on Equation (6), the maximum bias distance is related to the original bias distance, original bias angle, and the rotated angle of the first stage \(\alpha_{1}\). In this section, the maximum is set to 60 mm, the \(\alpha_{1}\) is set to its maximum--which is calculated by Equation (6)--and a relationship between the original bias angle and bias distance can be calculated. This relationship represents a series of situations, and in these situations, the permitted maximum of \(\alpha_{1}\) is used to adjust the TBM, and the maximum bias distance reaches 60 mm. Further, if the original bias distance and angle are larger than these situations, then the maximum bias distance will exceed 60 mm or the \(\alpha_{1}\) will be larger than the permitted value; in other words, the proposed constraints cannot be satisfied at the same time, and the proposed model is not suitable for these situations. The above relationship between the initial bias angle and distance is shown in Figure 15.  
As Figure 15 shows, the curve indicates a series of critical situations. If the original bias angle and distance are located on this curve, and the rotated angle \(\alpha_{1}\) is set to its maximum, then the maximum of the bias distance exactly reaches 60 mm. In addition, in the situations below to the curve, more flexible \(\alpha_{1}\) can be used to adjust the trajectory of TBM, and the proposed method and model is suitable for these situations. On the contrary, the proposed method and model is not suitable for the situations above the curve.