esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Beamforming Optimization with AN
### Beamforming Optimization with AN in the MISO SWIPT CR-NOMA Network
Use the scalar factors to transform the candidate precoder vectors into feasible solutions to problem (11): \(\mathbf{v}_{l}^{i}=\sqrt{\tau_{\text{SU},l}^{i}}\mathbf{Q}_{\text{SU},l} \Lambda_{\text{SU},l}^{1/2}x_{\text{SU},l^{\prime}}^{i}\), \(\mathbf{w}_{m}^{i}=\sqrt{\tau_{\text{EH},m}^{i}}\mathbf{Q}_{\text{EH},m}\Lambda_{ \text{EH},m}^{1/2}x_{\text{EH},m}^{i}\), and \(\mathbf{f}^{i}=\sqrt{\tau_{f}^{i}}\mathbf{Q}_{f}\Lambda_{f}^{1/2}\kappa_{f}^{i}\). Define \(ObjF_{i}=\sum\limits_{l=1}^{L}\left\|\mathbf{v}_{l}^{i}\right\|^{2}+\sum\limits_ {m=1}^{M}\left\|\mathbf{w}_{m}^{i}\right\|^{2}\). if\(ObjF_{i}<\text{Pow}_{\min}\)then \(\text{Pow}_{\min}=ObjF_{i}\), \(\left\{\mathbf{v}_{l}^{*}=\mathbf{v}_{l}^{j},\mathbf{w}_{m}^{*}=\mathbf{w}_{m}^{ i}\right\}\), \(\mathbf{f}^{*}=\mathbf{f}^{i}\)  end if  end for Result: The precoder vectors: \(\left\{\mathbf{v}_{l}^{*},\mathbf{w}_{m}^{*}\right\},\mathbf{f}^{*}\)
```  
**Algorithm 1**The proposed algorithm based on SDR and Gaussian randomization to solve problem (11).
# Design of Multi-Mode Antenna Array for Use in Next-Generation Mobile Handsets
## 3 Characteristics of the Single-Element/Multi-Band PIFA Resonator
As evident from the figure, there is very little impact on the first resonance, while the second and third resonant frequencies are influenced and tuned to higher frequencies. Figure 4b shows that L\({}_{4}\) (unlike W\({}_{3}\)) has a significant impact on the first resonance frequency and little effect on the second and third resonance frequencies. Figure 4c shows the effects of \(W_{2}\) (length of L-shaped strip) on the S\({}_{11}\) of the antenna. It can be observed that as \(W_{2}\) decreases from 12.65 to 15.65 mm, the first and third resonances at 2.6 GHz and 5.8 GHz shift up to higher frequencies, while very little variation is observed at the middle resonance frequency (3.6 GHz). The antenna S\({}_{11}\) characteristic of the antenna at different frequencies is also highly dependent on the length of the open-loop resonator (W). As shown in Figure 4d, changing the value of W affects all three resonances of the antenna at different operation bands. According to the obtained results, it can be calculated that the antenna frequency response in all operation bands is very flexible to be tuned to lower or upper frequencies. In addition, its impedance matching can be also affected by changing the parameter values [57; 58]. Figure 4: The S\({}_{11}\) characteristics for different sizes of (**a**) W\({}_{3}\), (**b**) L\({}_{4}\), (**c**) W\({}_{2}\), and (**d**) W.  
Figure 3: Surface current densities at (**a**) 2.6 GHz, (**b**) 3.6 GHz, and (**c**) 5.8 GHz. The fundamental radiation characteristics of the modified PIFA design including the radiation efficiency (R.E. ), total efficiency (T.E.) and maximum gain (M.G.) are studied in Figure 5. In theory, the radiation and total efficiencies are related according to  
\[e=e_{r}e_{cd} \tag{1}\]  
The antenna gain can be calculated using the radiation efficiency and the directivity as follows:  
\[G_{0}(dB)=10\log(e_{cd}D_{0}) \tag{2}\]  
where \(e_{0}\) is the total efficiency, \(e_{r}\) is the reflection (mismatch) efficiency = (1\(-\)\(|\Gamma|\)2), \(e_{cd}\) is the radiation efficiency and \(D_{0}\) is the antenna directivity [59]. As can be observed from Figure 5, the antenna provides better than 40% and 65% radiation and total efficiencies over the three operation bands. In addition, the maximum gain of the design varies from 2.5 to 4.5 dBi.
## 3 Simulation Results and Discussion
### Reconfiguration without DGs
#### 3.1.2 Standard IEEE 69-Bus Distribution System  
The initial configuration of the standard IEEE 69-bus system is provided in Figure 5 followed by the performance indicator listed in Table 2. For IEEE 69-bus system, both SSOE and MSSOE has found the same result but different computation time; the MSSOE is the fastest one. The initial active power loss is 224.95 kW and the minimum voltage node is 65 with 0.9092 pu. After reconfiguration, the power loss is reduced to 99.69 kW and the minimum voltage node is 61 with 0.9428 pu. The voltage profile is also improved with the reconfiguration as provided in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Items** & **Base Case** & **SPS [30]** & **SSOE [4]** & **MSSOE** \\ \hline Tie-switches & 69,70,71,72,73 & 14,55,61,69,70 & 13,57,61,69,70 & 13,57,61,69,70 \\ Power loss (kW) & 224.95 & 99.59 & 99.69 & 99.69 \\ Loss reduction (\%) & N/A & 55.74 & 55.68 & 55.68 \\ V\({}_{\rm min}\) (pu) & 0.9092 & 0.9428 & 0.9428 & 0.9428 \\ Voltage node & 65 & 61 & 61 & 61 \\ Computation time (s) & N/A & N/A & 0.4797 & 0.4325 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation result of reconfiguration for IEEE 69-bus without DGs.  
Figure 4: Voltage profile of IEEE 33 bus before and after the reconfiguration without distributed generations (DGs).  
Figure 5: Single-line diagram of IEEE 69-bus distribution system.
# Seismic Response of Aeolian Sand High Embankment Slopes in Shaking Table Tests
## 2 Shaking Table Tests
### Similarity Laws and Materials  
The dimensions of the shaking table are 2.2 \(\times\) 2 m (length \(\times\) width). Its bearing capacity is 40 kN with a loading frequency range of 0.01-500 Hz. The waveforms of the shaking table can be designed as sinusoidal waves, random waves, and earthquake excitations. The maximum acceleration and amplitude is 27.7 m/s\({}^{2}\) and 100 mm, respectively. Shaking table test models were designed according to a prototype of a aeolian sand high embankment--with a compaction degree of 96%--which are located at the northern part of the Mu Us Desert. Based on the dimension of prototype and the carrying capacity of the shaking table, a model size similarity ratio of 1:30 was chosen. The similarity law for the aeolian sand high embankment can be deduced based on the Buckingham \(\pi\) theory [39; 40; 41]. Considering the importance of the dynamic shear modulus to the horizontal dynamic response of Aeolian sand embankment as mentioned above, it is necessary to determine the precise dynamic shear modulus constant before making the model. [PERSON] [42] proposed an empirical formula as shown in Equation (2) to calculate the maximum dynamic shear modulus. According to this empirical formula, the similarity constant of dynamic shear modulus can be calculated as shown in Equation (3) for this research. To select a suitable model sand, a series of dynamic triaxial tests were conducted. The test samples were prepared by adding different contents of quartz sand (30% and 50%) into prototype sand under different degrees of compaction (90% and 86%) using vacuum saturation and isobaric consolidation. The design of dynamic triaxial tests and test results are shown in Figure 3.  
\[G_{max}=AP_{a}{(\sigma^{\prime}/P_{a})}^{1/2},\ A=625\frac{ OCR^{k}}{0.3+0.7e^{2}} \tag{2}\]  
where \(P_{a}\) represents the atmospheric pressure, \(\sigma^{\prime}\) represents the initial effective consolidation stress, OCR represents the overconsolidation ratio, and \(e\) represents the void ratio.  
\[C_{G}=C_{\rho}^{1/2}C_{l}^{1/2}\ C_{l}=30 \tag{3}\]  
Figure 2: Failure mechanism of prototype sand under horizontal accelerations.  
The results show that the average dynamic shear modulus similarity constant \(\overline{C_{G}}\) of the specimens (after adding 50% quartz sand with a compaction of 86%) is 5.71. The density similarity constant \(C_{\rho}\) of them is 1.02 according to laboratory tests. As above mentioned, \(C_{l}=30\). Then, according to Equation (3), \(C_{G}\) is calculated as 5.48. It can be seen that \(C_{G}\) is close to \(\overline{C_{G}}\)(5.71). Therefore, sand with a compaction of 86% and a quartz sand content of 50% was determined to be the model material. Using the geometry size, sand density, and gravity acceleration as control variables, the similarity laws of aeolian sand high embankment slope are shown in Table 1.
## 2 Background on Computing Paradigms
### Edge-Related Aspects
The Edge architecture allowed to switch more delay intolerant applications closer to the computation demanders, e.g., Augmented/Virtual/Mixed Reality (AR/VR/MR) gaming, cellular offloading, etc., all together following the proximity-driven nature of the paradigm [27]. Generally, there are two approaches to the proximity between the Edge and user's equipment: physical and logical proximity. Physical proximity refers to the exact distance between the top segment of data computation and user equipment. Logical proximity refers to the count of hops between the Edge computing segment and the users' equipment. There are potential occurrences of congestion because of the lengthy route caused by multiple hops, leading to increased latency issues. To avoid queuing that can result in delays, logical proximity needs to limit such events at the back-haul of the computing network systems. Despite the shortcomings of the normal Cloud paradigm innovations to match up with great demands, given lower energy level, real-time, and in particular security and privacy aspects, the Edge paradigm is not considered a substitute for the Cloud paradigm. Edge and Cloud paradigms are known to assist each other in a cordial manner in several situations. The Cloud and Edge paradigms cooperate in some network areas, including autonomous cars, industrial Internet, as well as smart cities, offices and homes. Importantly, Edge and Cloud paradigm collaboration offers many chances for reduced latency in robust software such as autonomous cars, network assets of companies, and information analysis on the IoT [28]. Nevertheless, Edge operation is executed through supported capabilities from several actors. Cellular LTE, short-range Bluetooth Low Energy (BLE), Zigbee, and Wi-Fi are various technologies that create connectivity by linking endpoint equipment and nodes of the Edge computing layer. There is great importance for access modalities as it establishes the endpoint equipment bandwidth availability, the connection scope, and the various device type assistance rendered [29].
###### Abstract  
Rapid urbanization presents significant challenges in energy consumption, noise control, and environmental sustainability. Smart cities aim to address these issues by leveraging information technologies to enhance operational efficiency and urban liveability. In this context, urban sound recognition supports environmental monitoring and public safety. This study provides a comparative evaluation of three machine learning models--convolutional neural networks (CNNs), long short-term memory (LSTM), and dense neural networks (Dense)--for classifying urban sounds. The analysis used the UrbanSound8K dataset, a static dataset designed for environmental sound classification, with mel-frequency cepstral coefficients (MFCCs) applied to extract core sound features. The models were tested in a fog computing architecture on AWS to simulate a smart city environment, chosen for its potential to reduce latency and optimize bandwidth for future real-time sound-recognition applications. Although real-time data were not used, the simulated setup effectively assessed model performance under conditions relevant to smart city applications. According to macro and weighted F1-score metrics, the CNN model achieved the highest accuracy at 90%, followed by the Dense model at 84% and the LSTM model at 81%, with the LSTM model showing limitations in distinguishing overlapping sound categories. These simulations demonstrated the framework's capacity to enable efficient urban sound recognition within a fog-enabled architecture, underscoring its potential for real-time environmental monitoring and public safety applications.  
IoT; fog computing; urban sound recognition; deep learning; smart city Article
## 5 Conclusions  
The main difficulty of stress detection for operators in nuclear power plants is obtaining the stress level in real time without disturbing their work. We induced stress by asking participants to complete a task in a time-limited situation that requires high cognitive resources using a nuclear power plant simulator. Our results showed that physiological indicators and voice features, such as MFCCs, change with the stress level. The contribution of our work can be summarized in two aspects. Firstly, we introduced a non-intrusive way of using voice features to detect stress. At the same time, voice features can be collected in real time. Secondly, we demonstrated the feasibility of speech recognition stress by introducing a machine learning method. The established neural network model can effectively evaluate the stress state and be used as a prototype of a speech stress recognition system in nuclear power plants. Future investigations can develop a model with increased accuracy and high generalization ability using multi-modal data.  
Writing-Original draft preparation, J.C.; Methodology, J.C., K.X. and Z.Y.; Data Curation, J.C. and K.X.; Formal analysis, B.W. and S.M.; Visualization, S.M.; Investigation, S.M.; Conceptualization, Z.Y.; Writing-Reviewing and Editing, Z.Y.; Supervision, Z.Y. and Y.S.; Project administration, Y.S.; Resources, Y.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China under Grant [31900768]; Youth Innovation Special Project of Basic Scientific Research Foundation of Zhejiang Sci-Tech University under Grant [2020Q046]; Zhejiang Provincial General Scientific Research Projects Fund of China [Y202147662]; and the NSF of China under Grant [12022112].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare that they have no conflict of interest.
## 5 Rational Approaches to the Biotechnological Production of TIAs
### Metabolic Engineering to Improve the Production of TIAs
#### 5.2.1 Overexpression of Genes which Regulate TIA Biosynthesis  
Some studies were focussed on cell and tissue genetic engineering in _C. roseus_, resulting in higher yields of some TIAs [6,126] being _TDC_ and _STR_ the most overexpressed genes. In fact, _STR_ overexpression in a transgenic _C. roseus_ culture resulted in a significant increase of TIAs, reaching levels of 123 mg/L [86] (Table 1). Moreover, loganin feeding experiments provoked an increase in total TIAs of 300 mg/L [86]. Similarly, _TDC_ overexpression did not increase [87] or slightly enhanced total TIAs levels (75 mg/L, [86]). However, when the precursors loganin and secologanin, were added to cell lines overexpressing _TDC_, the production of serpentine, catharanthine and strictosidine increased until 125-fold, resulting in a total TIA production of 625 mg/L [87] (Table 1).  
In a similar way, _C. roseus_ hairy root cultures overexpressing _DAT_ genes accumulated hornhammericine until 4-fold more than the wild-type (Table 1, [88]) while transgenic hairy roots overexpressing the gene encoding a Prx, accumulated 5- and 3-fold more serpentine and ajmalicine than their respective controls (Table 1, [89]). Moreover, as a result of overexpressing _geranyl diphosphate synthase_ (gene encoding an enzyme that catalyzes the first reaction of terpenoid pathway) in _C. roseus_ leaves, an increase in vindoline (Table 1) was observed [91]. Other study focused on the co-overexpression of several key genes including those monoterenoid and indole pathway, as well as _G10H_, alone or in combination in hairy root cultures of _C. roseus_[115], resulted in a significant increase in ajmalicine, lochnericine, and tabersonine (Table 1). This experiment pointed out the need for overexpressing multiple genes within the TIA pathway to obtain significant results.  
On the other hand, two genes involved in early steps of TIA biosynthetic pathway, _TDC_ and _STR_, have been introduced in other cell cultures, specifically tobacco and _M. citrifolia_[92]. In these conditions, an increase of strictosidine was observed in both tobacco and _M. citrifolia_ cell cultures fed withryptamine and secologanin reaching levels of 21.2 and 5.3 mg/L, respectively (Table 1). Similarly, these two genes were overexpressed in _C. officinalis_ hairy root cultures resulting in high levels of strictosidine (1.95 mg/g FW) [93]. Another strategy is the bioproduction of TIA using microorganisms like _S. cerevisiae_ and _Escherichia coli_[94]. Thus, transgenic yeasts overexpressing _STR_ and _SGD_ were able to produce a high amount of strictosidine (2000 mg/L) when these cultures were fed with tryptamine and secologanin (Table 1) while a transgenic _E. coli_ overexpressing _STR_ was able to transform strictosidine in vallesiachotamine and isovallesiachotamine, two TIAs found in plants which belong to the family Apocynaceae [127].
# Exploiting Autoencoder-Based Anomaly Detection to Enhance Cybersecurity in Power Grids
## 3 AE-GRU Anomaly Detection Framework
### An Overview of the IEC-60780-5-104 Protocol  
The IEC 60870-5-104 (IEC 104) protocol is a fundamental component of SCADA networks, which is pivotal in managing and overseeing critical infrastructures such as power stations [44]. Designed to enable seamless communication between control centers and substations, IEC 104 facilitates real-time monitoring, control, and data acquisition within power grid systems. Its adoption has become widespread due to its ability to ensure the reliable and efficient transmission of data, essential for maintaining the stability and functionality of power distribution networks. Unlike its predecessor, the IEC 60870-5-101 protocol, IEC 104 operates at higher speeds, allowing for faster and more responsive communication between substations and control centers. This enhanced speed is crucial for meeting the demands of the modern power grid operations, where rapid decision-making and real-time data exchange are paramount.  
The architecture of the IEC 104 protocol follows a client-server model, where substations act as servers and control centers act as clients. This structure enables bidirectional communication, facilitating the exchange of control commands, status information, and measurement data between the different components of the power grid infrastructure [45]. IEC 104 relies on standard networking protocols such as the Transmission Control Protocol (TCP) and Internet Protocol (IP) for data transmission. This reliance on widely accepted networking standards ensures the compatibility with the existing network infrastructure and simplifies the integration into modern communication networks. In addition to defining the communication procedures and data formats, the IEC 104 protocol also specifies various data types tailored to the specific requirements of power grid applications. These data types encompass a wide range of parameters, including analog values, binary states, and control commands, allowing for the comprehensive monitoring and control of the power grid operations.  
While the IEC 104 protocol offers robust communication capabilities, it is not immune to cybersecurity threats [46]. As the power grid systems become increasingly interconnected and digitized, the protocol's vulnerabilities may be exploited by malicious actors seeking to disrupt operations or compromise the sensitive infrastructure. Therefore, understanding the intricacies of the IEC 104 protocol is essential for developing effective cybersecurity strategies and implementing robust anomaly detection techniques to safeguard the critical power grid assets against potential cyberattacks.
## 5 Discussion
### Maximum Crack Width of the Lining  
Figure 16a illustrates the elastic modulus and gas pressure ranges where the crack control requirements are satisfied. The vertical and diagonal shaded areas represent the applicable regions for monolithic and segmented linings, respectively. Under strict crack control with a maximum crack width limit of 0.3 mm, the required elastic modulus of the surrounding rock increases sharply as the internal pressure gradually increases. This indicates a nonlinear relationship between internal pressure and rock mass elasticity when using the maximum crack width as the limiting factor. Higher internal pressure leads to increasingly stringent rock mass requirements. Notably, when the maximum storage pressure exceeds 12 MPa, very few surrounding rock conditions are suitable to meet the crack control requirements for the monolithic lining.  
However, when two preset seams are used, the required elastic modulus of the surrounding rock is reduced to 4.5 GPa to ensure that the lining meets the crack control criteria. This provides essential support for constructing CAES workings with pressures exceeding 12 MPa in the surrounding rock of fair to extremely hard quality. Therefore, it is recommended that when designing a monolithic lining structure with crack control, the maximum storage pressure should not exceed 12 MPa. For segmented lining structures with two preset seams, the maximum pressure in fair-quality rock mass should not exceed 15 MPa. Higher pressures can be designed for better-quality rock, with a maximum pressure of 17 MPa in extremely hard-quality rock mass. The use of preset seams in the lining significantly broadens the range of suitable rock conditions for meeting the crack control requirements under high internal air pressure.  
In this study, the crack width limit of 0.3 mm was adopted based on the Design Specification for Hydraulic Concrete Structures [10]. This limit is primarily used to prevent water seepage through structural cracks. However, in CAES workings, water seepage is not a concern. The primary objective is to ensure the integrity of the sealing layer and avoid failure due to crack-induced deformation. As a result, the 0.3 mm limit is considered conservative for CAES applications. The allowable crack width in such cases can be relaxed to some extent. However, the appropriate upper limit remains under investigation. Determining this limit is one of the key targets of our ongoing research.  
Figure 16: Relationship between internal pressure and the rock mass grade: (**a**) different lining types and (**b**) different preset seam opening displacements.
## 2 System Model and Problem Formulation  
### System Model  
As shown in Figure 1, for radio frequency signals collected from one particular emitter, the identification phase in the SEI system consists of three main steps: data collection, feature extraction, and classification. Considering that it is difficult to collect enough reliable labeled data for driving the training process of RFFs extractor and classifier in real-world scenarios, using unlabeled datasets to construct a self-supervised pretraining task is an effective method. In this paper, it is considered that in the application of real-world scenarios, the datasets of the pretraining phase are unlabeled, and self-supervised contrastive learning is applied to the pretraining task of the RFFs extractor. Subsequently, the pretrained extractor is fine-tuned together with the classifier to tune parameters with the support of limited labeled samples to obtain an end-to-end mapping from input samples to categories.