esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Compact and Efficient Vision-Based Detection Frameworks
### Architectures  
Intuitively, upon a first look at the data reported in Table 2, it is possible to observe the predominance of one-stage detectors (39 out of a total of 42) over the two-stage alternative [83; 95; 99]. Two-stage and single-stage detection frameworks (the latter also called unified detectors) are the two main categories typically considered for the classification of modern object detection pipelines. Two-stage detectors have typically featured high accuracy in both target localization and classification tasks thanks to the exploitation of a Region Proposal Network (RPN) [123] dedicated to producing Region of Interest (RoI) proposals, i.e., candidate object bounding boxes. However, region proposal generation has shown to be an insurmountable bottleneck when trying to produce less costly models. Therefore, many authors have focused their research on exploring unified detection strategies, seeking more efficient solutions. Such an approach models object detection as a single regression problem, addressing in a single step the prediction in the form of bounding boxes of both the areas where potentially interesting objects might be found (localization) and the various classes they belong to (classification). The result is a simplified and smaller architecture, conceived as a single feedforward neural network, suitable to derive high-inference-speed detection models. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Detector’s Name** &
\begin{tabular}{c} **Framework** \\ **Type** \\ \end{tabular} & **Baseline** & **Backbone** & **Pre-Training** & **Enhancement Emphasis** & **Used in** \\ \hline – & One-stage & Tiny YOLOv2 & Darknet-19 & – & – & – & [110] \\ – & One-stage & Tiny YOLOv3 & Darknet-19 & Detector & \(\bullet\) & Higher robustness & \\ – & One-stage & YOLOv3 & Darknet-53 & Backbone & – & [66; 77; 106] \\ – & One-stage & YOLOv3 & MobileNetV2 & Detector & \(\bullet\) & Lower memory & \\  & & & & & overhead & [107] \\ FRDet & One-stage & YOLOv3 & Custom & – & \(\bullet\) & Higher accuracy & \\  & & & & & Lower memory & [105] \\ – & One-stage & SSD & MobileNetV1 & Detector & \(\bullet\) & Higher robustness & \\  & & & & & Higher accuracy & [63; 65; 72; 75; 78; 80; 88; 91] \\ PPt-YOLOv3 & One-stage & Tiny YOLOv3 & Darknet-19 & – & \(\bullet\) & Higher robustness & \\ – & One-stage & SSD & MobileNetV2 & Detector & – & [65; 87; 100] \\ M7 & One-stage & Tiny YOLO & – & – & \(\bullet\) & Lower memory & [101] \\  & & & & & overhead & \\ Tiny-BDN & Two-stage & Faster-RCNN & VGG & – & \(\bullet\) & Lower memory & [99] \\  & & & & & overhead & \\ TIB-Net & One-stage & Extremely Tiny & – & – & \(\bullet\) & Higher robustness & \\  & & & & & \(\bullet\) & Higher accuracy & [98] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the compact CNN-based object detection frameworks used in the research works considered.
## 3 Theoretical Background
### Transformers-Based Models  
Transformer-based models have revolutionized NLP in recent years. Three prominent models are BERT and GPT, with all variations, each with unique characteristics and applications.  
BERT, developed by Google researchers [6], utilizes the encoder part of the Transformer architecture. BERT's key feature is its ability to interpret words in the context of the entire sentence using a bidirectional attention mechanism and a complex \"multi-head attention\" mechanism [4]. BERT's training process involves pre-training on a large unlabeled text corpus and fine-tuning for specific NLP tasks [6]. Numerous variations of BERT, such as RoBERTa [11] and DistilBERT [21], have been developed to meet specific needs and challenges [18].  
GPT [19] is a series of language models developed by OpenAI that exclusively utilize the decoder part of the Transformer architecture. GPT models have set new standards by combining innovative technical features with massive datasets [19]. The initial GPT model had 110 million parameters and used a two-stage training method [19]. GPT-2 increased the model's capacity to 1.5 billion parameters and was trained on an expanded dataset [22]. GPT-3, the latest publicly available model, has 175 billion parameters and introduced \"few-shot\" learning, allowing it to adapt to various tasks with minimal examples [23].  
Figure 1: Transformer architecture.  
ChatGPT, developed by OpenAI, is based on the InstructGPT model [24] and utilizes the advanced Transformer architecture. ChatGPT can adapt to a wide range of NLP tasks without specific training, demonstrating \"zero-shot\" learning ability [25]. Its adaptability is attributed to the instruction-based fine-tuning framework using reinforcement learning with human feedback (RLHF) [17]. The training process of ChatGPT involves three steps: supervised fine-tuning (SFT), reward model training (RM), and reinforcement learning (RL) [26].  
These Transformer-based models have provided deeper language understanding and versatility in various applications, setting new standards in NLP.
# Fluorescent Imprinted Nanoparticles for Sensing of Chlorogenic Acid in Coffee Extracts
## Materials and Methods
### Analysis on Real Sample  
Green coffee beans were ground to a fine powder in a mixer ball mill MM400 (Retsch, Haan, Germany) and extraction was performed in duplicate by dynamic heat-assisted water extraction [19].  
For this purpose, 1 g of powdered _Coffea arabica_ green coffee from Brazil was added to 100 mL of boiling water, and the mixture was stirred for 10 min at 200 rpm on a heated plate (Arex Velp Scientifica, Usmate, Italy) and filtered.  
The filtrate was diluted 1:20 and analyzed by HPLC using a Thermo scientific Vanquish Dionex Softron instrument with a Kinetex C18 250 \(\times\) 4.6 mm 5 \(\mathrm{\SIUnitSymbolMicro m}\) 100 A (Phenomenex) column, a column guard, a 100 \(\mathrm{\SIUnitSymbolMicro m}\) loop, and a UV detector set at 325 nm (for 5-CQA) and 270 nm (for caffeine). The flow was set to 1 mL/min and injections were of 20 \(\mathrm{\SIUnitSymbolMicro m}\). The eluent was: solvent A H\({}_{2}\)O + 0.05% TFA and solvent B CH\({}_{3}\)CN + 0.05% TFA in a 85:15 ratio for the calibration curve of 5-CQA (Figure S1) and caffeine (Figure S2) (see Supplementary Material), while a gradient was used for the analysis of coffee extract (10 min 85% A and 15% B, from 15% B to 60% B in 20 min, from 60% B to 15% in 10 min, and 10 min 85% A and 15% B). Standard stock solutions were prepared in water in the range of concentration 50-250 \(\mathrm{\SIUnitSymbolMicro m}\) for calibration curve of 5-CQA and caffeine. Each solution was analyzed by HPLC in triplicate, and the mean value of each solution was used for the calibration curve (Figures S1 and S2). The LOD and LOQ resulted in 6.4 \(\mathrm{\SIUnitSymbolMicro m}\) and 19.5 \(\mathrm{\SIUnitSymbolMicro m}\), respectively. They were calculated according to the following equations:  
LOD 3.3 \(\times\)\(\sigma\)/S and LOQ 10.0 \(\times\)\(\sigma\)/S, where \(\sigma\) is the standard deviation and S is the slope.
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 5 Simulation Results and Performance Evaluation
### Simulation Results
#### 5.2.5 Required Packer Error Ratio  
Figure 13 shows energy consumption per packet in the presence of different \(Pr_{req}\). We can observe, if \(Pr_{req}\) is high which means high reliability of data packet, energy consumption is also high. Because when data packets are received at the gateway, error is checked in them. If error is higher than threshold, this data packet is dropped. As we allow higher number of acceptable erroneous bits, energy consumption decreases. Increasing \(Pr_{req}\) value means that reliability of data packet is decreasing.  
Energy per packet with different \(Pr_{req}\) in both scenarios of LMPC is higher as compared to proposed schemes. Although, the difference is less because in all schemes multiple copies are generated of a data packet. So, data packets reach at the sink through different paths. If one path has high fading, data packet may get less affected on other path. FLMPC-One is performing better because of two hops information and achieves reliability in both scenarios. Figure 13 shows that FLMPC-Two outperforms both schemes because of three hops information. FLMPC-Two selects that path which advances data packet towards surface and effects the data packet less. If all the copies of the data packet are erroneous then still there is a chance of getting original packet by combining them at the sink.  
The energy consumption of DBR in Figure 13 is initially low as compared to LMPC and FLMPC because it only generates one copy from every source node. On the other hand, with the increase in packet interval, the energy depletion increases because of high message exchange among the network nodes. Additionally, the occurrence of void hole also increases which results in high energy dissipation and more packet drop ratio. In Figure 13, the results depicted clearly show that at 6 s of packet interval, the energy consumption is greater than 0.3 J. Hence, the FLMPC and LMPC have achieved considerable amount of improvement in terms of energy utilization as compared to DBR.  
Figure 12: Packet Received Ratio.
## 2 Related Work
### Statistical-Feature-Based Methods  
Statistical-feature-based methods perform classification by analyzing the statistical characteristics of traffic, such as the traffic packet size and time intervals. APPScanner [5] leverages the statistical features of packet sizes to train a random forest classifier. [PERSON] et al. [6] rely entirely on statistical features derived from flow headers and use the K-Nearest Neighbor (KNN) algorithm for classification without depending on port numbers or the application-layer payload content. While these features are relatively apparent in non-encrypted traffic, encrypted data obscure the statistical features, making them less reliable in encrypted traffic. Therefore, this approach also faces challenges in the identification of encrypted traffic.
# Experimental Evaluation of Rigidity Center
## 2 Materials and Methods  
### Balsa Wood  
Rigidity center of a building mainly depends on lateral stiffness of the vertical load-carrying members. Lateral stiffness of a member is affected by the material (as mechanical properties of materials are different), sectional properties (moment of inertia) and _length_ of the member. If all of the vertical load-carrying members in a building are made of the same material (either concrete, steel or wood), i.e., keeping _modulus of elasticity_ (_E_) the same, the only variable will be the moment of inertia and the location of the member in calculating the rigidity center. Therefore, considering the laboratory conditions (in terms of space, loading, measurement devices, data acquisition capability and the amount of the buildings), scaled versions of the buildings were constructed.  
In order to see the exaggerated deformations (drifts) of the columns, a material with low _modulus of elasticity_ was sought, and finally, balsa wood was selected. Balsa wood is one of the lightest and softest woods, and its _modulus of elasticity_ is relatively low compared to other woods.  
For the columns, 6 \(\times\) 6 mm balsa sticks were used, and 3 mm thick balsa plates with varying lengths were utilized for shear walls. Before the tests, _unit weight_ (\(W\)) of all the balsa wood and _modulus of elasticity_ (_E_) of some of the selected balsa wood were calculated from the three-point bending test recorded (Figures 1 and 2). _Unit weights_ varied between 62 kg/m\({}^{3}\) and 420 kg/m\({}^{3}\) in balsa sticks, and they were between 195 kg/m\({}^{3}\) and 372 kg/m\({}^{3}\) in balsa plates. _Modulus of elasticity_ measurements revealed that \(W\) was proportional to \(E\). Therefore, as difference in _modulus of elasticity_ affects the bending stiffness, balsa wood was grouped considering its _unit weights_ in order to eliminate the effect of it in rigidity center calculations.  
In addition, 8 mm thick medium-density fiberboard (MDF) plates, mostly \(300\times 300\) mm in dimension, were used as slabs, and 18 mm thick MDF plates with 500 \(\times\) 500 mm dimension were utilized as the bases of the buildings. The _unit weight_ of the MDFs was found to be 1000 kg/m\({}^{3}\), whereas their _modulus of elasticity_ was 4950 MPa. Although some insignificant differences exist, all the MDFs were assumed to have the same mechanical properties.  
Figure 1: _Weight_ measurements of balsa wood.
## 2 Methods
### Ex Vivo Anti-Inflammatory Effect
#### 2.3.4 Cytotoxicity Assessment of Samples on RAW 264.7 Cells  
RAW 264.7 cell count was determined using a cell counter. The cells were seeded at a density of 1 \(\times\) 10\({}^{5}\) cells/mL per well in a 96-well plate and incubated for 24 h at 37 \({}^{\circ}\)C with 5% CO\({}_{2}\) in a humidified incubator. The culture medium was then removed, and the samples were prepared at different concentrations using the culture medium. Each well received 100 \(\upmu\)L of the sample solution. After 48 h of incubation, the culture medium was removed, and 100 \(\upmu\)L of 500 \(\upmu\)g/mL MTT solution was added to each well. The cells were then incubated for another 3.5-4 h. The formazan crystals were dissolved in DMSO, and the absorbance at 550 nm was measured after 15-30 min [21].  
\begin{table}
\begin{tabular}{c c} \hline
**Gene Name** & **Primer Sequence** \\ \hline _p53_ & F: 5\({}^{\prime}\)-ATGGAGGACCGCAGATCAGA-3\({}^{\prime}\) \\ R: 5\({}^{\prime}\)-TGCAGGGCGCGCGCGCGTGTAG-3\({}^{\prime}\) \\ \hline _p21_ & F: 5\({}^{\prime}\)-ATGTCAGAACCGCGTGGGG-3\({}^{\prime}\) \\ R: 5\({}^{\prime}\)-GCCGGGCCCCGGGGA-3\({}^{\prime}\) \\ \hline _Bcl-2_ & F: 5\({}^{\prime}\)-AAGATTGTAGGATCGGTGC-3\({}^{\prime}\) \\ R: 5\({}^{\prime}\)-GCCGGAACACTTGATTCTGGT-3\({}^{\prime}\) \\ \hline _Bcl-xL_ & F: 5\({}^{\prime}\)-GCTGGGACACTTTTGTGGT-3\({}^{\prime}\) \\ R: 5\({}^{\prime}\)-TGTCTGCTCACTCCGACTG-3\({}^{\prime}\) \\ \hline _Bim_ & F: 5\({}^{\prime}\)-AGATCCCCGCTTTCATCTT-3\({}^{\prime}\) \\ R: 5\({}^{\prime}\)-TCTTTGGCGATCCCATATC-3\({}^{\prime}\) \\ \hline _Bad_ & F: 5\({}^{\prime}\)-CAATGACACCTTCTTACC-3\({}^{\prime}\) \\ R: 5\({}^{\prime}\)-GACAAGCTTCCGTTCTCAG-3\({}^{\prime}\) \\ \hline _Bax_ & F: 5\({}^{\prime}\)-TGCTTCAGGGTTTCACC-3\({}^{\prime}\) \\ R: 5\({}^{\prime}\)-GCCGCAATCACTCTG-3\({}^{\prime}\) \\ \hline _Bak_ & F: 5\({}^{\prime}\)-TCTGGCCCTACACGTCTACC-3\({}^{\prime}\) \\ R: 5\({}^{\prime}\)-AGTGTGTGCAGCATGAAGTCG-3\({}^{\prime}\) \\ \hline _Caspase-9_ & F: 5\({}^{\prime}\)-TTAGTTTGCCCACCACCAGT-3\({}^{\prime}\) \\ R: 5\({}^{\prime}\)-CTGCTCAAAGATGCTGCTTCCA-3\({}^{\prime}\) \\ \hline _Caspase-3_ & F: 5\({}^{\prime}\)-TTTTTCAGAGGGCGTGTGT-3\({}^{\prime}\) \\ R: 5\({}^{\prime}\)-CGGCCTCCACTGGATTTTA-3\({}^{\prime}\) \\ \hline _GAPDH_ & F: 5\({}^{\prime}\)-AGGTCGGTGTGACCGATTTG-3\({}^{\prime}\) \\ R: 5\({}^{\prime}\)-GGGCTGTTGATGGAACA-3\({}^{\prime}\) \\ \hline \end{tabular}
\end{table}
Table 1: Primer sequences of RT-qPCR assay.
## 2 Circuit Analysis and Design
### Frequency Staggering Technique
Through the frequency staggering, the overall gain of the whole amplifier exhibits a flat response of 12.8 \(\pm\) 0.5 dB from 25.6 to 42.4 GHz, covering the full Ka-band, as shown in Figure 6. After full electromagnetic simulation with Keysight ADS Momentum, the values of the matching components are slightly reoptimized considering the undesired magnetic and electric coupling between matching components and interconnection lines. The design parameter values of the full Ka-band amplifier are listed in Table 1.
## 2 Background and Related Work  
### Stream Cipher  
A stream cipher is a symmetric cryptosystem and is usually employed in a real time communication system. Figure 1 is the block diagram of a basic stream cipher structure [24]. In both the encryption part and decryption part, there is a pseudorandom bits generator. The key \(K\) is the input to a pseudorandom bits generator, which produces the random keystream \(k_{i}\). The keystream \(k_{i}\) is operated with plaintext using the bitwise exclusive-OR operation for encryption and decryption. A common form of stream cipher encrypts the plaintext usually in one bit or one byte and even encrypts units larger than one byte at a time.  
Figure 1: Block diagram of stream cipher.  
The keystream needs to approximate the properties of the true random number stream as close as possible. It is dependent on the value of the input key and is unpredictable without the knowledge of input key \(K\). The pseudorandom bits generator generates a successive keystream based on its internal state. The states are updated in the following two ways [5; 25]:  
* Synchronous stream cipher: In the synchronous stream cipher, the stream of pseudorandom digits is generated independently. The sender and receiver must be exactly in step for decryption to be successful.
* Self-synchronizing stream cipher: The self-synchronization has the advantage that it can synchronize automatically after receiving some digits of the ciphertext. This makes the self-synchronizing system easier to restore if the digits are discarded or added to the message stream.  
The stream cipher is similar to the one-time pad. The difference between them is that a one-time pad uses the genuine random number stream, but a stream cipher uses the pseudorandom bits generator. A stream cipher with a proper design could be as secure as a block cipher of comparable key length.
# Three-Dimensional Drone Cell Placement: Drone Placement for Optimal Coverage
## 4 Proposed Methods for Optimal DC Placement
### Particle Swarm Optimization  
Particle Swarm Optimization (PSO) is a meta-heuristic algorithm first proposed by [PERSON] and [PERSON] in 1995 inspired by the swarm behavior of flocking birds, and fish schools in nature [43]. As the name suggests, PSO works by having a swarm (population) of possible solutions. The movement of the particles in the search space is dictated by the particle's best-known position and the best position of the swarm. The best-known position determines the direction of the entire swarm. These steps are repeated in its standard form for a number of iterations until a probable solution is found.  
\begin{table}
\begin{tabular}{l c} \hline
**Environment** & **Elevation Angle (Degree)** \\ \hline High-rise urban & 75.52\({}^{\circ}\) \\ Dense urban & 54.62\({}^{\circ}\) \\ Urban & 42.44\({}^{\circ}\) \\ Suburban & 20.34\({}^{\circ}\) \\ \hline \end{tabular}
\end{table}
Table 1: Optimal elevation angles.  
PSO works as a globally connected swarm where all particles share information between each other and know the best position ever visited by any particle in the swarm. Each particle's position and velocity are given as [44]:  
\[x_{id}^{t+1}=x_{id}^{t}+v_{id}^{t+1} \tag{13}\]  
\[v_{id}^{t+1} =v_{id}^{t} \tag{14}\] \[\quad+c_{1}r_{1}(p_{id}^{t}-x_{id}^{t})\] (15) \[\quad+c_{2}r_{2}(g_{id}^{t}-x_{id}^{t}) \tag{16}\]  
where \(i\) is the particle index, \(d\) is the dimension (each particle has a position and velocity for each dimension), and \(x_{id}^{t}\) and \(v_{id}^{t}\) are the position and the velocity of the particle \(i\) in dimension \(d\) at iteration \(t\), respectively. \(c_{1}\) and \(c_{2}\) are the acceleration constants for the cognitive and social component, and \(r_{1}\) and \(r_{2}\) are the stochastic components of PSO in the limit [0, 1]. \(p_{id}\) is the location of the best visited location in dimension \(d\) for particle \(i\) and \(g_{id}\) is the best particle in the swarm in dimension d [44]. The pseudo code for PSO is presented in Algorithm 1.  
```
1:Initialize particles' positions \(\mathbf{X}^{0}\)
2:Initialize particles' velocities \(\mathbf{V}^{0}\)
3:Initialize personal best positions \(\mathbf{P}^{0}=\mathbf{X}^{0}\)
4:Initialize global best position \(\mathbf{G}^{0}=\text{argmin}\{f(\mathbf{X}_{i}^{0})\}\)
5:for each iteration \(t=1\) to \(T\)do
6:for each particle \(i=1\) to \(N\)do
7: Update velocity: \(\mathbf{V}_{i}^{t}=w\mathbf{V}_{i}^{t-1}+c_{1}r_{1}(\mathbf{P}_{i}^{t-1}- \mathbf{X}_{i}^{t-1})+c_{2}r_{2}(\mathbf{G}^{t-1}-\mathbf{X}_{i}^{t-1})\)
8: Update position: \(\mathbf{X}_{i}^{t}=\mathbf{X}_{i}^{t-1}+\mathbf{V}_{i}^{t}\)
9:if\(f(\mathbf{X}_{i}^{t})<f(\mathbf{P}_{i}^{t-1})\)then
10: Update personal best: \(\mathbf{P}_{i}^{t}=\mathbf{X}_{i}^{t}\)
11:endif
12:if\(f(\mathbf{X}_{i}^{t})<f(\mathbf{G}^{t-1})\)then
13: Update global best: \(\mathbf{G}^{t}=\mathbf{X}_{i}^{t}\)
14:endif
15:endfor
16:endfor
```  
**Algorithm 1** Particle Swarm Optimization (PSO)
## 3 Time-Frequency Representation
### Effect Analysis  
We select a same segment of signal for experiment to compare the four time-frequency analysis methods, and the results are shown in the Figure 11. The abscissa of image represents time, and the ordinate represents frequency. The lighter the color of the pixel, the higher the power. It can be seen that the time-frequency distribution of Cohen and WVD has obvious cross-term interference. Between STFT and CWT, the CWT's signal energy more concentrated. Therefore, we select CWT for time-frequency representation in this article.  
Through time-frequency joint representation, we have completed the transformation of signal information from time/frequency-domain to time-frequency joint characteristics, providing more usable information for signal perception. The next step is to send the obtained time-frequency image to the CNN for model training and signal perception.