esa-sceva/satcom-chunk-collection huggingface dataset:
# In Vitro Genotoxicity Assessment from the _Glycyrrhiza_ New Variety Extract
## 4 Discussion  
The purpose of this study is to assess the potential genotoxicity of WG. WG is a _Glycyrrhizia_ new variety, and various pharmacological effects including enhancing immune response and being anti-allergenic, anti-neuroinflammatory, and anti-ulcerative colitis are investigated [11; 12; 13; 14]. Therefore, the safety information of WG should be required to develop a therapeutic agent. In our previous report, we revealed the general toxicity of WG to estimate the potential risk to organs and the relationship between exposure and response [29; 30]. As a result of the general toxicity test, target organs and no-observed-adverse-effect level (NOAEL) were not observed in up to 5000 mg/kg/day of WG extract [31]. Further, we conducted the genotoxicity of WG in this study to estimate carcinogenicity, mutagenicity by damage to DNA or chromosome [21]. As a result of the genotoxicity test, WG extracts did not induce genotoxic or mutagenic activities. Therefore, our studies could contribute to providing safety information to clinical as well as to register the WG as a new species of _Glycyrrhizia_ in the KP. According to previous reports [32], licorice is composed of more than 400 compounds, including saponins (glycyrrhizin, uralsaponin, licorice-saponin, actoxylglycyrrhizin, apioglycyrrhizin, and araboglycyrrhizin), flavonoids (liquiduritigenin, liquiritin, rhamnuliuiritin, licochalcone, liocflavone, and liciosoflavanone), phenols (liquiduritin, liquiritin, liquiritin apioside, chromemes, counamins, and dihydrorostilbenes), essential oils (\(\alpha\)-/\(\beta\)-pinene, octanol, and stragole) and other compounds (fatty acids, glucose, sucrose, starch, polysaccharides, and sterols). We also identified the numerous ingredient profiling of WG by TOF-MS analysis. Approximately 3000 chemicals including glycyrrhizic acid and liquiritigenin were detected in the WG water extract (Figure S1). Glycyrrhizic acid (glycyrrhizin) is the major triterpenoid saponin present in licorice, which has various pharmacological effects such as being anti-inflammatory, anti-ulcerogenic, anti-allergic, antioxidant, anti-hepatotoxic, anti-tumor, and anti-viral [24; 33; 34]. Accordingly, a composition of at least 2.5% glycyrrhizic acid is an indicator component of licorice that must be present for its medicinal use according to the KP. According to a previous report, WG ethanol extract contains 3.9% glycyrrhizic acid [10]. In this study, we determined through HPLC analysis that water extract of WG contains 25 mg glycyrrhizic acid per g (2.5%) (data not shown). Accordingly, water and ethanol extract of WG both contain glycyrrhizic acid in sufficient quantities for medicinal use. The AMES test is the most widely employed initial screen for mutagenic potential for its convenience and low-cost. The AMES test uses the histidine auxotroph strains of _S. typhimurium_ and the tryptophan auxotroph strain of _E. coli_ to detect frameshift
# Querying on Federated Sensor Networks
## 2 Methods  
Layered and flat FSN federations are two methods for federating islands. By layered federation, each island can be managed as an autonomous system and run an island-specific querying protocol. While layered federation allows each island to run different querying protocols, flat management allows the same querying protocol to run on an entire FSN. According to the flat FSN model, an FSN is a geographically large WSN with communication holes.  
In our layered FSN model, islands are connected by gateways. Both gateways and island nodes gather data by in-network data processing and indexing techniques for data storage and retrieval. Using in-network data processing and indexing techniques, a gateway or a node either forwards a packet to the next unit (gateway or node) or replies back to a query source unit if it has valid data. We assume that gateways and nodes are initially pre-assigned in each island and have a capability of storing, aggregating and forwarding data.  
For the layered FSN model, we divide gateways into exterior gateways (EGs) and interior gateways (IGs). Each island is assigned with at least one EG to be connected to the rest of the FSN. While EGs perform communication among islands, IGs route packets within islands. Two EGs are neighbors if they can communicate directly, or, if their islands are adjacent, in such a way that at least one of the IGs in each island is neighbor with the one in the adjacent island. Two IGs are neighbors if they can communicate directly. In a layered FSN model, the exterior and interior gateway topology is demonstrated in Figure 1.  
For the flat FSN model, both gateways and nodes run the same querying protocol. For example, if this querying protocol is a hierarchical cluster-based querying protocol, in the flat FSN model, an island node can become the clusterhead of a gateway, and gateways, as well as island nodes have the same priority and privilege with respect to packet routing.
# FPGA-Based Pulse Compressor for Ultra Low Latency Visible Light Communications
## 2 Pulse Compression Basics  
### Theoretical Background  
A very brief summary with the basic principles of the chirp pulse compression is reported below, with the intention of helping readers who are not familiar with the method. On the other hand, pulse compression is a mature technique, and more details can be easily found in books, like, for example [22; 23].  
The transmitted signal is typically a chirp, i.e., a signal with temporal duration \(T\), and frequency that changes in time to span a bandwidth \(B=F_{Max}-F_{Min}\), where \(F_{Max}\) and \(F_{Min}\) are the higher and lower boundaries of the frequency interval. An important performance parameter of this method is the bandwidth-time product \(BT\). An example of chirp where the frequency chances linearly with time is:  
\[CH(t)=\sin\biggl{(}2\pi\biggl{(}F_{Min}\cdot t+\frac{B}{2T}\cdot t^{2}\biggr{)} \biggr{)} \tag{1}\]  
The received signal \(RF_{Rx}(t)\) is processed with a filter matched to the transmitted chirp. Several techniques have been studied for the optimization of such a filter [24]; however, in the simplest case, the received signal is correlated to the chirp itself:  
\[R(t)=\int_{0}^{T}CH(\tau)RF_{Rx}(t+\tau)d\tau \tag{2}\]  
In the positions where the \(RF_{Rx}(t)\) contains the transmitted chirp, the matched filter (2) produces the so-called \"compressed pulse\", i.e., a pulse-shaped signal with amplitude \(A\) and width \(W\). It can be demonstrated [22] that \(W\approx 1/B\) and \(A\approx\sqrt{BT}\). Since \(W<T\), the pulse is \"compressed\", which justifies the name of the technique. The \(W\) parameter represents the time resolution of the system, i.e., how much different chirps can be overlapped one to the other in time so that they can be individually detected in the compressed signal. Moreover, the amplitude of the compressed pulse is about \(A=\sqrt{BT}\) with respect to the original chirp. This amplitude gain extends the pulse beyond the noise floor, represents a useful SNR gain, and, at the end, eases the data detection.  
The example reported in Figure 1, generated in MATLAB (The Mathworks, Natick, MA, USA), clarifies the concept. Panel a reports a chirp with \(F_{Min}=1\) MHz and \(F_{Max}=5\) MHz, temporal duration \(T=10\) us, and thus \(B=4\) MHz and \(BT\) product of 40. The frequency is linearly modulated by (1), like reported in Figure 1b. The spectrum of the chirp is shown in Figure 1c. Finally, after the application of the compressor filter (2), the signal assumes the peaked shape represented in Figure 1d. The width of the pulse (evaluated at \(-4\) dB) is roughly \(W\approx 1/B=0.25\) us, and the amplitude gain is \(A\approx 6.3\).  
Figure 1: Example of chirp with \(T=10\) μs, \(B=4\) MHz, \(BT=40\) and its compressed pulse. (**a**) Chirp signal; (**b**) instantaneous frequency of the chirp; (**c**) spectrum of the chirp; (**d**) compressed pulse with \(-4\) dB width \(W=0.25\) μs and amplitude gain \(A=6.3\).
# Chaves Thermal Spring Water Impact on Skin Health: Potential Cosmetic Application
## 2 Materials and Methods
### Evaluation of the Antioxidant Capacity of CHAVES Thermal Spring Water  
The 2.2\({}^{\prime}\)-azino-bis (3-ethylbenzothazoline-6-sulphonic acid) diammonium salt radical cation (ABTS) decolorization assay was performed as previously described [31]. In brief, 20 mL of ABTS solution was obtained by the addition of 7 mmol/L of ABTS (Sigma-Aldrich, St. Louis, MO, USA) to a 2.45 mM of potassium persulfate (K\({}_{2}\)S\({}_{2}\)O\({}_{8}\)) solution (Merck, Darmstadt, Germany) at a 1:1 (\(v/v\)) proportion. The solution was left in the dark for 16 h and then diluted with deionized water to obtain an initial optical density (OD) of 0.700 \(\pm\) 0.020 at 734 nm. An amount of 15 \(\upmu\)L of the Chaves thermal spring water samples was placed in a 96-well plate with 200 \(\upmu\)L of the diluted ABTS solution, in duplicate, and incubated for 5 min at 30 \({}^{\circ}\)C. A calibration curve with Trolox standard solutions (0.075-0.008 mg/mL) (Sigma-Aldrich, St. Louis, MO, USA) was included. After incubation, the OD was measured at 734 nm using a Synergy H1 microplate reader (Biotek, Winooski, VT, USA), and results were expressed as the percentage of inhibition of the free radicals generated.  
Additionally, the 2,2-diphenyl-1-picrylhydrazyl (DPPPH) salt radical cation decolorization assay was performed as already reported [31]. Firstly, a 100 mL solution of 600 \(\upmu\)M DPPH was prepared combining 24 mg of DPPH salt (Sigma-Aldrich, St. Louis, MO, USA) with methanol. This solution was then diluted with methanol to obtain a DPPH solution with an OD of 0.600 \(\pm\) 0.100 at 515 nm. Afterwards, 25 \(\upmu\)L of the Chaves thermal spring water was added (in duplicate) to a 96-well plate with 175 \(\upmu\)L of the DPPH solution. It also included a calibration curve prepared with Trolox standard solutions (0.075-0.008 mg/mL). The plate was incubated at room temperature for 30 min, followed by the measurement of the OD at 515 nm. The results were expressed as the percentage of inhibition of the free radicals generated.
## 2 Related Works
### Hardware-in-Loop Testing  
HIL testing refers to simulating the hardware environment and integrating the software system with real or virtual hardware platforms. It is primarily used to verify the performance and reliability of control algorithms and embedded software during the product development stage.  
[PERSON] et al. [12] integrated the Cohda OBU MK5 commercial hardware as a new plug-in component, expanding the Eclipse MOSAIC co-simulation framework. A case study of a Cooperative-ITS (C-ITS) serving slow vehicles within the Italian A56 highway validates the effectiveness of the co-simulation framework for Connected, Cooperative and Automated Mobility (CCAM) implementations. [PERSON] et al. [13] provide Vehicle-to-Infrastructure (V2I) Roadside Unit (RSU) device deployment strategies for the USADepartment of Transportation (DOTs) through HIL testing of Commercial Off-The-Shelf (COTS) RSUs.
# SoK: Delegated Security in the Internet of Things
## 6 Discussion
### Authentication Delegation
#### 6.2.3 Availability  
Many protocols in this domain are vulnerable to \(\mathcal{A}_{weak}\) in terms of availability. These protocols primarily focus on ensuring confidentiality and integrity, but often lack mechanisms to address availability threats such as DoS attacks. Hence, \(\mathcal{A}_{weak}\) can exploit this by launching DoS attacks, overloading system resources, and preventing legitimate users from accessing services [41; 47; 49; 16].  
There are even fewer protocols that ensure availability against \(\mathcal{A}_{middle}\), as this adversary not only possesses the capabilities of \(\mathcal{A}_{weak}\) but can also exploit internal accomplices, who may potentially bypass built-in protection mechanisms against availability threats, such as rate limiting or traffic filtering. The same applies to \(\mathcal{A}_{strong}\), which as the most powerful adversary, can exploit both external and internal access. However, the use of decentralised system architectures [46] can effectively mitigate availability attacks, even from the strongest adversaries.
# Secure and Scalable Internet of Things Model Using Post-Quantum MACsec
## 4 Methodology
### PQC-TLS Using NTRUs
#### 4.2.2 Implementation  
We have selected NTRUs as the key exchange algorithm to facilitate the sharing of a secret key within the SSI-PQM system. Figure 3 represents the integration of the NTRU encryption algorithm within the TLS 1.3 protocol, specifically designed to enable secure key exchanges between IoT devices and an Authentication Server or between CN and the CN controller (CN Cont.). It outlines a two-way process that focuses on the generation and decryption of a secret key through NTRUs, a public key encryption algorithm. This process is crucial for establishing a secure communication channel in a PQC environment.  
On the IoT device side, the initial step involves generating an NTRU Key Pair. This key pair is crucial for the PQC mechanism to enable the IoT device to securely decrypt the secret key sent by the server. The decryption process using NTRUs ensures that only the intended recipient IoT device can access and utilize the secret key, thereby maintaining the confidentiality and integrity of the transmitted data. We highlight the secure key generation step in Figure 3, which is designed to safeguard the communication from potential eavesdroppers or attackers who might exploit vulnerabilities in classical cryptographic algorithms.  
Conversely, the server side in Figure 3 shows the encryption of the secret key using NTRUs before its transmission to the IoT device. This encryption ensures that the secret key is protected throughout its journey from the server to the IoT device for further secure communication. The figure also displays the components of the PQC-TLS handshake process, including the \"PQC-TLS Client Hello {+KeyShare}\" and \"PQC-TLS Server Hello {+KeyShare}, EncryptedExtensions, Certificate, Certificate Verity\", illustrating the comprehensive steps involved in establishing a secure TLS session. The visual representation emphasizes NTRUs' role in enhancing security during key exchanges in the quantum computing era, ensuring protection against advanced cryptographic attacks.
# Complete Blood Cell Detection and Counting Based on Deep Neural Networks
## 4 Experiments and Results
### Experiment Results
#### 4.4.2 White Blood Cell Detection  
WBC detection is usually the easiest task in blood cell counting due to the size and peculiar color of WBCs. Figure 9 shows an image with two adjacent WBCs. We comprehend the ability of each model in Table 7.  
Figure 8: An image with overlapping red blood cells.  
Figure 9: An image with adjacent white blood cells.  
As shown in Table 7, all of the models, except Model 3, can precisely identify neighboring WBCs, as seen in Figure 9 under confidence scores of 0.9 and 0.8. Model 1 has the best precision and recalls of WBC detection on the validation set.
# Exploring Space Syntax Integration at Public Transport Hubs and Public Squares Using Drone Footage
## 2 Materials and Methods  
The method applied in this study is similar to the one used by [PERSON] and [PERSON] (2020) [15] and [PERSON] and [PERSON] (2017) [19], with some important differences. Both those papers study public squares using Space Syntax integration; additionally, both of them divide the study area into a number of pedestrian zones which are then used throughout the analysis. Compared to these studies, the method presented here differs in four important aspects.  
Firstly, the pedestrian detections were done using video analysis from drone recordings made at each location (more details provided in Section 2.2: Study Locations), rather than manual counts made at specific gates or static snapshots that only capture a specific moment in time. Drone footage provides a complete overview of each location during the recording and makes it possible to gather a more comprehensive view of pedestrian movements in the area compared to other approaches.  
Secondly, the pedestrian zones were not selected based on the spatial configuration of the area as in the previously mentioned studies. Instead, the zones were defined as percentile areas depending on the distribution of the levels of integration--that is, the area with the highest level of integration was considered the first zone, and so on. Note that this approach does not create zones that are necessarily connected and instead focuses on dividing the area based on how well integrated its different parts are.  
Thirdly, both studies use axial lines to create an axial map to perform their spatial analysis. On the other hand, the method used in this study uses isovist fields adopting the same methodology proposed by [3; 20]. The advantage of isovists and isovist fields is that there is a complete description of how far one can see or move from every point in the space compared to axial maps [21].  
Finally, the proposed method goes into less detail about the design and function of each location compared to the above-mentioned studies. The main reason for this is the difference in the research scope. This study focuses more on exploring space syntax applied in the context of transport hubs and public squares, mainly to provide useful inputs for future studies centred on micromobility. This aim differs from the other studies which focused more on understanding the accessibility, sociability, and social interactions of the studied locations.
## 4 Experiments
### Experimental Setting  
**Data preprocessing.** For the audio modality, this approach transformed the signal into a log mel-spectrogram with a window size of 25 ms and a stride of 10 ms. The resulting spectrogram was segmented with a size of 960 ms. For the visual modality, frames from the video were extracted with a sample rate of 33 Hz. The pretrained multitask cascaded convolutional network [30] detected faces in each frame, and the facial images were cropped to 260 \(\times\) 260 pixels. For the text modality, the ALBERT tokenizer prepared the input for the pretrained linguistic model.  
**Feature extraction.** Pretrained deep learning networks extracted features from three modalities. The EfficientNet model from [31] was pretrained on facial analysis tasks to extract visual attributes for the visual modality. For the text modality, the pretrained ALBERT [32] (on the IEMOCAP and CMU-MOSEI datasets) and the pretrained Chinese BERT (on the CH-SIMS dataset) extracted word embeddings. The VGGish [9] network was pretrained on large-scale audio datasets for the acoustic data. Four heads of self-attention were applied for the transformer encoder layer configuration with a latent dimension of 64. Table 2 lists the additional customized hyperparameters designed for each dataset.  
**Network parameters.** The model was trained end-to-end, fine-tuning the feature extractors with a learning rate of one-tenth that of the main network. This work employed the Adam optimizer with a learning rate of \(5\times 10^{-5}\) and set the default temperature (\(T\)) to 10 to smooth the weights of loss candidates in the DRA and DWA strategies. To enhance stability when incorporating a pretrained text modality, we applied a scaled learning rate strategy using a text learning rate factor. Specifically, if a pretrained text model is used, its associated parameters are updated with a smaller learning rate to prevent excessive gradient updates, which can destabilize training. To further improve training efficiency, we optionally integrated a cosine annealing learning rate scheduler, which gradually reduces the learning rate over the course of training, improving convergence. Moreover, this work applied the summation of the average F1 score and accuracy (or weighted accuracy) as an indicator for early stopping. The training process halts if this signal does not improve for six epochs.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Hyperparameter** & **IEMOCAP** & **CMU-MOSEI** & **CH-SIMS** \\ \hline Learning rate & \(5\times 10^{-5}\) & \(5\times 10^{-5}\) & \(3\times 10^{-4}\) \\ Epochs & 30 & 30 & 30 \\ Batch size & 2 & 8 & 8 \\ Early stop condition & F1 + Acc & F1 + Wacc & F1 \\ \hline Language model & Pretrained base Albert & Pretrained base Albert & Pretrained base Bert Chinese \\ \hline Self-attention layers & 2 & 4 & 2 \\ Bottleneck layers & 2 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Customized hyperparameters for IEMOCAP, CMU-MOSEI, and CH-SIMS datasets.
## 4 Results and Analysis
### Deformation of the Mountain Surface Observation Points  
From the settlement observations of the mountain surface observation points, there is a subsidence in the mountain above the tunnel, and the observation point has undergone displacement both in the horizontal and vertical directions, mainly embodied in vertical displacement (i.e., settlement); the mountain surface observation points above the southeast of the tunnel have experienced a slightly larger settlement, but it is less than \(60\) mm and generally uniform.
## 3 Proposed Methodology
### Long Short-Term Memory  
Deep learning models frequently run into the issue of gradient disappearance (also known as learning pausing) or explosion (also known as learning scattering) [17]. LSTM networks are a type of Recurrent Neural Network (RNN). LSTM networks can effectively learn long-term dependencies and are resilient to the vanishing gradient problem. The LSTM architecture in Figure 3 has three processing gates: forget gate \(f_{t}\), input gate \(i_{t}\), and output gate \(o_{t}\)[18].  
LSTM has memory cells equipped with gates that control the flow of information, making LSTMs well-suited for time series forecasting [19]. Because it can accurately predict future loads based on past load sequences, the LSTM architecture is used as the foundation of the forecasting model [20]. The input layer (input feature), hidden layer, and output layer (target feature) are the most important parts of LSTM architecture. LSTM uses memory cells within the hidden layer.  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}) \tag{1}\]  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{2}\]  
\[\bar{C}_{t}=\tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{C}) \tag{3}\]  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\bar{C}_{t} \tag{4}\]  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{5}\]  
\[h_{t}=o_{t}*\tanh(C_{t}) \tag{6}\]  
Figure 2: Block diagram of data processing and AI model training.  
where \(\sigma\) denotes a sigmoid activation function; \(W_{f}\), \(W_{i}\), \(W_{C}\), and \(W_{o}\) represent weight matrices; \(h_{t-1}\) represents the output at the time \((t-1)\); and \(b_{f}\), \(b_{i}\), \(b_{C}\), and \(b_{o}\) represent the bias vectors.