esa-sceva/satcom-chunk-collection huggingface dataset:
# Achieving Low Latency Communications in Smart Industrial Networks with Programmable Data Planes
## 4 Design of the INC-Based System  
This paper presents an INC-based system focused on improving the processing of industrial communications, achieving shorter response times and reducing the amount of transmitted data. More specifically, the solution presented in this paper consists of processing a sensor measurement publication message (aforementioned MQTT publish message), analyze it in the programmable switch, and if it exceeds a threshold, duplicate and send it through another port towards the edge broker, calling that new duplicated message an \"alarm\". The transmission to an edge broker results in a lower response time compared with the cloud communication. Besides, the transmission between the switch and the edge broker is much less congested than the main link to the cloud data center, which usually entails going through a public network, thus reducing link failure possibility and avoiding network congestion problems that would slow down TCP transmissions.
# Utilizing Ensemble Learning to Improve the Distance Information for UWB Positioning
## 2 Preliminaries  
### Problem Formulation  
In the UWB positioning architecture, anchors can measure the distance from the tags to themselves via some radio ranging approaches. The measured distance can be expressed as follows:  
\[d_{measure}=d_{real}+d_{noise}, \tag{1}\]  
where the noise part is assumed as a white Gaussian noise generally. However, in [5], the authors illustrate that the white Gaussian noise may not fit the uncertain measurement well in practice. Thus, we need some novel ideas for overall consideration instead of merely using a deterministic process to simulate the uncertainty.
# Application Layer Protocol Identification Method Based on ResNet
## 4 Experimental Results and Discussion
### Experimental Results and Analysis
#### 4.4.3 Training Performance
In summary, based on the experimental results, it is clear that using composite feature RGB images as a model input leads to significant improvements in all metrics. The fundamental reason is that composite feature data reduce much of the noise and redundant information, allowing the model to more easily identify relevant features at the lower layers of the network. At the same time, they stabilize gradient updates at deeper layers, reducing the risk of gradient vanishing, which in turn accelerates convergence and improves accuracy.
# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 5 Detailed System Description
### User Joins the System  
#### 5.1.1 Construction of UIP  
Smartphones have access to a multitude of user information such as browsing logs, emails, text messages, mobile app data, GPS coordinates and data from available sensors, such as accelerometers, pedometers and heart rate monitors. By simply tapping into these resources, the client can determine the users advertising needs with much higher effectiveness than a remote observer. For example, when the device detects high physical activity while in close proximity to a park, this can be associated to an advertising interest for sporting equipment.  
The user's advertising interests are represented in the form of a list that follows a standard format throughout the entire system and is termed the User's Interest Profile (UIP). Each entry of the UIP denotes a common consumer interest (automotive, technology, food and drink, etc.) and is recognized by a unique identifier, which is noted as the Interest-ID. When given the right permissions, the client associates the user's activity with specific advertising interests that can in turn be marked on the UIP in a simple binary manner where \"true\" represents that the user's activity matches the particular interest of the UIP and \"false\" that it does not. The UIP is stored locally where it can be dynamically maintained in accordance to the alterations of the users behaviour and is not accessible to anyone else.  
The determination of suitable interests happens independently from the rest of the system, thus offering a great deal of versatility. Developers will be able to fine-tune the system by designing their own algorithms that can be fully compatible with the rest of the system for as long as they produce an output which follows a standard UIP format.  
Figure 1: System sub-protocols and their corresponding triggers.
## 4 Experimental Results and Discussion  
We have used MATLAB 2019b on an Intel(r)Core(tm) i7 CPU having 4.20 GHz processing power along with GTX 1080 Ti GPU for feature extraction and evaluation. Experiments on 3718 high quality, low quality and blurry images have been performed. Most of the vehicles license plates are from different states of America, Europe along with vehicles from Pakistan, UAE, Canada, Australia, Mexico, UAE, etc.  
License plates having different foreground and background colors with a variety of formats are included in the test dataset, as shown in Figure 14. Most of the images are gathered from Media Lab [52] and [PERSON]'s databases [53]. Moreover, the low-resolution images and the images effected by diverse weather and illumination conditions are also included in the test database. In several license plate images, shadow, partial shadow and blur are also included. Some cases in which license plates were under direct sunlight are also included to verify the adaptability of the proposed method.
# Invisible Threats in the Data: A Study on Data Poisoning Attacks in Deep Generative Models
## 2 Related Works
### Backdoor Attack
#### 2.2.3 Backdoor Attacks against DGMs  
Even though there is currently quite a lot of research in the field of invisible backdoor attacks, most of the research has been on deep neural networks, and not a lot of research has been conducted in the field of DGMs. One of the major reasons for this is that training a DGM is a difficult task, which often requires expert machine learning knowledge and high-performance equipment to ensure that the model can converge. In 2020, [PERSON] et al. [29] were the first to propose a backdoor attack against the deep generative model-domain GAN models. The article proposed a novel dynamic backdoor technique that generates triggers with random patterns and locations, thus reducing the effectiveness of current backdoor detection mechanisms. Nevertheless, the study fails to achieve the concealment of the triggers, thereby ignoring the key property of backdoor invisibility.  
There are fewer studies on invisible backdoor attacks on DGMs. In 2021, [PERSON] et al. [1] proposed three innovative training-time backdoor attack methods to achieve specific attack goals. However, all three methods require parameter changes and additions to the generator of the model, which may cause numerous inconveniences and limitations in practical applications.  
The study of invisible backdoor attacks against DGMs is an emerging research area. The objective of our study is to investigate the feasibility of invisible backdoor attacks against DGMs by poisoning the data in the data preparation phase through an encoder-decoder network, thereby implementing backdoor attacks without any changes to the model itself. This approach allows for the implementation of backdoor attacks without any changes to the model structure or parameters. This further provides new insights and support for the development of DGM security.
## 2 Experimental Section
### XPS Measurements  
Surface chemical characterisation of original and IL-doped membranes was performed by XPS. A Physical Electronics spectrometer (PHI 5700, Physical Electronics, Minneapolis, MN, USA) was used, with X-ray Mg K\({}_{a}\) radiation (300W, 15 kV, 1253.6 eV) as the excitation source. High-resolution spectra were recorded at a given take-off angle of 45\({}^{\circ}\) by a concentric hemispherical analyser operating in the constant pass energy mode at 29.35 eV, using a 720 \(\upmu\)m diameter analysis area. Under these conditions, the Au 4f\({}_{7/2}\) line was recorded with 1.16 eV FWHM at a binding energy of 84.0 eV. The spectrometer energy scale was calibrated using Cu 2p\({}_{3/2}\), Ag 3d\({}_{5/2}\), and Au 4f\({}_{7/2}\) photoelectron lines at 932.7, 368.3, and 84.0 eV, respectively. Charge referencing was done against -CF\({}_{2}\)-carbon of Nafion (C 1s, 292.0 eV). Membranes were mounted on a sample holder without adhesive tape and kept overnight at high vacuum in the preparation chamber before being transferred to the analysis chamber of the spectrometer for testing. Each spectral region was scanned several sweeps until a good signal to noise ratio was observed. The pressure in the analysis chamber was maintained lower than 5 \(\times\) 10\({}^{-6}\) Pa. PHI ACCESS ESCA-V6.0 F software (Physical Electronics, Minneapolis, MN, USA) package was used for acquisition and data analysis. A Shirley-type background was subtracted from the signals. Recorded spectra were always fitted using Gauss-Lorentz curves and following the methodology described in detail elsewhere [20], in order to determinate more accurately the binding energy (BE) of the different element core levels. Atomic concentration percentages of the characteristic elements of the surfaces were determined taking into account the corresponding area sensitivity factor [21] for the different measured spectral regions. Membrane samples were irradiated for a maximum time of 20 min to minimize possible X-ray damage.
# A Light-Weighted Machine Learning Approach to Channel Estimation for New-Radio Systems
## 5 Simulation Analysis
### Simulation Environment
The performance metric between the actual channel and the estimated channel was verified using the MSE defined as follows in Equation (20) for each signal-to-noise ratio (SNR)  
\[\text{MSE}=\frac{1}{IN}\sum_{i=1}^{I}\sum_{n=0}^{N-1}\|\mathbf{G}_{i}(n)-\hat{ \mathbf{G}}_{i}(n)\|^{2}, \tag{20}\]  
where \(\mathbf{G}_{i}(n)\) is the actual channel for the \(n\)-th component of the \(i\)-th iteration, and \(\hat{\mathbf{G}}_{i}(n)\) is also the estimated channel for the component corresponding to \(\mathbf{G}_{i}(n)\).
## 4 Advanced Persistent Threat Life Cycle Analysis
### Seven Stage Attack Model  
In the article [72], a general approach of an APT attack of seven stages was presented. These stages are:  
1. Research: The attackers seek publicly available information about the victim.
2. Preparation: The attackers prepare an initial attack to exploit the vulnerabilities using network scanning to create custom exploits.
3. Intrusion: The attackers launch the first attack which usually consists of spear-phishing.
4. Conquering the network: Remote access tools or backdoors to control the system are used when the attacker has compromised at least one host.
5. Hiding presence: The attacker seeks to remain hidden in the network for a long time. The attack can have periods of inactivity.
6. Gathering data: The attacker looks for data of interest and masks it as legitimate traffic to be slowly extracted.
7. Maintaining access: The attacker can modify or create exploits, remote access tools and C&C servers, to obtain prolonged access to the network.  
The Lockheed Martin company proposed a seven-stage life cycle called cyber kill chain (CKC) [73]. This model seeks to understand how an attack works to enrich the understanding of tactics, techniques, and procedures used by attackers. These stages are described below:  
1. Reconnaissance: The attacker performs a preliminary reconnaissance of the network of the organisation, using spear-phishing techniques, port scanning, and social engineering.
2. Weaponization: The attacker builds a payload that is sent to the victim. It usually consists of an exploit with a RAT/troyan delivery.
3. Delivery: The payload created is sent to the victim through mail, websites or a removal devices.
4. Exploitation: The attacker executes the exploit that has been sent to the victim.
5. Installation: A Trojan and/or remote access trojan (RAT) is installed when the attacker gains access to the system.
6. Command and control: The remote access software connects to C&C of the attacker.
7. Actions and objectives: The attacker performs data exfiltration compromising the integrity and availability of the data. This stage can last weeks, months or even years.
## 3 Results
### Structural and Electronic Properties
#### 3.1.3 W Doping  
In Figure 1e,f we present the W:TiO\({}_{2}\). In this case, the interstitial sits at 1.96 A near the oxygen atom, while the substitutional is located at 1.91 A near the O-atom.  
We have also examined the electrical properties of W:TiO\({}_{2}\) both as interstitial and substitutional defects. Specifically, with regards to W\({}_{i}\):TiO\({}_{2}\), in Figure 2e we predict that some mid-gap states are formed and the bandgap is significantly reduced, reaching a value of 2.8 eV. To analyze these states, in Figures 2e and 3e, we observe that three gap states are formed at 0.4-0.6 eV, 1.1-1.3 eV, and 1.5 eV and are mainly created from the Ti-3d and O-2p orbitals. In this case, we compute that the W-4d orbitals contribute significantly neither to the gap states, nor to the conduction band.  
Conversely, examining the DOS and PDOS (Figures 2f and 3f) of the substitutional doping case, we observe that the W-5d orbitals create some available states near the conduction band, which reduce the bandgap to the value of 2.87 eV. In addition, some states are formed at 0.1 eV, which result from the O-2p and Ti-3d orbitals. Our band gap calculations are in good agreement with the experiments, which predict a value of 2.9 eV [56].  
In Table 2, we have summarized our results. Our calculations indicate that the gap is reduced more in Mo\({}_{\text{Ti}}\) doped TiO\({}_{2}\) and reaches a value of 2.77 eV (448 nm). Therefore, it is suggested that this type of bandgap engineering of titania can be applied to develop photocatalysts with an absorption within the visible. Generally, in all the above cases, we calculate a significant bandgap reduction. Additionally, we have extensively discussed the creation of gap states in the middle of the bandgap for all the doping cases. These formed states improve the n-type conductivity of titania and serve as donors. As a result, we suggest that their photocatalytic performance through water splitting experiments should be examined, especially for the Mo doping. Although these states can be advantageous for photocatalytic applications, they could be a disadvantage for solar cell devices, as they might constitute trap sites for the photogenerated charge carriers. As a result, we propose only the W doped TiO\({}_{2}\) for application in photovoltaics.  
Figure 2: The partial density of states for (**a**) Cr as an interstitial dopant, (**b**) Cr as a substitutional dopant, (**c**) Mo as an interstitial dopant, (**d**) Mo as a substitutional dopant, (**e**) W as an interstitial dopant, (**f**) W as a substitutional dopant, (**g**) undoped TiO\({}_{2}\).  
Figure 3: The mid gap states for (**a**) Cr as an interstitial dopant, (**b**) Cr as a substitutional dopant, (**c**) Mo as an interstitial dopant, (**d**) Mo as a substitutional dopant, (**e**) W as an interstitial dopant, (**f**) W as a substitutional dopant.
## 5 Conclusions  
In this paper, starting with the first proposed FBFET, various device structures were explored, and the positive feedback mechanism comprising their principle of operation was explained in detail. As a result of the positive feedback phenomenon, FBFETs show excellent subthreshold swing values (\(\sim\)0 mV/decade at 300 K) and high on-/off current ratios (\(\sim\)10\({}^{10}\)), giving them potential for use as next-generation memory cells with hysteresis. FBFETs are applied to 1T-1C DRAM to create a capacitor-less DRAM and to 6T-SRAM to create 2T-SRAM. These novel DRAM and SRAM devices can overcome the limits of conventional devices. FBFETs have also been applied to neuromorphic circuits, which have shown significantly improved performance. They can simultaneously solve both the power consumption and large-required areas. These data confirm the possibility of replacing existing devices with next-generation devices having ultra-low power, high performance, and high density.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] equally contributed to this work. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2020R1A2C1009063). And, this work was supported by the Future Semiconductor Device Technology Development Program (10052925) funded by the Ministry of Trade, Industry & Energy (MOTIE) and the Korea Semiconductor Research Consortium (KSRC).  
**Conflicts of Interest:** The authors declare that there is no conflict of interest.  
Figure 13: (**a**) Illustration of pattern classification with a spiking neural network and (**b**) the proposed neuron circuit diagram for a capacitorless structure with FBFETs for “integration-and-fire” [55; 91]. (**c**,**d**) Transient currents flowing through the trigger device in three different neurons when the same number of input pulses are provided to the neuron circuit [21].
# End-Users' Augmented Reality Utilization for Architectural Design Review
## 3 Research Methodology
### BIM Models for Design Review Tasks and Display Devices  
A one-story residential building was modeled and visualized through three visualization platforms (i.e., a 2D screen, a VR head-mounted display, and an AR headset). The building is separated by seven different areas including a living room, a main hall, a bedroom, a study room, a shower room, a toilet, and a laundry room (Figure 1). The model presented a detailed level of architectural design. The walls and floors inside are decorated with materials with different textures and colors, and each area is equipped with components such as a furnace, sofa, table, sink, and table (Figure 2). The BIM model was built using Autodesk Revit Software, and was imported into the Unity game engine to visualize the model in the VR and AR systems. The size of the model was adjusted in Unity so that users could experience the same magnitude and volume of the model regardless of system type. This process also requires additional software to display the model in VR and AR devices (SteamVR for VR, and Vuforia for AR systems).  
Two groups wore head-mounted devices to use the VR and AR systems; the HTC Vive and Microsoft HoloLens were used for VR and AR, respectively, while the remaining group reviewed the design presented on a 2D screen (Figure 3). The subjects that used a 2D screen (2D screen user group) could directly control the model using a mouse and keyboard whenever they wanted to rotate or zoom in/out. The group with an HCT Vive (VR user group) could walk in and around the virtual model in a fully immersive environment by simply controlling their movement with the arrow keys on a keyboard. Last, the group using a Microsoft HoloLens (AR user group) was required to walk around in the real world to change the scenery of the visual model, which was overlaid into a designated physical space. The participants used the device in front of the desk and walls where no other objects exist; the augmented building information in the HoloLens was not disturbed by other objects in the physical space. To get closer or turn an angle of view in the AR environment, the subjects needed to physically approach or rotate around the area, which is similar to a real-world setting.  
Figure 1: 3D BIM model for design review.  
Figure 2: Interior view of the 3D BIM model.
## 3 c-di-AMP Degradation
### GdpP  
The best-characterized PDE, GdpP, contains the catalytic DHH/DHHA1 domain (DHH stands for Asp-His-His) [44; 45]. This domain is primarily found in phosphatases and phosphodiesrases that regulate the phosphorylation of proteins and breakdown of phosphodiester bonds, respectively [35]. The DHH/DHHA1 domain in GdpP cleaves c-di-AMP into pApA [45]. GdpP is highly specific for c-di-AMP and has been observed to have only very weak enzymatic activity toward c-di-GMP [16; 34; 45]. GdpP and GdpP homologs have been identified in most microorganisms that produce c-di-AMP thus far [45]. The operon containing the \(gdpP\) gene appears to be evolutionarily conserved amongst all GdpP producing bacteria as \(gdpP\) is co-transcribed with the genes of ribosomal protein L9 (RpL9) and a DNA replication protein, DnaC [36; 46]. Thus, the co-expression of GdpP, RpL9, and DnaC likely leads to a decrease in the c-di-AMP level during cell growth and replication [36; 46]. GdpP also possesses a PAS (Per-Arnt-Sim) domain that is involved in phosphodiesterase inhibiting activity. PAS domains are present in many signaling protein molecules and play critical roles as sensory domains in signal transduction pathways [45]. The PAS domain in GdpP can bind to heme, which inhibits the  
Figure 1: Synthesis and degradation of cyclic di-adenosine monophosphate (c-di-AMP). Diadenylate cyclase (DAC) enzymes synthesize c-di-AMP through a condensation reaction of two ATP or two ADP molecules. c-di-AMP binds to specific target proteins, thereby regulating the functions of downstream proteins within a variety of cellular pathways. To maintain appropriate levels of c-di-AMP, phosphodiesterases (PDEs) degrade c-di-AMP into pApA, which further degrades into AMP [22; 43].  
enzymatic activity of GdpP [47]. GdpP also contains a degenerated c-di-GMP synthesizing domain, GGDEF. However, this domain is not able to synthesize c-di-GMP, but, rather, is involved in the degradation of ATP [45]. A mutation in the GGDEF domain causes reduced catalytic activity of GdpP, thus demonstrating a role of the GGDEF domain in the regulation of GdpP catalytic activity [45].
# An Investigation of Applying Large Language Models to Spoken Language Learning
## 1 Introduction
### The Contributions of This Study  
This paper investigates the performance, interpretability, and limitations of LLMs in SLI within the field of spoken language learning. We have collected a composite dataset comprising a set of concepts mainly designed to test the large models' knowledge of spoken language, along with application questions tailored for industrial production. We examine a series of LLMs in our study, which is conducted in two rounds. We comprehensively review their performance on a large scale and consider two prompting strategies--direct and CoT prompting--under both zero- and few-shot learning paradigms. In the second round, we meticulously analyze representative models using advanced optimized methods. The main contributions of this study are as follows:  
* This study introduces a dataset on spoken language intelligence that serves as a substantial benchmark for speech and language learning scenarios, providing valuable supplementation to existing benchmarks.
* This study conducts a study on various prompting strategies (such as zero-shot, few-shot, direct/CoT, domain-specific exemplar, and external tools) and analyzes their performance using multiple-choice questions.  
* This study demonstrates that in-domain example sampling techniques can consistently improve performance on domain-specific data.
* This study conducts an expert evaluation of a small set of multi-turn conversations generated by GPT-3.5. Unless otherwise specified, the results mentioned in this paper regarding GPT-3.5 are based on the GPT-3.5-turbo-0613 model. Error analysis indicates that GPT-3.5 has the potential to enhance conversational spoken language learning.