esa-sceva/satcom-chunk-collection huggingface dataset:
# An Ultra-Wideband Vivaldi Antenna System for Long-Distance Electromagnetic Detection
## 4 Conclusions  
In this article, an UWB Vivaldi antenna system for long-distance electromagnetic detection has been proposed. In the frequency range of 2.5-4 GHz, the transmitting antenna was an ultra-wide bandwidth beam Vivaldi antenna with HPBW above 90\({}^{\circ}\) in the frequency range 2.5-5 GHz. The receiving antenna was an ultra-wideband narrow-beam Vivaldi antenna array. The array unit maintained HPBW less than 70\({}^{\circ}\) in the frequency range between 2.5 GHz and 8 GHz, and the receiving antenna was a eight-element Vivaldi array with HPBW less than 10\({}^{\circ}\) in the frequency range of 2.5-4 GHz. It was verified by experiments that after detecting their frequency and time-domain behaviors, the detection system can achieve measurement covering a radius of 30 m.  
Conceptualization, J.R.; methodology, H.F.; software, Z.Y., and J.R.; validation, Q.T., and H.F.; formal analysis, J.R.; investigation, Y.X.; data curation, J.R., H.F. and Q.T.; writing--original draft preparation, J.R. and Z.Y.; writing--review and editing, J.R. and X.Z.; supervision, X.Z.; project administration, Y.X.; funding acquisition, Q.T. and J.R. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Natural Science Foundation of Jiangsu Province under Grant BK20180360, the Aero-Science Fund of China under Grant 201926069001, and the National Defence Basic Research Program of China under Grant No. JCKYS2020 DC404. And we are grateful for the assistance of the reviewers and editors.  
The data used to support the findings of this study are available from the corresponding author upon request.  
There is no conflict of interest regarding the publication of this paper.  
Figure 13: Actual and tested position map of the target (Unit: m).
# Assessment of Fatigue Life and Failure Criteria in Ultrasonic Testing Through Thermal Analyses
###### Abstract
* Deriving S-N curves independent of temperature effects. * Applying fatigue failure criteria models to ultrasonic frequency test results to identify the model that best aligns with experimental data and determine the most suitable parameters for representing fatigue damage in VHCF testing.
## 2 Literature Review
### Discrete vs. Dimensional Emotion Representation
#### 2.2.2 Dimensional Emotion Representation  
Dimensional emotion theory represents emotions as points along continuous dimensions rather than discrete categories. One of the earliest dimensional models introduced the dimensions of pleasure-displeasure, tension-relaxation, and activity-passivity, offering a flexible framework for capturing the complexity of emotions [30].  
The Circumplex model, depicted in Figure 2a, maps emotions along two axes: valence (pleasure-displeasure) and arousal (activation-deactivation). This model represents emotions in a circular space, where each emotion occupies a specific position based on its  
Figure 1: [PERSON]’s Wheel of Emotion.  
valence and arousal levels. For example, happiness is located in the high valence and high arousal quadrant, while sadness resides in the low valence and low arousal quadrant [31].  
The PAD model (Pleasure, Arousal, Dominance), shown in Figure 2b, extends the Circumplex model by incorporating a third dimension--dominance. This three-dimensional representation provides a richer understanding of emotions by accounting for the degree of control or influence an individual feels in a particular emotional state. For instance, anger is characterized by high arousal, low pleasure, and high dominance, whereas fear exhibits high arousal, low pleasure, and low dominance [32].  
The integration of two-dimensional and three-dimensional models allows for a more comprehensive understanding of emotional states and their interrelationships, making them particularly valuable for SER applications.
# Photocatalytic Degradation of Selected Non-Opioid Analgesics Driven by Solar Light Exposure
## 3 Results
### Photocatalysis and Photoelectrocatalysis--Systems with Tungsten Oxide and Iron Oxide
#### 3.2.1 Application of WO\({}_{3}\) and Fe\({}_{2}\)O\({}_{3}\) Used Individually
After 30 min, another additional signal appeared on the chromatogram, with a retention time of 3.2 min, the surface area of which increased intensively with a further extension of the photocatalysis time. During the process, the intensity of the naproxen peak steadily decreased (Figure S2). In the final stage, 70% of the drug was degraded (Figure 3b). It should be mentioned that in the photolytic tests (without photocatalyst) under the same measurement conditions, a 45% decrease in the content of naproxen was observed. Thus, the decomposition of the analyte in the presence of WO\({}_{3}\) proceeds much faster. Comparison of these results with those present in Figure 2 led to the conclusion that an increase in the photocatalyst active surface significantly enhances the efficiency of the decomposition of studied analytes. The photolytic degradation of diclofenac with Fe\({}_{2}\)O\({}_{3}\) under solar light was conducted in neutral media due to the limited solubility of this compound in an acidic environment. \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) (hematite) has an advantage over the other conventional photocatalysts, like TiO\({}_{2}\) or ZnO, of a low band gap (\(\sim\)2.2 eV) and using visible light for its photocatalytic reactions [53]. When the diclofenac solution was irradiated, a new chromatographic peak appeared with a retention time of 3.1 min, similar to that observed during the photolysis process (Figure 1c). However, using a solar lamp assisted with Fe\({}_{2}\)O\({}_{3}\), only about 20% of diclofenac was degraded (by UV, it was \(\sim\)15%) (Figure S3). It suggests that the observed catalytic effect was minimal.
## 2 Background  
### Wireless Sensor Networks  
A WSN is a wireless network for distributed devices, using sensors to monitor cooperatively the physical or environmental conditions, such as temperature, sound, vibration, pressure, movement, etc. Recent advances in wireless communication and electronics have enabled the development of low-power, low-cost multifunctional sensor nodes. These sensors can detect, process, and send/receive information over short distances. In general, they are present in a dense form, to collect high precision data and perform other complex tasks related to both the collection and dissemination of information.  
One of the sensor nodes' main characteristics is their energy limitation, computational resource limitation, and access to the network. However, it is necessary for WSNs to be scalable, robust to failure, self-organizing, and to have a long service life across the network [1]. Therefore, the protocols and the application algorithm involved must be light, resulting in a longer network lifetime without compromising its performance.  
Figure 1: A real monitoring scenario for a coffee plantation with three different topologies, which are tree, circular, and grid topologies.
## 3 SDN Saturation Attacks Overview  
In SDNs, a table miss occurs upon the arrival of a new packet which the data plane does not know how to handle, i.e., there is no matching flow entry installed in the flow table of the switch. As a result, a Packet-In message is generated. Depending on the switch memory status, this Packet-In may comprise the header of the received packet when the switch memory is not full, or the whole packet is encapsulated in the Packet-In message and passed to the controller if the switch memory is full. After receiving the Packet-In message, the controller decides the fate of the table-miss packet by sending Packet-Out and Flow-Mod messages to install flow rule(s) in the flow table of the OpenFlow switch. This reactive packet processing exposes a security vulnerability. An attacker may exploit table miss to exhaust the control and data planes' computational resources (i.e., CPU and RAM) and flood the OpenFlow channel by launching a data-to-control plane attack. The saturation effect of this attack is caused by generating and sending vast amounts of Packet-In messages to the SDN controller which drastically consumes the controller's computational resources. Figure 1 shows the CPU utilization of the SDN controller under the UDP flooding attack. Before the attack started, the CPU utilization of the control plane ranges from (20-40%). When the UDP flooding attacks are triggered, the CPU utilization reached up to 90% since the controller is overwhelmed by processing the malicious Packet-In packets.  
Upon a successful data-to-control plane flooding attack, a large number of Packet-Out and Flow-Mod messages are sent by the controller, which leads to a control-to-data plane attack. Therefore, the targeted switch(s) flow tables are filled with fake flow rules, which prevents the benign flow rules from being installed. At this point, the buffer of the victim switch is consumed, and unable to accommodate legitimate new packets. This also exhausts the OpenFlow channel bandwidth, which disables the delivery of OpenFlow messages between the controller and the OpenFlow switches. Figure 2 depicts the impact of the UDP saturation attack on the OpenFlow connection channel bandwidth. Before launching the attack, the average bandwidth of the OpenFlow connection channel is equal to 3.5 Gbps. When the UDP attack targets the SDN environment, the bandwidth of the OpenFlow connection channel drops to 0 Gbps due to the vast amount of OpenFlow messages (e.g., Packet-In and Packet-Out) that are generated by the malicious table miss packets.  
To conduct saturation attacks in the SDN network, the attacker can employ the TCP-SYN, UDP, ICMP, IP-Spoofing, and TCP-SARFU flooding attacks, or their combinations  
Figure 1: Control plane CPU utilization under UDP flooding attack.  
(i.e., hybrid saturation attacks) to generate a large number of Packet-In messages. To defend SDNs against these attacks, it is highly important to detect both known and unknown saturation attacks.
# Improved Neural Differential Distinguisher Model for Lightweight Cipher Speck
## 2 Preliminaries  
### Brief Description of Speck Cipher  
The lightweight family of ARX block ciphers Speck designed by the NSA [4] to build a cipher efficient in software implementations in Internet of Things (IoT) devices, which adopts a very simple Fesitel structure combining bitwise XOR operation(\(\oplus\)), modular addition (\(\boxplus\)) and bit-wise rotation. In ref. [4], various versions of Speck are presented that differ from the number of rounds (r), the block size (n), and the key size (m). Generally, Speckn/m will denote Speck with n bits block size and m bits key size. This paper will focus mainly on Speck32/64 and abbreviate it as Speck32.  
Let \((C\_l,C\_r)\) be the input of \(i\)-round of Speck32. Then the output of \(i\)-round is \((C_{l},C_{r})\), and \((C_{l},C_{r})\) is computed as follows:  
\[C_{l}:=((C\_l\gg\alpha)\boxplus C\_r)\oplus K\]  
\[C_{r}:=(C\_r\ll\beta)\oplus C_{l}\]  
The round function of Speck is shown in Figure 1 where \(k_{l}\) represents the subkey at \(i\)-round and where \(\alpha=7\), \(\beta=2\).
# Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning
## 3 Methodology
### Model Training and Experimental Setup  
The models trained in this study were built using PyTorch 2.2.0 and an NVIDIA RTX 3070 GPU with 8 GB of RAM for this experimental setup (NVIDIA, Santa Clara, CA, USA). The Flower framework was used to construct the FL simulation. Computer resources were distributed evenly across each FL client for training (performed through customization of the Flower framework). One epoch per client selection was chosen to limit overfitting on local data and keep each client from diverging excessively from the global model. The experimental setup also consists of a batch size of sixteen and a learning rate of 0.001, working best with the Adam optimizer. The researchers conducted five rounds of FL, which worked well enough with the project dataset. More rounds are often needed for more extensive or more diverse datasets. Due to computational restrictions, this study used four FL clients to keep the setup manageable despite real-world deployments frequently involving many more clients. Finally, with the previously mentioned 3402 images used in this study's dataset, these data were split evenly across all 4 FL clients in the architecture. These parameters were chosen through intense simulations.
## 3 Hypotheses  
This study aimed to examine how perceived diversity in social recommendations affects users' attitudes in the context of SVOD. Figure 1 shows the hypotheses (H1s and H2s) of the relationships between perceived diversity, information quality, perceived playfulness, and intention to use.  
Figure 1: Hypotheses 1s and 2s.  
Social interactions can improve user competence by providing various types of information [40]. Diverse recommendations help users make new and informative discoveries [48]. Positive emotions accompanied by diversity lead to user enjoyment [40; 43]. We derived the following hypotheses (H1a and H1b) in the SVOD domain:  
**Hypothesis 1a (H1a).**_Users' perceptions of diversity in social recommendations in SVOD services positively affect their perception of information quality._  
**Hypothesis 1b (H1b).**_Users' perceptions of diversity in social recommendations in SVOD services positively affect their perception of playfulness._  
Previous studies show that the quality of information in an information system is a strong predictor of users' propensity to use it [49; 50]. Additionally, it was found that perceived playfulness positively influences intention to use [30; 51]. Thus, the following hypothesis was proposed:  
**Hypothesis 2a (H2a).**_Users' perceptions of information quality mediate the effect of perceived diversity on the intention to use an SVOD system._  
**Hypothesis 2b (H2b).**_Users' perceptions of playfulness mediate the effect of perceived diversity on the intention to use an SVOD system._  
The additional hypotheses focus on which aspects of social recommendations affect perceived diversity and information quality. First, these perceptions depend on the source of the social recommendations. Related to users' bias towards algorithms, algorithmic advice on a subjective decision is deemed less reliable than advice from real people [46]. In addition, it is often considered that a reputed person accesses diverse environments and trustworthy information [52; 53]. Thus, we propose the following hypotheses:  
**Hypothesis 3a (H3a).**_Users' perceptions of diversity in SVOD services are higher for recommendations based on human choices than for those based on algorithmic choices._  
**Hypothesis 3b (H3b).**_Users' perceptions of information quality in SVOD services are higher for recommendation lists based on human choices than for those based on algorithmic choices._  
**Hypothesis 4a (H4a).**_Users' perceptions of diversity in SVOD services are higher for influencers' recommendations than for online friends' recommendations._  
**Hypothesis 4b (H4b).**_Users' perceptions of information quality in SVOD services are higher for influencers' recommendations than for online friends' recommendations._
## 4 Series Expansions
### Large-\(\omega\) Expansion  
A complementary series expansion for large \(\omega\) can be derived by expanding the \(\exp(-t^{\beta})\) term in Equation (4). Using  
\[\int_{0}^{\infty}\!\mathrm{d}t\,t^{\mu-1}\mathrm{e}^{i\omega t}=\frac{\Gamma( \mu)}{\omega^{\mu}}\mathrm{e}^{i\mu\pi/2} \tag{18}\]  
one obtains a series in powers of \(\omega^{-\beta}\) (in [28] attributed to [PERSON] 1941 [34]):  
\[F_{\beta}(\omega)=i\sum_{k=0}^{\infty}{(-1)}^{k}\mathrm{e}^{ik\beta\pi/2}B_{k }\omega^{-k\beta-1} \tag{19}\]  
with  
\[B_{k}:=\frac{\Gamma(k\beta+1)}{\Gamma(k+1)} \tag{20}\]  
Separating real and imaginary parts,  
\[\begin{array}{rcl}Q_{\beta}(\omega)&=&\sum_{k=1}^{\infty}{(-1)}^{k-1}\sin(k \beta\pi/2)B_{k}\omega^{-k\beta-1}\\ \\ V_{\beta}(\omega)&=&\sum_{k=0}^{\infty}{(-1)}^{k}\cos(k\beta\pi/2)B_{k} \omega^{-k\beta-1}\end{array} \tag{21}\]  
Instead of \(P_{\beta}\), we compute its complement  
\[\overline{P}_{\beta}(\omega):=P_{\beta}(\infty)-P_{\beta}(\omega) \tag{22}\]  
The limit \(P_{\beta}(\infty)=\pi/2\) is obtained most easily through Equation (6). Integration of Equation (21)  
\[\overline{P}_{\beta}(\omega)=\int_{\omega}^{\infty}\!\mathrm{d}\omega^{\prime }\,Q_{\beta}(\omega^{\prime}) \tag{23}\]  
yields  
\[\overline{P}_{\beta}(\omega)=\sum_{k=1}^{\infty}{(-1)}^{k-1}\sin(k\beta\pi/2) \frac{B_{k}}{k\beta}\omega^{-k\beta} \tag{24}\]  
The expansions of \(Q_{\beta}(\omega)\), \(V_{\beta}(\omega)\), and \(\overline{P}_{\beta}(\omega)\) shall be summarized as  
\[Y={(-1)}^{1-\kappa}\sum_{k=1-\kappa}^{\infty}{(-1)}^{k}\sin\left((k\beta+ \kappa)\frac{\pi}{2}\right)u_{k} \tag{25}\]with  
\[u_{k}:=B_{k}(k\beta)^{-\mu}\omega^{\mu-1-k\beta} \tag{26}\]  
and with switches \(\kappa\), \(\mu\) as defined in Equation (17). To improve the numeric accuracy of the sine term in Equation (25) for \(\beta\to 2\), the complementary exponent \(\bar{\beta}:=2-\beta\) is introduced so that \(\left(-1\right)^{1+k}\sin(k\beta\pi/2)\) can be replaced by \(\sin(k\bar{\beta}\pi/2)\).  
For \(\beta\leq 1\), these series converge for all \(\omega\
eq 0\); for \(\beta>1\), they are asymptotic expansions. This is just complementary to the small-\(\omega\) expansion.
# Quantum Implementation of AIM: Aiming for Low-Depth
## 2 Preliminaries  
### [PERSON]'s Key Search  
[PERSON]'s search algorithm is a quantum algorithm that can reduce the search complexity of ciphers against classical computers by a square root. That is, ciphers that use a \(k\)-bit key have an exhaustive key search complexity of \(O(2^{k})\) against classical computers, but [PERSON]'s key search on a quantum computer reduces the complexity to \(\sqrt{2^{k}}\). The [PERSON]'s key search process for recovering a \(k\)-bit key for a known plaintext-ciphertext pair can be summarized as follows: \(\textit{Prepare}\rightarrow(\textit{Grover oracle}\) and \(\textit{diffusion operator})^{\sqrt{2^{k}}}\rightarrow\textit{measure}\).  
Firstly, to prepare the key in a superposition state, Hadamard (\(H\)) gates are applied to the \(k\) qubits, which causes the \(k\)-qubit key to be represented as a probability distribution over all possible key values (i.e., \(2^{k}\) values, as in Equation (1)). We refer the reader to [15] for comprehensive information about the Hadamard gate.  
\[H^{\otimes k}|0\rangle^{\otimes k}(|\psi\rangle)=\left(\frac{|0\rangle+|1 \rangle}{\sqrt{2}}\right)=\frac{1}{2^{k/2}}\sum_{x=0}^{2^{k}-1}|x\rangle \tag{1}\]For the known plaintext (\(P\)), qubits are allocated and X gates are applied according to the value of the known plaintext. The main component, the Grover oracle, contains the quantum circuit of the target cipher. The known plaintext is encrypted using the quantum circuit for the target cipher and the \(k\)-qubit key (\(\psi(k)\)). This generates a superposition state of the ciphertext encrypted with all possible key values. Then, the Grover oracle compares a superposition state of the ciphertext with the known ciphertext (\(C\)). If there is a match (Equation (2)), the Grover oracle returns the solution by flipping the sign of the corresponding state of the key (Equation (3)) as follows:  
\[f(x)=\begin{cases}1\text{ if }Enc_{\psi(k)}(P)=C\\ 0\text{ if }Enc_{\psi(k)}(P)\
eq C\end{cases} \tag{2}\]  
\[U_{f}(|\psi\rangle|-\rangle)=\frac{1}{2^{n/2}}\sum_{x=0}^{2^{n}-1}(-1)^{f(x)}| x\rangle|-\rangle \tag{3}\]  
Another module, the diffusion operator, amplifies the amplitude of the solution returned from the [PERSON] oracle, increasing the probability of recovering the key.  
[PERSON]'s key search sequentially iterates the oracle and the diffusion operator \(\sqrt{2^{k}}\) times to increase the amplitude of the solution sufficiently, then recover (measure) the key with high probability. From a cost perspective, optimizing the encryption quantum circuit within the [PERSON] oracle is crucial for reducing the cost of [PERSON]'s key search.
## 3 Results and Discussion
### General Observation of the Experimental Tests  
The results of all the measurements are reported in Table 6. From a rough evaluation of the collected data, it is possible to make some useful considerations for evaluating the benefit of the statistical analysis. First, it was noted that each response the data reported had a good variability; therefore, it is possible to suppose that the selected input factors may have some effect that can be calculated quantitatively on the responses. Nevertheless, it was not possible to identify a precise trend from a rough observation of the data collected for the same response. Comparing different responses, the data evolution of the same property, considering passing time, was generally consistent with expectations due to plant growth; for example, a general increase in the height and amount of water was observed by increasing the day of observation. Thereafter, in these conditions, a statistical analysis of the data could be performed, and was necessary for the quantitative calculation of the effects of the input factors on the selected responses.
## 4 Conclusions and Future Work
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{\(K_{C}\) **NOTS**} & \multirow{2}{*}{**Noise**} & \multirow{2}{*}{**Diagnosis**} & \multirow{2}{*}{**SNR**} & \multicolumn{3}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**Sensitivity (\%)**} & \multicolumn{2}{c}{**Precision (\%)**} \\  & & & & & & & \([Acc]\) & & & \([S_{c}]\) & & \([P_{r}]\) \\ \hline  & & & & & SVM & CNN & SVM & CNN & SVM & CNN \\ \hline Simulated 1 & FCS [57] & 10 & 15 & \(N_{R}\) & - & \([-10\) dB, 10 dB] & 98.36 & 97.61 & 99.94 & 97.94 & 98.42 & 99.49 \\ \hline Simulated 2 & FCS [11] & 10 & 15 & \(N_{R}\) & - & \([-10\) dB, 10 dB] & 95.85 & 97.93 & 97.81 & 98.28 & 97.93 & 99.53 \\ \hline Simulated 3 & FCS [12] & 10 & 15 & \(N_{R}\) & - & \([-10\) dB, 10 dB] & 98.03 & 97.44 & 99.94 & 97.72 & 98.08 & 99.50 \\ \hline Simulated 4 & CCS [57] & 10 & 15 & \(N_{R}\) & - & \([-10\) dB, 10 dB] & 96.57 & 92.98 & 98.25 & 92.73 & 98.27 & 99.60 \\ \hline Simulated 5 & CCS [11] & 10 & 15 & \(N_{R}\) & - & \([-10\) dB, 10 dB] & 98.63 & 98.25 & 99.90 & 98.54 & 98.71 & 99.55 \\ \hline Simulated 6 & CCS [12] & 10 & 15 & \(N_{R}\) & - & \([-10\) dB, 10 dB] & 95.47 & 94.34 & 96.86 & 94.21 & 98.54 & 99.58 \\ \hline Real 1 & FCS [28,56] & 10 & 15 & \(N_{R}\) & IPF & \([-10\) dB, 10 dB] & 94.58 & 91.42 & 96.10 & 93.11 & 98.32 & 99.34 \\ \hline Real 2 & CCS [28,56] & 10 & 15 & \(N_{R}\) & BE & \([-10\) dB, 10 dB] & 74.78 & 72.34 & 76.03 & 74.00 & 97.72 & 98.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the results of the proposed method as input of an SVM and CNN in terms of accuracy, sensitivity, and precision (mean values per crackle type) \(K_{C}\): number of crackles per signal. NOTS: number of signals per SNR. \(N_{S}\): number of signals generated considering all SNRs evaluated.