esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on the Impact of Intelligent Surfaces in the Terahertz Communication Channel Models
## 1 Introduction
In [11], two 2.92 mm thick glass layers with 1.96 mm air in between were analyzed to absorb 500 GHz waves by an absorption coefficient of 15 and a refractive index of almost 2.6. THz communications will experiment with multiple reflections in non-line of sight (NLoS) propagation, causing significant variations around the local average power. 6G systems must take those phenomena into account to be able to capture the arriving THz signal. In addition, solid-state circuits, antennas, and beamforming and beamtracking techniques still need to be enhanced to be cost-efficient and energy-efficient [12]. To overcome the challenges mentioned earlier, one of the enabling THz communication technologies being considered is the reconfigurable intelligent surface (RIS). This is essential to smart radio environment (SRE), which signifies a wireless environment that is programmable and controllable. RISs are generally defined as an intelligent thin composite material sheet that can cover walls, ceilings, buildings, and other structures capable of modifying radio waves impinging on it in a configurable way by means of external stimuli [13]. They are compared to mMIMO technology, except they can alter the channels' characteristics between the transmitter and receiver. RIS can be used to create NLoS links in dead zone coverage areas, steer signals towards specific directions, suppress interference, build desired destructive interactions, increase channel capacity, enhance focus, uplift radio localization, recharge sensors, and recycle radio waves [13; 14]. The authors of [15] showed that RIS can help wireless systems reduce active antenna chains while maintaining performance. In [16], the authors employed RIS for a downlink multi-user communication from a multi-antenna base station that provides up to 300% higher energy efficiency than regular multi-antenna amplify-and-forward relaying. An optimum phase shift that provided spectral efficiency degradation of less than 1 bps/Hz with a 2-bit quantizer was designed for a RIS-assisted large-scale antenna system in [17]. The authors in [18] utilized RIS in a multi-cell communication system to mitigate inter-cell interference and have studied the optimum location of RISs inside a four-cell communication scenario. Finally, RISs were exploited in [19] to provide additional communication links to increase both the signal-to-noise ratio (SNR) for legitimate receivers while suppressing the SNR at eavesdroppers.
* loss of service;
* loss of accuracy;
* inability to estimate any position boundary (e.g., integrity information, also called Quality of Service, QoS, in mass market terminology).  
The fusing algorithms for GNSS data blend several types of information: vehicle odometer data and data from onboard inertial measurement unit [2]; Light Detection and Ranging (LIDAR) Simultaneous Localization and Mapping (SLAM) data and data from the Inertial Navigation System (INS) [3]; or camera-deduced information. The data acquired by cameras are very useful, taking into account the large amount of information contained in images [4].  
Fusing GNSS receiver information with camera-based information can present several advantages, especially in order to provide a QoS indicator to the supplied position. Indeed, whereas GNSS signals are distorted by the surrounding environment, on its side, a camera is able to observe the environmental scene. Adapted image processing techniques may provide key information about the local environments and thereof about the potential impacts on the GNSS signal processing.  
For example, a fisheye camera mounted on the roof of a vehicle can give information to the GNSS mounted on the same vehicle about the NLoS satellites in a specific point of the vehicle's trajectory. This information can be used by the GNSS for the selection of the best satellites (those that are in LoS) for positioning. In this paradigm, the image processing module, which processes the information given by the fisheye camera, is responsible for giving information about sky/non-sky regions in each frame acquired by the camera.  
The aim of this paper is to investigate the usefulness of such a solution for GNSS-based localization of vehicles in constraint environments by estimating some QoS indicators. Our solution supposes the utilization of a GNSS system and of a fisheye camera connected to an image processing module.
###### Abstract  
The objective of this study was to investigate the aroma profile and major flavor compounds in Xiaoqu liquor by a combination of sensory profiling, GC-O/GC-MS analysis, and SBSE-GC-MS. A total of 57 important volatile compounds (FD \(\geq\) 2) were screened based on the retention indices, aromatic characteristics, standard comparison, and aroma extract dilution analysis (AEDA). By calculating the odor activity values (OAVs), 32 aroma active compounds were identified in Xiaoqu liquor. In the aroma recombination experiments, these active compounds were dissolved in 46% ethanol water at their natural concentrations, resulting in successful simulations of the typical aromas of Xiaoqu liquor, including fruity aroma, sweet aroma, and grain aroma. Omission experiments showed that the critical compounds that contribute to the characteristic flavor of Xiaoqu liquor include ethyl octanoate, acetaldehyde, 1,1-diethoxyethane, isoamyl acetate, ethyl acetate, valeric acid, 1-octen-3-ol, and ethyl isovalerate. The results provide some guidance for upgrading the fermentation and microbial strain in the production of Xiaoqu liquor.  
Xiaoqu liquor; aroma extract dilution analysis; odor activity value; sensory characterization; flavor compounds +
Footnote †: journal:  
Article
# Low Complexity List Decoding for Polar Codes with Multiple CRC Codes
## 3 Low Complexity SCL Decoding with Multiple CRC Codes
### Encoding and Decoding of the J-Tuple CRC Scheme
The SC decoding decides the value of \(u_{i}\) so that the  
Figure 3: The cyclic redundancy check (CRC) encoding of the \(J\)-Tuple CRC scheme. temporary list size is maintained as \(l_{i+1}=l_{i}\) even if \(l_{i}<L\). Because \(u_{i}\) for \(i\in\mathcal{A}_{g}\) is highly reliable, the correct path may not be ruled out by the value decision of \(u_{i}\). The list decoding with this criterion is termed the \"instant-decision-aided SCL (ID-SCL) decoding\". (Here, we view the conventional SCL decoding operation as a delayed decision.) In fact, the instant decision significantly reduces the complexity even though multiple CRC codes are not applied because it skips sorting operations that account for a large portion of the total complexity. In addition, the instant decision also has an effect of maintaining the temporary list size lower. Therefore, it is expected that the multi-CRC scheme including its optimization can also reduce the complexity under the ID-SCL decoding. In the next subsection, the method of how to optimize CRC positions and determine \(\mathcal{A}_{g}\) is explained based on the aforementioned encoding and decoding methods. Note, however, that the \(J\)-tuple CRC scheme can be decoded with or without the instant bit decision. Obviously, CRC positions can be also optimized without the instant decision. The decoding without the instant decision is called the basic SCL decoding for the \(J\)-tuple CRC scheme.
## 4 Remote Sensing Platforms Used in Peatland Research
### Multiple Platforms
#### 4.5.4 Challenges and Future Directions  
Validation of remote sensing-based estimations remains a challenge in peatland research. [PERSON] et al. (2018) highlight the need for validation using higher-resolution data, such as airborne hyperspectral imagery (HSI), and in situ measurements to improve accuracy. This is crucial for ensuring reliable and precise estimation of peatland properties [141]. [PERSON] et al. (2017) emphasise the significance of interdisciplinary collaboration and knowledge integration for comprehensive peatland analysis. They emphasise incorporating ecological and hydrological knowledge with remote sensing technologies. Future research should focus on integrating multiple data sources, including environmental covariates and machine learning algorithms, to enhance the understanding and modelling of peatland ecosystems [38]. Additionally, future research should focus on refining multiple approaches and methodologies, improving calibration techniques, and integrating multi-sensor data to enhance our understanding of peatland ecosystems and their response to global environmental changes. We found that just a few studies have investigated the benefits of hyperspectral drone data compared to other datasets for analysing vegetation patterns in peatlands. Future studies should explore the potential benefits of hyperspectral UAV data in high-resolution estimation of vegetation features and patterns on peatlands. This exploration may enhance not only mapping of vegetation but also the monitoring of peatland restoration efforts. \begin{table}
\begin{tabular}{|c c c c c c c c c c c|} \hline \hline
**Reference** & \ \begin{tabular}{c} **Peatland** \\ **Type \({}^{*}\)** \\ \end{tabular}  & \begin{tabular}{c} **Peatland** \\ **Status** \\ \end{tabular}  & **Cominant Vegetation** & **Active/Passive** & **Spaceborne** & **Airborne** & **UAV** & **In Situ** & **Applications** \\ \hline
[146] & Bog & Disturbed & \begin{tabular}{c} Gramindol, _Ericase_ spp., \\ _Sphagram_ spp. \\ \end{tabular}  & Active & & \(\bullet\) & & \(\bullet\) & Carbon stocks estimation \\ \hline
[147] & Peatland & Disturbed & \ \begin{tabular}{c} _Sphagram_ spp., _Carc_ spp., shrub \\ \end{tabular}  & Passive & & \(\bullet\) & & \(\bullet\) &
\begin{tabular}{c} Characterising
## 2 Overview and Modeling of the AUH
### Tracking Differentiator  
A tracking differentiator is a type of control algorithm used in control systems to estimate the derivative of the input signal of the system. It is a high-frequency filter that can accurately estimate the derivative of the input signal, even in the presence of noise and disturbances. The tracking differentiator can be used in various control applications, such as motion control, robotics, and aerospace systems. It is particularly useful when the input signal is noisy or when the system is subject to disturbances that can affect the accuracy of the derivative estimation.  
In classical control theory, the differential signal for a given signal \(v\) is generally represented, as seen below [23].  
\[y=w(s)v=\frac{s}{Ts+1}v=\frac{1}{T}(v-\frac{1}{Ts+1}) \tag{9}\]  
Equation (8) can be written as follows.  
\[w(s)=\frac{s}{\tau^{2}s+2\tau s+1}=\frac{{r_{a}}^{2}}{\left(s+{r_{a}}\right)^{ 2}}s \tag{10}\]  
Running it on a computer, we have to discretize Equation (9) [23].  
\[\left\{\begin{array}{l}m_{1}(n)=m_{1}(n-1)+m_{2}(n-1)h\\ m_{2}(n)=m_{2}(n-1)+\left[{r_{a}}^{2}(m_{1}(n)-\overline{v}(n))-2{r_{a}}m_{2}( n-1)\right]h\end{array}\right. \tag{11}\]  
where \(h\) refers to the sampling step size and \(r_{a}\) denotes the speed factor. The larger the \(r_{a}\), the faster the signal tracking speed. By combining LADRC with TD, the output of the controller LADRC-TD is \(u^{\prime}\).  
\[\left\{\begin{array}{l}e_{1}{}^{\prime}=m_{1}-\hat{x}_{1}\\ e_{2}{}^{\prime}=\hat{x}_{2}-m_{2}\\ u_{0}{}^{\prime}=K_{p}\cdot{e_{1}}^{\prime}-K_{d}\cdot{e_{2}}^{\prime}\\ u^{\prime}=\frac{u_{0}-\hat{x}_{3}}{b_{1}}\end{array}\right. \tag{12}\]  
In summary, the block diagram of the LADRC-TD algorithm can be obtained (Figure 6).  
In the case of the AUH, the dynamics can be decoupled into vertical and horizontal movements, where the vertical propellers control the depth of the vehicle, and the horizontal propellers control the heading. By decoupling the dynamics, it becomes easier to design control systems that can effectively control the vehicle's motion in each direction. This approach helps to simplify the control system design and ensure that each subsystem is optimized for its specific task, resulting in improved overall control performance. In the control system described in the paper, the LADRC-TD is used for both the depth controller and the heading controller of the AUH. The control flow for the AUH can be summarized as follows (Figure 7).
# Random Exploration and Attraction of the Best in Swarm Intelligence Algorithms
## 4 Revision of the PSO and GWO Algorithms
### The PSO Algorithm  
The general idea of PSO is that each particle is moved in the direction that results from the sum of three directions:  
* Inertial, which points in the same direction as the previous movement.
* Cognitive, that points toward the best historic particle position.
* Social, that points toward the best historic position of the swarm.  
In Figure 4, these directions are depicted; in this figure the unknown optimum position is denoted by \(\chi^{*}\).  
To describe the PSO implementation, let us denote the swarm as a list of \(m\) particles, that is  
\[S=(\chi_{1},\chi_{2},\cdots,\chi_{m}) \tag{19}\]  
where \(\chi_{i}\) is the position of the \(i\)-th particle of the swarm. In [62], particles are moved by  
\[v_{i}^{k+1} =\underbrace{\omega\cdot v_{i}^{k}}_{\text{inertial vel.}}+ \underbrace{k_{c}\cdot\rho_{1}\circ(\chi_{i_{hb}}^{k}-\chi_{i}^{k})}_{\text{ cognitive vel.}}+\underbrace{k_{s}\cdot\rho_{1}\circ(\underbrace{s_{hb} ^{k}-\chi_{i}^{k}}_{\text{social vel.}})}_{\text{social vel.}} \tag{20a}\] \[\chi_{i}^{k+1} =\chi_{i}^{k}+v_{i}^{k+1} \tag{20b}\]  
where \(v_{i}^{k+1}\) is the next direction the particle will be moved towards; \(\chi_{i_{hb}}^{k}\) is the best historic position of the particle; \(s_{hb}^{k}\) is the best historic position of the swarm and the constants \(k_{c}\) and \(k_{s}\) are known as cognitive and social factors, respectively. Note that \(v_{i}^{k}\) is composed by three terms named inertial, cognitive, and social velocity. The main characteristics of these velocities are:  
* Inertial velocity results in a vector with the same direction as the previous movement of the particle, scaled by \(\omega\).
* Cognitive velocity results in a random vector within the region \(\cup\chi_{i}^{k},\chi_{i_{b}}^{k}\), scaled by \(k_{c}\).
* Social velocity results in a random vector within the region \(\cup\chi_{i}^{k},s_{hb}^{k}\), scaled by \(k_{s}\).  
These three velocities combined make the particles move through the search space. This movement can be thought of as a random exploration of the space. At the same time, particles are attracted to the best-known positions. Since the particle's initial positions and velocities are randomly generated, at the initial stages of the algorithm, historical best particle positions and swarm best positions change frequently. After a few iterations, the best positions change less frequently.  
Figure 4: Movement of a search agent in the PSO algorithm.  
This can be thought of as the beginning, the attraction of the best is loosely and such attraction becomes stronger as the algorithm evolves. Therefore, the PSO can be seen as a combination of REAB procedures; random exploration is more significant during the initial iterations; as the algorithm progresses, random exploration decreases in importance, and attraction of the best becomes prioritized.
## 2 Materials and Methods
### Study Framework  
The main goal of this research was to identify the factors affecting the mortality of patients undergoing 3-vessel primary PCI and construct a prediction machine learning model. For this aim, a three-stage framework was designed. In the first stage, data of patients who underwent 3-vessel PCI for the first time between 1 January 2007, and 31 December 2009, were extracted (operation codes 33078A and 33078B) from the NHIRD in Taiwan (n = 2647). The exclusion criteria were missing information (n = 6) and patientswho underwent PCI in 2002-2006 (n = 304). Finally, 2337 patients were enrolled in this study; 1188 (50.83%) survived, and 1149 (49.17%) died. The initial inpatient date of PCI was set as the index date. The final follow-up time was the day of death or follow-up until the end of the time in the database (31 December 2019). Death records were extracted from the National Death Registry. Figure 1 presents the overall conceptual framework.  
Figure 1: Processing framework of patients who underwent first-time PCI between 2007 to 2009. CART, classification and regression tree; LGR, logistic regression; MARS, multivariate adaptive regression splines; NHIRD, National Health Insurance Research Database; PCI, percutaneous coronary intervention; RF, random forests; XGboost, extreme gradient boosting.  
After selecting the population (stage 2), we trained five different machine learning models using five cross-validations to find the optimal features. A fivefold cross-validation approach was applied to divide five equal subset folds in the training dataset randomly, and four subset folds were used to build the model as a training dataset. The remaining fold (1-fold) was used to validate the model. The five machine models included LGR, classification and regression tree (CART), multivariate adaptive regression splines (MARS), random forests (RF), and extreme gradient boosting (XGboost). In the final stage, the feature scores for each method were averaged, and single and ensemble prediction models were implemented; finally, the final prediction result was evaluated.
## 3 Methodology
### Datasets
#### 3.1.1 Open Source Environmental Sound Datasets  
Table 1 summarizes the most popular open source datasets for urban sound and ESR, which are used in our methodology to identify the most suitable sound recognition systems to be embedded. Although most of the categories in these datasets are not directly urban related, they present enough representative urban sounds to be considered for our purpose.  
* **BDLib** The authors in [1] created an audio dataset composed of 12 categories representing real-life situations. The collected audio is from sources such as _BBC Complete Sound Effects Library_, _Sony Pictures Sound Effects Series_ and the online sound repository called _Freesound_[11]. Each category has 10 audio files of ten seconds each, with great variation between them.
* **UrbanSound** UrbanSound is a large audio dataset of 12 GB with around 18.5 h of labelled audio [3]. This dataset is exclusively composed of urban sound grouped in 10 categories. Similarly to the BDLib, this dataset is created by manually filtering and labelling every recording from the online sound repository called _Freesound_[11]. Although a larger subset of 4-second audio clips called UrbanSound8K has been also created based on the UrbanSound dataset that is not considered here.
* **ESC** An annotated collection of more than 2000 short audio clips belonging to 50 different categories is presented in [2]. This dataset (ESC-50) comprises not only urban sounds but also present a large number of categories including environmental recordings such as animal sounds, domestic sounds or natural soundscapes. A shorter dataset version recognizing 10 audio categories (ESC-10) is used in this work.
## 2 Related Work  
This literature review covers studies published between 2017 and 2024, focusing on object detection, multi-object tracking, and velocity estimation. Relevant sources were retrieved from academic databases including IEEE Xplore, ScienceDirect, and SpringerLink. The main keywords used in the search included \"object detection\", \"small-object detection\", \"multi-object tracking\", and \"cutting recognition\". Priority was given to works with strong relevance to industrial applications, fine-grained target detection, and real-time processing techniques. Studies focused solely on static 2D images or tasks unrelated to our problem domain were excluded.
# Carbon Footprint Assessment on the Viability of Utilizing Brewer's Spent Grain to Produce Biochar
## 4 Discussion
### Limitations  
The most significant limitation for this study is the scale at which calculations were performed. Generally, as the scale increases, the cost (monetarily, energetically, and resource-wise) tends to decrease while maintaining the desired output, thereby improving the system. This analysis does not consider construction of the buildings or equipment used in the process, which could contribute to a higher GWP, depending on the timeframe of the analysis.  
There were multiple minor data collection limitations:  
1. Collecting data on the pH of caustic waste proved challenging, and, therefore, confirmation was only obtained from a few breweries;
2. In the calculated model, the oven would need to be filled to full capacity, potentially requiring drying times exceeding 6 h;
3. The calculations for grinding were based on a small coffee grinder, which would not be efficient for large-scale production;
4. Although the recorded average nitrogen loss was 50 L per run, the calculations indicated a requirement of 170 L for the duration of the run, suggesting an error with the flow meter. The actual flow rate for nitrogen depleted was closer to 15 mL/min, while the flow meter read 5 L/min;Regarding the syngas and bio-oil recovery calculations, the reference study employed an auger reactor (1 kg biomass per hour), while the present study used a smaller-scale pyrolysis furnace (30 g per run);  
One final major limitation to consider is the extrapolation of data to calculate the GWP of utilizing biochar for soil amendment and recovering bio-oil and syngas for energy. The data collected utilize different biomass precursors and slightly different pyrolysis conditions. The BSG-to-biochar model excludes transportation of biochar to a farm for soil amendment and transportation of the BSG to a central processing facility.
## 5 LPWAN Security Analysis
### Availability
Physical attacks which can be committed by disgruntled employees/ex-employees, agents, protesters, thieves, etc., aim to tamper, manipulate, expose, delete, or acquire access into LPWA networks, thereby inducing integrity attacks include node tampering and side-channel attacks on the network. In node tampering, the intruder can capture an end device and initiate the collection of vital information from it thereby compromising the whole network. Furthermore, the intruder can destroy or replace the node device and security keys can be stolen and compromised [87; 88]. On the other hand, in a side-channel attack, an attacker can snoop or exploit vulnerabilities from the implementation of the network hardware. In such attacks, timing information, electromagnetic leaks or even sound wave, etc. can be snooped on, in order to monitor and gather information on network events as well as network keys. The information collected can then be used to compromise the network. As IoT devices including LPWAN end nodes are mostly Complementary Metal-Oxide-Semiconductor (CMOS) based, they are highly vulnerable to this form of attack. To mitigate these attacks, the use of various hardware mechanisms which can ensure proper authentication and access control mechanisms, tamper resistance and other security mechanisms such as the secured elements (SE) proposed in [89; 90; 91; 92; 93; 94] are particularly important for IoT devices. Hardware-based secure elements can provide a high level of security required by various IoT applications. SE is a microprocessor chip which can store confidential information. The SE architecture is equipped with hardware devices component, such as a cryptographic co-processor, secure random number generator, secure memory, and tamper-proofingtechnology. Apart from physical attacks, natural disasters, wear and tears can also disrupt the normal operation or initiate the loss of LPWAN components such as end nodes such as sensors or actuators.