esa-sceva/satcom-chunk-collection huggingface dataset:
# Location-Based Deferred Broadcast for Ad-Hoc Named Data Networking
## 5 Simulation Evaluation
### Trade-Off Study  
As aforementioned, both CA timer and FP timer use a number from a range according to our defined calculation method. The maximum value of a range determines not only the delay but also other performance such as transmission overhead. In this subsection, we study the impact of maximum CA timer and FP timer, separately.  
We first perform a similar study as Section 4. The difference is that in this simulation, use dynamic topology and mobility are configured. Figure 12 shows the impact of max CA timer to the normalized transmission overhead and average delay. The results indicate that the transmission overhead increases as the increment of max CA timer and remains similar values when max CA timer is larger than a certain value. Also, the results show that there is a clear linear positive correlation between max CA timer and average delay.  
Figure 13 gives the results of max FP timer's impact on the performance. We observed that longer max FP timers contribute to less normalized transmission overhead, but leads to longer average delay. This is a trade-off when assigning the value of max FP timers.  
Figure 11: Average Delay.  
Figure 12: Impact of Max CA Timer.
# Intra-Mode Decision Based on Lagrange Optimization Regarding Chroma Coding
## 4 Proposed Lagrange Optimization Scheme
### Rdo-Based Distortion Feature  
By combining the RDO theoretical approach, the Lagrange optimization within one chroma frame can be expressed as follows:  
\[J=\sum\
olimits_{k}D_{k}+\lambda_{fit}\sum\
olimits_{k}R_{k}. \tag{15}\]  
Assuming that the \(k^{\text{th}}\) block is associated with luma pixels, the corresponding coding distortion could propagate to the following chroma blocks below and right through intra prediction, which establishes a distortion connection between \(D_{k,blow}\) and \(D_{k,right}\) with respect to \(R_{k}\). Consequently, the RD cost can be calculated by derivation as follows:  
\[\begin{split}\frac{\partial J}{\partial R_{k}}=\frac{\partial D_ {k}}{\partial R_{k}}+\frac{\partial D_{k,blow}}{\partial R_{k}}+\frac{ \partial D_{k,right}}{\partial R_{k}}+\lambda_{fit}\\ =\frac{\partial D_{k}}{\partial R_{k}}+\frac{\partial(D_{k,blow}+ \theta_{blow}D_{k})}{\partial R_{k}}+\frac{\partial(D_{k,right}+\theta_{ right}D_{k})}{\partial R_{k}}+\lambda_{fit}\\ =(1+\theta_{blow}+\theta_{right})\frac{\partial D_{k}}{\partial R _{k}}+\lambda_{fit}\\ =\delta\frac{\partial D_{k}}{\partial R_{k}}+\lambda_{fit}.\end{split} \tag{16}\]  
So, the Lagrange optimization can be further transformed as follows:  
\[J=\sum\
olimits_{k}D_{k}+\frac{\lambda_{fit}}{\delta}R_{k}=\sum\
olimits_{k} D_{k}+\lambda_{fit}^{\prime}R_{k}, \tag{17}\]  
where the parameter \(\delta\) is estimated by linear regression using coded chroma blocks, and subsequently, the modified Lagrange multiplier \(\lambda^{\prime}_{ir}\) for the modes of LM, LM_T, and LM_L can be derived in turn using (17). Following that, the Lagrange multiplier \(\lambda^{\prime}_{sec}\), which utilizes the distortion measurement of SSE, can be updated by the following:  
\[\lambda_{sec}^{\prime}=(\lambda_{fit}^{\prime})^{2}=(\frac{\lambda_{fit}}{ \delta})^{2}. \tag{18}\]  
In intra-mode decision, the first rough decision is made using the Lagrange multiplier \(\lambda^{\prime}_{fit}\) for the modes of LM_T and LM_L, contrary to other modes which are aided by \(\lambda_{fit}\). Furthermore, \(\lambda^{\prime}_{sec}\) and \(\lambda_{sec}\) are separately incorporated into the fine decision process to determine the optimal chroma mode, where \(\lambda^{\prime}_{sec}\) is employed for the modes of LM, LM_T, and LM_L. Figure 7 illustrates the modified intra-mode decision using Lagrange optimization.
## 4 Results
### Model Explanation  
Deep learning networks are often described as \"black boxes\" because the reason that a network makes a certain decision is not always obvious. Interpretability techniques are used to translate network behavior into output that a person can interpret, such as locally interpretable model-agnostic explanations (LIME) [40], a Shapley value [41], and visualization methods. To explore the activations of the LSTM-based risk estimation method, activations were extracted to investigate and visualize the features learned by the LSTM network. Figure 18 shows the activations output by the LSTM layer (third layer) for each time step of the sequences in estimating the risk index of the hazard event--\"The robot is being lifted during the transportation phase, while the operator carefully positions it onto a transportation pallet. The operator remains in close proximity to the robot throughout the lifting process. Unfortunately, due to an unstable lift, the operator becomes trapped between a fixed object and the robot\". The heatmap showed how strongly the first 10 hidden units (a total of 250) activated and highlighted how the activations changed over time (the sequence length).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Risk Estimation Method**} & \multicolumn{3}{c}{**Accuracy**} & \multicolumn{3}{c}{**Acceptable Rate**} & \multirow{2}{*}{
\begin{tabular}{c} **Percentage of** \\ **Small Deviation** \\ \end{tabular} } & \multirow{2}{*}{\(RI_{dis}\)} \\ \cline{2-2} \cline{6-9}  & **Min** & & & & & & & & \\ \hline bi-LSTM-based & 5.71\% & 58.57\% & 80.00\% & 60.00\% & 94.29\% & 100.00\% & 90.29\% & 1.25 \\ LSTM-based & 20.00\% & 62.86\% & 88.57\% & 65.70\% & 88.57\% & 100.00\% & 88.76\% & 1.23 \\ CNN-based & 40.00\% & 70.00\% & 94.29\% & 65.71\% & 97.14\% & 100.00\% & 91.62\% & 0.51 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the three methods.
## 6 Experimental Results
### Normalization Performance Evaluation  
To verify the effectiveness of normalization for our proposed CDAENet method, we plot quantitative experiments of normalization versus without normalization on the metric of classification accuracy. As shown in Figure 15, there is a significant improvement in the classification performance of the normalization method compared to without normalization. Specifically, the accuracy improved by 0.78%, 1.72%, 1.02%, and 0.88% for D1, D2, D3, and D4, respectively. This result proves that the normalization process is important in our method.
## 1 Introduction
This is one of the most important limitations with UAV-assisted communications, requiring a proper management; otherwise, the concept can get infeasible if the the flight time of UAVs cannot be sufficiently prolonged (The sufficiency mentioned here should be discussed according to the scenario; i.e., the conditions of the networks and the requirements of mobile network operators as well as users, and hence, it is quite hard to put a formal and strict definition and/or a numerical value for it. However, the main idea is to maximize the flight time of UAVs as much as possible). Furthermore, UAV-based communication systems represent even a more complex case, because the total energy consumption depends on both the communication to the ground users and flying on a predefined trajectory or simply hovering over a fixed point. Besides, to cover as much as users as possible while maintaining the energy efficiency is an important aspect of UAV-assisted networking, since the the main objective is to enhance the capacity of the network to serve more users in temporally-dense networks. Put it another way, energy efficiency is required to realize the main objective, which is capacity enhancement in this case, an thus the idea is to keep the UAV BSs more in the air to serve more users in total. Therefore, to capture this phenomena in our work, we consider both the energy consumption and the total number of users covered as objective functions of our novel problem formulation. No-fly-zones (NFZs), which are restricted or prohibited areas where UAV is not allowed to fly--such as military bases--are considered as a practical constraint in the deployment phase of UAV BSs. This is because the optimal UAV trajectory and positioning are affected by NFZs given that UAVs need to avoid those places even if they are optimal positions. In other words, UAV BSs are supposed to be positioned considering the NFZ constraints, bringing additional challenge to the optimization process. In addition, as aforementioned, there are also regulations on the minimum and maximum altitude of the UAVs; such that the UAVs are supposed to be within the allowed range in terms of their altitude (The altitude regulations vary for different countries and regions). In this regard, to seize this idea of NFZs and altitude regulations in this work, we consider them as constraints in the problem formulation. Machine learning has a considerable place in optimizing wireless communications networks [9; 10; 11; 12; 13], due to its strong capabilities in terms of convergence, dynamism, and agility. Moreover, it is envisioned that it will play rather a more critical role in the upcoming generations of mobile communication networks, such as sixth generation of mobile communication (6G) [14; 15]. Therefore, we employed reinforcement learning (RL), a subset of machine learning, in this study to provide more intelligent, dynamic, and effective solution.
## 4 Experimental Results and Discussions  
The effect of the patch array has been shown in Figure 2b; the proposed antenna was fabricated on a 100 mm \(\times\) 100 mm double-sided FR4 epoxy. This antenna was the 5 \(\times\) 5 microstrip patch parasitic array using a cavity-backed slot. The experimental results were measured with the Agilent E5071C ENA network analyzer and compared to those of the simulation. Figure 14 showed the measured return loss of the antenna prototype. It could be seen from Figure 14 that the minimal return loss of \(-22.497\) dB appeared at 5.0 GHz. Figure 15 showed the comparison of the measured and simulated results in terms of the return loss (dB) versus the frequency (GHz). It could be seen from Figure 15 that the return loss and the operating bandwidth with \(-10\) dB of the return loss are \(-22.49\) dB and \(40\) MHz (\(4.97\)-\(5.02\) GHz), respectively. These were corresponding to an impedance bandwidth of \(1\%\) in respect to the center frequency of 5.0 GHz. Therefore, the measured and simulated results successfully met the requirements for the return loss.  
Figure 12: Simulated radiation pattern E-plane at 5.0 GHz.  
Figure 13: Simulated radiation pattern H-plane at 5.0 GHz.  
Although the measured return loss seemed to be a narrower bandwidth over the simulated outcomes, these discrepancies were due to the following: the fabrication tolerances of the related physical parameter, the position of the cavity-backed slot coupling, the uncertainty of the thickness and/or the dielectric constant of the FR4 substrate, and the effects of improper soldering of the cavity and SMA connectors that are used in material characterization and microwave devices [31]. Figure 16a,b showed the measured and simulated values in the E-plane (y-z plane) and H-plane (x-y plane) at 5.0 GHz. with far-field normalized radiation patterns. These radiation patterns of the proposed antenna were measured with the HP 8722D Network Analyzer in an anechoic chamber. The obtained radiation patterns have directional radiations in the E-plane and H-plane of half-power beam widths around \(\pm\) 65\({}^{\circ}\) and \(\pm\) 40\({}^{\circ}\), respectively. The maximum directive gain of the proposed antenna computed by applying the Friis Transmission Equation [32] was approximately 5.6 dB. The measured front-to-back ratios in comparison with the simulated one of E-plane radiation patterns were 30 and 16 dB, respectively while the simulated ones of H-plane radiation patterns were 22 and 14 dB, respectively.  
Figure 14: Measurement returns loss of an antenna prototype.  
Figure 15: Comparison between the measured and the simulated return losses.
## 2 Materials and Methods
### Characterization
#### 2.3.4 Mechanical Characterization  
The tensile tests were conducted on a universal testing machine, according to the ASTM D412 standard, in which five specimens of the polyureas sample were tested with a crosshead speed of 15 mm/min and a gauge length of 10 mm. A 100 kN load cell was used to stretch the specimens until failure-yield or fracture. The tensile strength, T\({}_{(xxx)}\)), at a specific (xxx)% elongation, and the elongation (E), at any degree of an extension, were calculated by Equations (2) and (3) respectively:  
\[\text{T}_{(xxx)}=\text{F}_{(xxx)}/\text{A} \tag{2}\]  
\[\text{E}=100\ ((\text{L}-\text{L}_{\text{o}}))/\text{L}_{\text{o}} \tag{3}\]  
where F\({}_{(xxx)}\) is the force at a specified elongation, A is the cross-sectional area of the unstrained specimen, L is the observed distance between benchmarks on the extended specimen and L\({}_{\text{o}}\) is the original distance between benchmarks.  
The compression tests were carried out according to the standard ISO 604 to determine the modulus and compressive strength of the polyureas. The speed used to determine the compression modulus and the compressive strength was 1 mm/min and 5 mm/min, respectively. Type A specimens were used to determine the modulus of elasticity and Type B to determine the compressive strength, both with a resistant cross-section of 10 \(\times\) 7 mm.  
The test method used to measure Shore D hardness of the polyureas was following the standard ASTM D2240. During the hardness tests, the durometer indenter foot penetrated the samples for 3 or 15 s.
## 4 Methods
### Data Acquisition  
The hyperspectral imaging spectrometer used in this paper is HySpex-320m imaging system (Figure 7a). The rock chips were run sequentially through the camera at a distance of about 0.5 m under artificial illumination. Two sensors were used in this camera, one of which operated in the VNIR and the other in the SWIR regions. The VNIR sensor is configured to record 125 bands, consisting a full spectrum with a spectral sampling interval of 3.7 nm between 0.4 and 1.0 \(\upmu\)m. The spectral range of the SWIR sensor is 1.0-2.5 \(\upmu\)m with 256 bands, and the spectral resolution is 6.25 nm. Because the hyperspectral images were obtained from the cut surface of the rock chips at a close distance, the spatial resolution is as high as 0.3 mm \(\times\) 0.3 mm.  
Point spectroscopy is often used to validate mineralogy mapping results [27]. In this paper, ASD FieldSpec-3 pro spectrometer was used to obtain the spectra of 15 samples (Figure 7b). The sampling interval from 0.35 to 1.0 \(\upmu\)m is 1.4 nm and from 1.0 to 2.5 \(\upmu\)m is 2.0 nm. It was fitted with a contact probe with a light source, which ensures stable illumination conditions during spectral measurement. The spectra were then divided by the reflectance of the calibration panel to obtain absolute reflectance. At least 5 individual measurements of each sample were taken to avoid any heterogeneity.  
Figure 6: The laboratory-based hyperspectral imaging processing model.  
Figure 7: The instruments used for data acquisition in this paper: (**a**) HySpex hyperspectral imaging system and (**b**) Analytical Spectral Devices (ASD) FieldSpec-3 pro spectrometer.  
The samples of Q-2, Q-4, Q-11, and Q-13 were analyzed using the electron probe microanalyzer (EPMA) to determine the specific chemical composition of chlorite. The JEOL JXA-8230 was taken at the Key Laboratory of Mineralogy and Metallogeny, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences. The operating conditions were acceleration voltage of 15 kV, 20 nA beam current, and 1 \(\upmu\)m beam diameter. The analysis standards include tugtupite, orthoclase, albite, BaF\({}_{2}\), kaersutite, diopside, rhodonite, rutile, magnetite, and olivine.
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 8 ML and Cyberattack Detection
### ML Challenges in WSN
#### 8.3.4 Challenges Related to Cross-Layer Attack Detection  
Most of the existing techniques only mitigate specific types of attacks belonging to a single stack layer, excluding attacks on other layers. For instance, the network layer IDS can only detect routing attacks, and cannot recognize attacks belonging to the MAC, physical, or transport layers. It is essential to develop a cross-layer IDS that can detect different possible attacks that may occur at different WSN layers. Attacks can be identified by exploiting the information across the different layers to correlate the cross-layer features among them, such as between the MAC and network layers [86; 88; 89].
# Improvements of the xAAL Home Automation System
## 5 The xAAL Communication Protocol
### The Security Layer  
The data of the Security Layer is the payload of the UDP multicast messages of the xAAL bus. This is a CBOR array of five fields:  
* [0]: _version_--Unsigned int, of the value 7. The version of the protocol. Other values should be rejected, the message is ignored.
* [1]: _seconds_--Unsigned int. First half of the timestamp. The number of seconds since the Epoch (1970-01-01 00:00:00 +000 UTC).
* [2]: _microseconds_--Unsigned int. Second half of the timestamp. The number of microseconds since the beginning of the above second.
* [3]: _targets_--A definite byte string build as the CBOR serialization of the array of destination addresses for the message. Note that xAAL device addresses are UUID (see Section 3), here encoded as definite bytesting [16] without tags. An xAAL device receiving a message should accept it if its own xAAL address is present in the array of targets. An empty target array means a broadcast message. A target field containing an empty byte string is not allowed (the message should be ignored).
* [4]: _payload_--A definite byte string which is the ciphered _Application Layer_ according to version 0.5-r2 principles (Poly1305/Chacha20, a symmetric key, a binary nonce build on the timestamp of messages, an acceptance window for the timestamp of messages, the target field covered by the cryptographic signature). If a message includes other fields in addition to the above mandatory ones, the message may be accepted, but the extra fields must be ignored. The above-described CBOR items must have no CBOR tags. Figure 2 gives the CDDL specification of the xAAL Security Layer.
# Utility-Based Wireless Routing Algorithm for Massive MIMO Heterogeneous Networks
## 5 Numerical Results
### Simulation 1: Available Antennas  
In the first simulation, we observed the performance of the three methods under different antenna resources. The total BS, i.e., \(I\), was set to 60, while \(N_{i}\) of each BS was adjusted from 400 to 2000. The average utility and transmission speed (Mbps) of each area of each method are shown in Figure 5a,b. As \(N_{i}\) increased, the average transmission speeds of all areas grew. Among them, BSs in the first area had obviously higher speeds in SPT and CFC because inner-layer BSs require fewer links and thus are considered more efficient to serve in SPT and CFC. On the other hand, PSR aims to maximize concave utility functions of each BS and serves BS in a more balanced way. When resources are scarce, it favors the BS with better channel quality. However, when it is served with certain amounts of resource and tends to saturate, it turns to those having more links or poorer channels. Thus, BSs in different layers  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Bandwidth (B) & 1000 MHz \\ Transmit power (P) & 10 W \\ Noise density (D) & \(-\)204 dB/Hz \\ \(l_{i}(s,t)\) & \(\min(s_{i},t)\cdot B_{s}\cdot\log_{2}\Bigl{(}1+\frac{P_{i}\times 10^{20}}{R_{s }\cdot 10^{200}}\Bigr{)}\) bps \\ Minimal distance between BSs & 250 m \\ The number of UE for each BS & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 4: Simulation environment.  
had relatively approximate speed in PSR, and those in the third area had data rates higher than the other two schemes.  
Figure 5c shows the total utility of the system. The total utility of all methods was improved as the available resources increased. Among them, the total utility of PSR was significantly higher than the other two schemes because PSR is utility-oriented. The total utility of CFC was higher than SPT because there are many unnecessary short links in SPT that always find the shortest path of nodes.
## 3 Results
### Recruitment and Sample Selection
#### 3.1.3 Baseline Characteristics of the Study Sample
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variables** & \(n\) & \begin{tabular}{c} **Overall** \\ **(n = 1030)** \\ \end{tabular} & \
\begin{tabular}{c} **Intervention A** \\ **(n = 487)** \\ \end{tabular} &
\begin{tabular}{c} **Intervention B** \\ **(n = 543)** \\ \end{tabular} & \(p\)**-Value** \\ \hline Duration of interview in minutes, M (SD) & 1030 & 137.1 (35.8) & 139.9 (33.0) & 134.5 (37.9) & 0.016 \\ \hline \multicolumn{6}{c}{Sociodemographic characteristics} \\ \hline Age in years, mean (SD) & 1030 & 69.0 (4.9) & 69.1 (4.9) & 68.8 (5.0) & 0.418 \\ \hline Sex, female (\%) & 1030 & 52.1 & 52.0 & 52.3 & 0.910 \\ \hline Education (CASMIN), \% & & & & & \\ Low & 24.4 & 27.3 & 21.7 & & \\ Intermediate & 1030 & 53.0 & 52.6 & 53.4 & \\ High & & 22.6 & 20.1 & 24.9 & 0.053 \\ \hline Income in Euros, mean (SD) & 930 & 1572.2 (845.8) & 1543.1 (779.0) & 1597.8 (900.7) & 0.325 \\ \hline Married/cabiatting, \% & 1030 & 64.6 & 65.5 & 63.7 & 0.550 \\ \hline \multicolumn{6}{c}{Cognitive function} \\ \hline MoCA sum score (points), mean (SD) & 1026 & 24.5 (3.1) & 24.5 (3.0) & 24.6 (3.1) & 0.574 \\ Total score \(\geq\) 26, \% & & 39.4 & 36.8 & 41.8 & \\ Total score \(<\) 26, \% & & 60.6 & 63.2 & 58.2 & 0.106 \\ \hline CERAD: Word List Learning (words), mean (SD) & 1004 & 18.8 (4.5) & 19.2 (4.1) & 18.5 (4.9) & 0.024 \\ \hline CERAD: Word List Recall (words), mean (SD) & 1002 & 5.7 (2.3) & 5.9 (2.2) & 5.6 (2.3) & 0.042 \\ \hline CERAD: Verbal Fluency (words), mean (SD) & 1022 & 22.2 (5.6) & 21.9 (5.5) & 22.5 (5.7) & 0.114 \\ \hline Trail Making Test A (seconds), mean (SD) & 1012 & 52.3 (21.2) & 52.5 (20.9) & 52.2 (21.5) & 0.824 \\ \hline Trail Making Test B (seconds), mean (SD) & 1000 & 123.5 (59.7) & 122.4 (59.7) & 124.5 (59.8) & 0.582 \\ \hline Reading the Mind in the Eyes Test (points), mean (SD) & 975 & 20.6 (4.1) & 20.5 (4.1) & 20.8 (4.1) & 0.257 \\ \hline \multicolumn{6}{c}{Health and lifestyle factors} \\ \hline High blood pressure (\%) & & & & & \\ SBP \(>\) 140 mmHg & 1012 & 53.9 & 59.0 & 49.3 & 0.002 \\ DBP \(>\) 90 mmHg & 1012 & 29.1 & 30.0 & 28.2 & 0.528 \\ \hline Systolic blood pressure, (mmHg), mean (SD) & 1024 & 145.6 (19.2) & 147.2 (18.4) & 144.1 (19.9) & 0.012 \\ \hline Diastolic blood pressure, (mmHg), mean (SD) & 1024 & 85.5 (11.0) & 86.1 (10.4) & 84.9 (11.5) & 0.081 \\ \hline BMI (kg/m\({}^{2}\)), mean (SD) & 1014 & 31.0 (5.5) & 31.0 (5.3) & 31.0 (5.6) & 0.998 \\ \hline BMI \(\geq\) 30 kg/m\({}^{2}\) (\%) & 1014 & 55.3 & 55.9 & 54.8 & 0.869 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Baseline characteristics of study participants in the AgeWell.de trial with respect to group allocation (\(n=1030\)).