esa-sceva/satcom-chunk-collection huggingface dataset:
# Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
## 2 Background
### Covert Channel Evaluation  
The channel capacity of covert communication typically refers to the maximum amount of information that the channel can transmit per unit time, usually measured in bits per second (bps). The Trusted Computer System Evaluation Criteria (TCSEC) [40] states that a channel bandwidth more than 100 bps is considered a high-bandwidth channel. Physical covert channels have been shown to have widely varying bandwidths, ranging from a few bps to kilobits per second (kbps). Table 1 summarizes the important characteristics of various types of physical covert channels. Generally, thermal covert channels are at the lower end of the spectrum, at approximately 10 bps. The bandwidth of power covert channels is slightly higher at a few hundred bps [32; 38], and electromagnetic covert channels have the highest bandwidth on the order of multiple kbps [31]. Generally, the bandwidth decreases if the covert channel is established between two separate devices and not between two compute elements within the same device. Another metric commonly used to describe covert channels is bit error rate (BER), which is the number of incorrect bits transmitted, divided by the total number of transferred bits, over a period of time. Physical covert channels aim to have a BER that is as low as possible, typically below 2%, as listed in Table 1[32; 33; 38; 41]. Although both bandwidth and BER are able to measure and quantify the performance of the side-channel, no insight on the covertness is provided by these metrics. [PERSON] et al. proposed using metrics, such as steganographic capacity, to remedy this issue [5]. Steganographic capacity refers to the maximum amount of data that can be covertly transmitted before the likelihood of detection.
# Spatial Deformation Calculation and Parameter Analysis of Pile-Anchor Retaining Structure
###### Abstract  
Scholars often consider the deformation of a foundation pit retaining structure as a significant indicator of its stability. However, the current theoretical prediction formula for pit with pile-anchorretaining structure deformation is not yet perfect. This study utilizes a simplified spatial deformation model of a pile-anchorretaining structure and the principle of minimum potential energy to derive a prediction formula for the retaining structure's spatial deformation. Afterwards, a numerical simulation model is developed based on actual engineering practices. On-site monitoring data is compared with the results of theoretical calculation formulas and numerical simulation models to validate their applicability. The research findings reveal minimal discrepancies between the theoretical calculation results, numerical simulation outcomes, and on-site monitoring data, indicating a high level of accuracy. Those three results follow consistent rules. The horizontal deformation curve of the crown beam exhibits a \"V\"-shaped distribution, and as the distance from the calculation point to the centerline of the foundation pit decreases, the horizontal deformation of the crown beam increases. The horizontal deformation curve of the pile displays a \"V\"-shaped distribution, and the pile's horizontal deformation increases as the distance to the centerline of the foundation pit decreases. The research findings indicate that increasing the size and material strength of the crown beam and waist beam has only a limited effect on controlling the retaining structure's deformation. However, by increasing the size and material strength of the pile, the deformation of the retaining structure can be significantly reduced.  
pile-anchor composite system; spatial deformation; prediction formula; numerical simulation; parameter analysis +
Footnote †: journal: Applied Sciences  
1
## 3 Results and Discussion
### Answering RQ1 and RQ2
Because AR is a technology enhancing human perspectives by virtual and real context fusion, a universal human-centered model for AR-based solutions development can help in closing the gap between academia and industry implementations [236, 237]. Following the international human-centered design standards ISO 9241-210, 2019 [188, 195], a human-centered model can be developed by combining a simplified AR pipeline [17] and AR system elements [238] with a value-sensitive design approach for smart 4.0 operators [239]. All the AR-based implementations and their industry adoption status are shown in Table 10.  
Layer 5 provides an overview of the emerging trend in integrating AR with AI, industrial IoT, Digital twin and other comprehensive industry 4.0 technologies that result in the development of layer 5 Intelligent AR (IAR) solutions. This layer provides the holistic approach for implementing intelligent industry 4.0 elements with AR to enhance the robust and smart features of AR systems/solutions for long-term adoption in industry. This layer considers all studies that propose or involve interesting, significant concepts, algorithms and implementations that show high potential in the further development of the IAR system in the future (see Table 11). \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Classification Criteria**} & \multicolumn{2}{c}{**References**} \\ \hline \multirow{5}{*}{\begin{tabular}{c} Concept and \\ Theory \\ \end{tabular} } & \multirow{5}{*}{\
\begin{tabular}{c} \\ \end{tabular} } & \multirow{5}{*}{
\begin{tabular}{c} Field \\ experiment \\ \end{tabular} } & [7, 30, 122, 138, 143, 146, 151, 152, 161, 162] \\  & & & [165, 167, 181, 188, 193, 196, 198, 199] \\  & & & [201, 220, 222, 223, 225] \\ \cline{3-3}  & & & [11, 51– 57, 63, 64] \\  & & & [66, 68, 71– 76, 79, 81] \\  & Laboratory & & [86, 90, 92, 98, 113, 119, 130, 140– 142] \\  & experiment & & [145, 147– 150, 154, 156, 159] \\  & & & [157, 163, 164, 166, 168– 180] \\  & & & [182, 184, 186, 187, 189, 192, 201] \\  & Novel stage & & [204, 206, 211, 214– 216, 218, 222] \\ \cline{3-3}  & & & [8, 62, 65, 67, 69, 77, 84, 87– 89] \\  & & & [93– 97, 99, 100, 102, 103, 110] \\  & Pilot project & & [115, 116, 121, 123, 125– 129, 131] \\  & & & [132, 139, 144, 153, 155, 158, 160, 177, 183, 185] \\  & & & [190, 191, 197, 200– 203, 205, 207– 210] \\  & & & [212, 213, 217, 219, 221, 224, 235] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Layer 4 AR solutions and their industry adoption status 2010–2021. Figure 11: Industry adoption distribution of AR solutions 2010–2021. Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) are recently applied or proposed for further improvement of the registration methods by enhancing the CV-based tracking algorithms [74; 191], while Industrial IoTs and Digital Twin are frequently considered in recent studies [37; 190] to utilize Big data information and the advantages of AR in visualization, fusing digital data with a real working context.
###### Abstract  
The removal of cadmium (Cd) and zinc (Zn) from soil by phytoremediation was investigated using _Taraxacum officinal_ and _Gazania_. A plant environmental control system was used to cultivate the plants. The effects of different biodegradable chelating agents (i.e., EDDS, HIDS, and GLDA), relative humidity, and other competitive metals on the adsorption of Cd and Zn were also studied. In addition, the approach for metal recovery was explored by extraction of metals from plants after phytoremediation using _Gazania_. The concentrations of Cd and Zn were determined by inductively coupled plasma mass spectrometry (ICP-MS). In addition, one-way analysis of variance (ANOVA) tests were performed.to determine significant differences between the experimental treatments adopted in this work. Consequently, the following main conclusions were obtained: (1) In the case of _Taraxacum officinal_, Cd and Zn could be removed even under the presence of other heavy metals. (2) By adding a chelating agent, the amount absorbed by the shoot generally increased. (3) In the case of _Gazania_, the concentration of Cd was higher in root than that in shoot, whereas the concentration of Zn was higher in the shoot than that in the root. (4) _Taraxacum officinal_ was more suitable for phytoremediation of Cd than _Gazania_. (5) Cd and Zn could be extracted from plants by adding a low concentration of nitric acid. (6) The one-way ANOVA tests showed no statistically significant differences among the experimental treatments.  
phytoremediation; cadmium; zinc; biodegradable chelating agent; one-way ANOVA; metal recovery +
Footnote †: journal: Applied Sciences
## 3 Results  
### Sequence Analysis  
High-throughput sequencing was performed using an Ion Torrent platform GENE STUDIO S5 at the Laboratory of Plant Genetics and Breeding, School of Agriculture, Aristotle University of Thessaloniki, Greece. Seven hypervariable regions (V2, V3, V4, V6, V7, V8, and V9) of the 16S rRNA gene were amplified and sequenced. Sequencing yielded 3,836,209 reads in total, with an average of 15,775 classified sequences per cultivar with an average read length of 234 bp. Accordingly, V3 provided the greatest sequencing depth that is the number of reads generated per sample (33% of total reads), followed by V6 and 7 (20%), V4 (18%), V8 (18%), V2 (10%), and, V9 (>0.1%) for profiling the bacterial diversity. The proportion of the unclassified reads ranged from the lowest of Limniona (60%) to the highest of Mavro (97%) with an average of 84% and were excluded from further analyses. Though the rarefaction curve was not parallel with the x-axis, the Good's coverage of bacterial diversity reached 99.9%, with the majority of microbial diversity being captured (Figure S1).  
A 463 bacterial distinct OTUs were detected excluding singletons (with a threshold of 97% similarity; Greengenes. 13_8); identifying 11 phyla, 22 classes, 42 orders, 98 families, and 204 genera (Figure 1).
## 3 Research Framework of Internet of Emergency Services
### Communication Protocols
By overcoming these challenges, IoES has the potential to transform emergency management and response, enabling emergency responders to make more informed decisions and coordinate their efforts more effectively, ultimately saving lives and reducing the impact of disasters and emergencies. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Protocol** & **Context of Use** & **Description of Use** \\ \hline [PERSON] (2022) [119] & 5G-IoT & Telemedicine and EMS & ID-based secure communication scheme to improve patient treatment and privacy preservation in medical emergency situations. \\ \hline [PERSON] et al., (2022) [120] & 5G, IoT, Fog Computing & Remote healthcare services & Proposed a holistic scheme using technical enablers for efficient and remote healthcare services, along with emergency health monitoring and response capability. \\ \hline [PERSON] et al., (2018) [116] & oneMEM & Emergency monitoring & Internet-based architecture using open protocols for wearable sensors to monitoring systems, achieving low latency and high autonomy at a lower cost. \\ \hline [PERSON] et al., (2019) [121] & WAVE, WiFi, 4G/LTE & IoV & Heterogeneous network architecture employing the Best Interface Selection algorithm to ensure reliable communication through the best available wireless interface, supporting seamless connectivity required for efficient data forwarding in V2I communication. \\ \hline [PERSON] and [PERSON] (2016) [122] & IoT & Smart emergency management system using IoT, sensors for real-time monitoring, a data analysis platform for decision making, and a communication system for alerting relevant parameters. \\ \hline [PERSON] et al., (2022) [43] & LoRA & Emergency response & UAV-aided emergency environmental monitoring system using a LoRa mesh networking \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of papers on communication protocols for emergency management and response.
## 2 Materials and Methods
### Phase Space Reconstruction Method  
It was in 1980 that Packard et al. [31] first tackled the problem of how to connect the phase space or state space vector \(\mathbf{x}(t)\) of the dynamical variables from one physical system to a possible time series \(\{s_{n}\}\) measured in any experiment.  
A time series is a sequence of scalar measures \(s_{n}\) of an observable, acquired at regular times \(\Delta t\). The different measures depending on the current state of the system,  
\[s_{n}=\psi(\mathbf{x}(n\Delta t))+\eta_{n}, \tag{3}\]  
where \(\psi\) represents an observable measurement function and \(\eta_{n}\) the measurement noise, which characterizes the random nature of the imprecision of the measure [32].  
An \(m\)-dimensional reconstruction of the \(\mathbf{s}_{n}\) state vectors, from scalar measurements, is given by:  
\[\mathbf{s}_{n}=(s_{n-(m-1)\tau},\dots,s_{n-\tau},s_{n}),\quad\tau\in\mathbb{Z} ^{+}. \tag{4}\]  
The time interval between adjacent coordinates of the state vector is \(\tau\Delta t\), and it is known as the lag or delay time. Formally, it has been shown that if \(m\) is higher than two times the capacity dimension of the attractor (\(\mathrm{dim_{C}}\)), a one-to-one correspondence between the reconstructed attractor and the real attractor is guaranteed, no matter how large the dimension of the original state space is [10, 11].
# Self-Piercing Riveted Joint of Vibration-Damping Steel and Aluminum Alloy
## 1 Introduction
[21] investigated the mechanical behavior of self-piercing riveted joints in multi-layers composed of aluminum (AA6111 and NG5754) and steel alloys (HSLA350) and found that the configuration of specimens had a considerable effect on the strength and failure mechanism of the multi-layer joint. [PERSON] et al. [22] studied the performance of various self-piercing riveted joints (tension, shear, and peel joint) in aluminum sheets (A5754) at nominal car-crash speeds, and found that all joint types showed interlock failure, and the performances of tension and shear joints were quietly reduced at high rate test conditions. As described above, many researchers have investigated the characteristics of SPR joints of various aluminum alloys and steels. However, to the best of our knowledge, no study has reported the joining of vibration-damping steel and aluminum alloy using SPR, thus far. The sandwich feature of the vibration-damping steel with a viscoelastic adhesive in between makes the dissimilar joining more complicated and difficult than general steel sheets. The adhesive layer induces inhomogeneous behavior in the SPR process. In addition, each outer steel sheet is less than 1 mm, which is thinner than the minimum thickness of the bottom sheet for the SPR joint. In this study, the geometrical characteristics and mechanical performance of the self-piercing riveted dissimilar joint between the vibration=damping steel and aluminum alloy are investigated for the first time. In particular, the effect of specimen configuration and die type on the geometrical and mechanical characteristics of the joint are studied. Moreover, the mechanical performances of the SPR joint between the vibration-damping steel and aluminum alloy are also compared with those of the SPR joint between SPFC590 DP (a skin material of the vibration-damping steel) and aluminum alloy.
# Cooperation-Assisted Spectrum Handover Mechanism in Vehicular Ad Hoc Networks
## 2 Related Work  
### IEEE 802.22  
Global wireless spectrum resources are gradually in short supply, and national telecommunication spectrum management agencies are actively discussing how to effectively use spectrum. A perceived wireless network is an intelligent wireless communication system that can self-detect the surrounding wireless environment to adjust the operating parameters of its wireless devices in real time to adapt to changes in the external wireless environment. Figure 1 shows the flow chart of the cognitive radio technology.  
The Institute of Electrical and Electronics Engineers (IEEE) established an 802.22 working group [5] in October 2004. Its main task is to develop and establish a set of wireless wide area network standards based on perceptual wireless network technology. This technology allows secondary users (SUs) to use idle TV broadcast spectrums for communication without affecting the primary user (PU). The same area may be covered by the signal range of multiple BSs. To avoid mutual interference, IEEE 802.22 requires SUs to be equipped with at least two antennas, one of which is a directional antenna to reduce interference when uploading, and the other is an omnidirectional antenna to detect  
Figure 1: The flow chart of the cognitive radio technology.  
spectrums that is not currently used by the PU. To dynamically use the idle spectrum, both the BS and the SU must be able to detect whether there is a PU. The BS scans the spectrums that may be idle and determines which spectrums are truly idle. Finally, the BS will select a spectrum from the idle spectrums as the operating spectrum, and select a spectrum as the backup spectrum. The operating spectrum is the spectrum that the BS mainly provides services, and the backup spectrum is the spectrum that the SU will switch to when a PU appears. The BS will periodically broadcast these messages to all SUs in the signal range of the BS. [PERSON] et al. [6] proposed an enhancing spectrum utilization scheme in cellular mobile networks by using cognitive radio technology. However, this scheme does not take the mobility situation into account.
# Advanced Optimization Techniques for Federated Learning on Non-IID Data
## 5 Experimental Results
### Results of Federated Learning with Data Sharing and Pre-Training on Augmented Data  
This section presents the performance improvements achieved through the implementation of the strategy combining data sharing and pre-training on augmented data, compared to the baseline federated learning model. This strategy was applied to the CIFAR-10 dataset, which was partitioned in a non-IID manner among 500 clients, as described in Section 3.4.  
We compare the performance of three different strategies: the baseline federated learning model with a fixed learning rate, the model using the Cyclical Learning Rate (CLR) strategy, and the model that combines the data sharing and pre-training on augmented data strategy with the CLR strategy.  
The graph in Figure 12 displays the evaluation accuracy across 200 communication rounds for the three different federated learning strategies applied to the CIFAR-10 dataset with non-IID data distribution. The blue line represents the performance of the FedAvg algorithm with a fixed learning rate of 0.02. The orange line illustrates the performance of FedAvg with a cyclical learning rate, where the learning rate ranges between 0.02 and 0.2. Finally, the green line depicts the performance of the FedAvg algorithm using the data sharing and pre-training on the augmented data strategy, in which the cyclical learning rate policy (min_lr = 0.02 and max_lr = 0.2) has also been applied.  
Table 10 highlights the differences in evaluation accuracy between these three strategies per 10 rounds of training.  
Figure 12: CIFAR-10 Fixed LR vs. CIFAR-10 CLR vs. CIFAR-10 CLR + PreTrained.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Max Accuracy (\%)** & **Rounds to 37\%** & **Speedup (Rounds)** \\ \hline cifar\_sgd\_fixed & 37.8 & 176 & x1 \\ \hline cifar\_sgd\_clr & 48.8 & 90 & x1.95 \\ \hline \end{tabular}
\end{table}
Table 9: Maximum accuracy and rounds to 37%.  
As shown in Table 11, the model trained with the data sharing and pre-training strategy on augmented data outperformed both the CLR model and the model with a fixed learning rate, achieving higher accuracy and faster convergence. This strategy significantly reduced the number of rounds required to achieve the target accuracy compared with the other approaches, demonstrating a significant improvement in training speed and efficiency.
## 8 Complexity Analysis of the Proposed Approach
### **Lyrebird Optimization Algorithm-CatBoost Classifier (LOA-Cb-C):**  
**LOA-Cb-C** offers a competitive time complexity of O(T \(\times\) N \(\times\) F +T \(\times\) n \(\times\) f \(\times\) D), which is efficient because of the robust optimization provided by the LOA and the fast training and inference of CatBoost.  
Thus, the LOA-Cb-C combination strikes a favorable balance between computational efficiency and classification performance, making it a suitable choice for large-scale, real-time IoT-based WSN applications.
## 4 Experiments
### Training  
For training the proposed model, it was found that the following hyper-parameters led to the best performance: \(batch\_size=32\), \(epochs=100\), \(validation\_split=0.05\), \(learning\_rate=5\times 10^{-4}\), \(decay\_step=100\),000, \(decay\_rate=0.98\), and \(\epsilon=1\times 10^{-7}\). The number of trainable parameters was 582,657. The logarithmic hyperbolic cosine error loss function [35] and adaptive moment estimation (Adam), a combination of gradient descent with momentum and root mean square propagation (RMSprop) algorithms [36], showed their worthiness to calculate the loss and optimisation for the gradient descent. Adam demonstrated its efficiency in dealing with several parameters using less memory. The number of output filters for the three 1D-CNN layers were 64, 32, and 16, respectively, whereas, for the three 1D MaxPooling layers, a pool size of two provided the best results.  
Figure 6 allows visual comparison of training loss among the different models under study. It shows that the proposed 1D-CNN-LSTM model and the other baselines under consideration are free of under-fitting and over-fitting issues and that the models accurately match the data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attributes** & **Values** \\ \hline Test Statistics & 2.722 \\ \(p\)-Value & 0.01 \\ Critical Values & (’10\%’: 0.119, ’5\%’: 0.146, ’2.5\%’: 0.176, ’1\%’: 0.216) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the KPSS test on traffic speed data.  
Figure 7 illustrates the validation loss comparison among the different models under study. It reveals that the proposed 1D-CNN-LSTM, GRU and LSTM models fit new data well. However, for the CNN-GRU-based model, the validation function moves noisily. The CNN-GRU model struggled to model these examples since the validation data did not represent the training data. On the other hand, the proposed CNN-LSTM model performed well even though the validation dataset is more challenging to predict than the training dataset.
###### Abstract  
In this work, we put forward and demonstrate a bi-direction free-space visible light communication (VLC) system supporting multiple moveable receivers (Rxs) using a light-diffusing optical fiber (LDOF). The downlink (DL) signal is launched from a head-end or central office (CO) far away to the LDOF at the client side via a free-space transmission. When the DL signal is launched to the LDOF, which acts as an optical antenna to re-transmit the DL signal to different moveableRxs. The uplink (UL) signal is sent via the LDOF towards the CO. In a proof-of-concept demonstration, the LDOF is 100 cm long, and the free space VLC transmission between the CO and the LDOF is 100 cm. 210 Mbit/s DL and 850 Mbit/s UL transmissions meet the pre-forward-error-correction bit error rate (pre-FEC BER = 3.8 \(\times\) 10\({}^{-3}\)) threshold.  
visible light communication (VLC); optical wireless communication (OWC); light diffusing optical fiber (LDOF); laser diode (LD); light-diffusing optical fiber (LDOF) IEEEexample:BSTcontrol
## 2 Message Replication in OppNets for Sensed Data Collection
### Current State of Message Replication in OppNets  
Although message replication in OppNets has been widely investigated, the existing replication techniques are not designed with the unique characteristics of Smart City scenarios in mind. Hence, in this section, we investigate the impact of relevant OppNet characteristics on existing message replication techniques. OppNet movement scenarios and simulation set-up are presented in Section 2.4.1, with which experiments are conducted to analyse the performance of existing message replication techniques in Section 2.4.2.