esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### One-Time Password (OTP)  
A one-time password is an electronic financial transaction authentication service that authenticates users with a different password for each transaction, making it mathematically impossible to guess the next password from the currently used password. By using shared secret information between the institution and the user, such as timing or event information, the algorithm for generating the disposable password is created internally so that attackers cannot guess it on their own. Currently, OTPs are provided as tokens, card-type terminals, and mobile applications (e.g., phones) [12].  
Authentication methods using OTPs are divided into synchronous and asynchronous methods, and are classified into timing synchronization, event synchronization, and combination synchronization [13]. Each method has its advantages and disadvantages, and the timing method may vary, depending on the situation. Timing synchronization requires accurate synchronization between the OTP terminal and the authentication server, and with event synchronization, events generated on the terminal and the server must be synchronized accurately. Combination synchronization complements the problems of timing and event synchronization methods, but issues such as synchronization failure or error range may still occur [14; 15].  
An asynchronous method is an authentication technique that does not require synchronous information between the OTP terminal and the authentication server. An example is the challenge-response method, which sends a query value to a terminal that generates a password, verifies whether the user's response is valid, and performs authentication. When a user requests a transaction using that password, the server sends a random query value to the user, and the user enters the output value from the terminal and sends it back to the server. If the response sent by the user is valid, the server authenticates the user and performs the transaction. However, this challenge-response method has an inconvenience--the user must enter the query value and response value, and when using the communication network, there may be charges for the transmitted data. In addition, the server bears the burden of generating and managing the query and response values.
# Optimal Design of Small-Aperture Optical Terminals for Free-Space Links
## 2 Materials and Methods
### Link Power Loss and Fluctuations  
Only the fraction of power that makes it into the optical terminal can be coupled into fibre. For a given FSO link, this fraction is determined from the geometric loss associated with beam truncation at any aperture along the optical path. Geometric losses can be  
Figure 1: Coupling efficiency, \(\eta_{c}\), versus turbulence strength, \(D_{RX}/r_{0}\). The orange curve is for an uncompensated wavefront, and the blue is for a wavefront with TT compensation. Both curves are evaluated for a = 1.12. The diamonds correspond to values of \(a\) that maximise \(\eta_{c}\) for a specific \(D_{RX}/r_{0}\). The black dashed curve is the factor of improvement gained by TT compensation.  
calculated by using link budget analysis. The net gain from transmitter to receiver, \(G_{link}\), can be expressed through [21]  
\[G_{link}=\frac{G_{TX}G_{RX}}{L_{FS}}\, \tag{7}\]  
where \(G_{TX}\) is the transmitter gain, \(G_{RX}\) is the receiver gain, and \(L_{FS}\) is the free-space loss. For a transmitted Gaussian beam with a \(1/e^{2}\) diameter of \(D_{TX}\) that diverges by its diffraction limit, being received by a circular aperture of diameter \(D_{RX}\) after propagating over a distance L, these three gain parameters are given by  
\[G_{TX}=2\left(\frac{\pi D_{TX}}{\lambda}\right)^{2},\ \ G_{RX}=\left(\frac{\pi D _{RX}}{\lambda}\right)^{2},\ \ \text{and}\ \ L_{FS}=\left(\frac{4\pi L}{\lambda}\right)^{2}. \tag{8}\]  
Additionally, due to time-averaged power losses, the power received by the optical terminal will scintillate due to self-interference of the perturbed beam. These fluctuations are often quantified by the log-amplitude scintillation index  
\[\sigma_{ln,I}^{2}=\ln\left(1+\frac{\sigma_{I}^{2}}{\mu_{I}^{2}}\right)\, \tag{9}\]  
where \(\sigma_{I}^{2}\) is the variance of the irradiance, and \(\mu_{I}\) is the mean irradiance. In general, \(\sigma_{ln,I}^{2}\) will increase with increasing turbulence strength, reaching its maximum value when the beam has been perturbed to the point that it is fully incoherent at the receiver [22].  
The ratio \(D_{RX}/r_{0}\) has a strong effect on the scintillation at the receiver. If the aperture size spans multiple Fried parameters, the receiver will average the power fluctuations of each coherent patch, resulting in a lower scintillation index. This is termed aperture averaging [23]. We are further interested in how a varying aperture size affects the scintillation index of the power coupled into SMF. Given their analytical complexity [17], we use numerical beam propagation simulations to obtain these scintillation indices.
# CarrDet: Attention-Symmetry-Based Microscopic Carrier Detection for Wastewater Treatment
## 2 Related Work  
### Carrier for Wastewater Treatment  
The carrier is typically a material with a large specific surface area and favorable bioadhesive properties [12], such as high concentration powder carriers. These carriers can enrich microorganisms at the surface to improve the amount of biomass in the bioreactor [1]. Adding carriers into wastewater leads to an increased concentration of mixed liquor suspended solids [13] and establishes a symbiotic microbial \"double sludge\" ecosystem comprising suspended growth and attached growth [14]. This ecosystem not only enhances the shock resistance of the WWT system but also improves the loading capacity of pollutants and settling performance of sludge [13]. This system plays a vital role in WWT, as it transforms and degrades organic pollutants from wastewater to meet effluent standards. Moreover, the carrier contains specialized organic alternative carbon sources, like polyhydroxyalkanonates (PHA), which can enhance the biological denitrification process and can substantially improve denitrification efficiency, thereby achieving biological purification of the wastewater [15]. By regulating carrier quantity, an optimal microbial living environment can be sustained, maximizing microbial system efficiency in WWT and ensuring effluent water quality adherence to established standards [2]. Hence, in the urban domestic WWT process, the recognition and quantification of carriers are of paramount importance. While image analysis methods have been used by wastewater treatment plants for the last many years [16], there is still a lack of specialized carrier detection methods in wastewater microscopic images.
# Exposure Time to Work-Related Hazards and Factors Affecting Musculoskeletal Pain in Nurses
###### Abstract  
Nurses who work in hospitals are exposed to various occupational hazards and are recognized as having high rates of musculoskeletal pain. This study aims to estimate the level of exposure to work-related hazards for nurses working in hospitals and derive factors that affect back pain, upper limb pain, and lower limb pain. This study was conducted on 462 nurses from the 6 th Korean Working Condition Survey (KWCS) data, deriving exposure time related to physical, biochemical, and ergonomic hazards. Also, using binomial logistic regression analysis, this study determines the factors influencing musculoskeletal pain by comprehensively considering work-related factors and physical, biochemical, and ergonomic hazards. The exposure time for standing is the highest, followed by repetitive movements, awkward postures, patient lifting/carrying, infection, heavy object handling, and vibration. The average exposure times to occupational hazards were higher for nurses experiencing pain in their back, upper limbs, and lower limbs than those without pain complaints. Factors that contribute to back pain include years of work experience (\(p=0.002\)), type of healthcare establishment (\(p=0.001\)), exposure level to vibration (\(p=0.029\)), and awkward posture level (\(p<0.001\)). Factors affecting upper limb pain include the type of shift work (\(p=0.013\)), handling heavy objects (\(p<0.001\)), awkward postures (\(p=0.033\)), and repetitive movements (\(p=0.002\)). The factors that influence lower limb pain are awkward posture (\(p=0.001\)), patient lifting/carrying (\(p=0.002\)), and repetitive movements (\(p=0.006\)). This study emphasizes the importance of implementing strategies to improve occupational hazards to prevent or alleviate musculoskeletal pain. These findings provide practical guidance for managing risk factors and preventing musculoskeletal disorders among nursing professionals.  
msculoskeletal disorders; occupational hazards; back pain; upper limb pain; lower limb pain +
Footnote †: journal: Applied  
While ergonomic risk factors present the closest association with musculoskeletal pain [1; 3; 4; 8], there is evidence that exposure to physical and chemical/biological hazards is also related to pain [8]. There is a lack of research analyzing the priority and impact of various risk factors on musculoskeletal pain, taking into consideration nurses' job-related characteristics, as well as the duration of exposure to these risk factors. This study aims to investigate whether nurses are exposed to physical, biochemical, and ergonomic hazards and to systematically analyze the priority and degree of impact of these risk factors on musculoskeletal pain, taking into account workers' characteristics and the exposure time to these risk factors among workers with back pain, upper limb pain, and lower limb pain.
# Local Back-Propagation for Forward-Forward Networks: Independent Unsupervised Layer-Wise Training
## 2 Related Works
### Applications and Limitations of the Forward-Forward Algorithm  
The FF algorithm proposed by Hinton [10] introduced a training mechanism that replaces the backward pass with two forward passes using positive and negative sam ples. While FF is notable for its conceptual novelty and biological plausibility, it relies on handcrafted input variants and a non-standard loss function. These constraints limit its practicality in general-purpose learning tasks.  
Several recent studies have attempted to improve upon FF's limitations. Ororbia and Mali [21] integrate Predictive Coding with FF to ensure effective local layer-to-layer learning. Since the FF algorithm operates using only forward passes, closely resembling the function of the brain, it still requires verification of inter-layer information exchange. By incorporating Predictive Coding, Ororbia and Mali [21] have demonstrated that FF can achieve performance close to that of MLP models trained with BP.  
The Self-Contrastive Forward-Forward algorithm [11] refines the construction of positive and negative samples by introducing a more stable contrastive formulation. The Integrated Forward-Forward method [12] introduces shallow gradient feedback to stabilize the learning process while retaining the forward-only property. Additionally, the On-Tiny-Device FF approach [13] investigates the viability of FF training on highly constrained devices by searching for initialization strategies that enhance convergence and performance.  
An attempt to combine FF with federated learning [22] was made by FedFwd [23], aiming to validate its practicality in decentralized environments. While the approach showed promise, its performance remained lower than that of MLP models trained with BP.  
These works share a common goal of improving the stability and applicability of FF, yet they still depend on the core FF structure and do not generalize to broader unsupervised models or standard data formats.
# Single Trace Side Channel Analysis on NTRU Implementation
## 5 Conclusions  
Although the cryptosystem is proven to be secure, the security of its implementation must be considered as the devices may expose side channel leakages. Since PQC cryptosystems are implemented in classical computers, side channel must be considered. In this paper, we analyzed the two versions of NTRU implementation - NTRUencrypt and NTRU Open Source. By using a single power consumption obtained in the decryption, we were able to recover the private key on both implementations. Our attack is practical and powerful since it can be applied without constraints of the environment. We also proposed countermeasures for our attack. Our countermeasures not only prevent our proposed attack but also prevents the previous attack. Moreover, our countermeasures do not degrade its performance. In addition, as the NIST standardization project is still in process, every algorithm including NTRU may provide an updated optimized implementation. The proposed analysis is based on the implementation up to now but more optimized version might appear in the future.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2017R1C1B2004583). All authors have contributed to this work. [PERSON] and [PERSON] analyzed the algorithm and drafted and revised the manuscript. [PERSON] and [PERSON] performed the experiment and analyzed the result. [PERSON], [PERSON], [PERSON], and [PERSON] devised the countermeasure and [PERSON] verified the analytical methods and supervised this work. The authors declare no conflict of interest.
## 2 Numerical Simulations and Wind Tunnel Tests
### Governing Equations  
In the field of structural wind engineering, the maximum wind speed is less than 0.3 Ma, and the atmospheric density is regarded as a constant and remains constant. It is generally considered as an incompressible flow, which should satisfy the law of conservation of mass, momentum, and energy. However, it is considered to be in isothermal stratiform. The air temperature is regarded as constant, and the energy change is ignored. Therefore, in the numerical simulation calculation, the energy conservation equation is generally not considered, and only the mass and momentum conservation equations need to be satisfied. The conservation equations of mass and momentum are shown in the following equation.  
\[\frac{\partial u_{i}}{\partial x_{i}}=0 \tag{1}\]  
\[\rho\frac{\partial u_{i}}{\partial t}+\rho\frac{\partial u_{i}\partial u_{j}} {\partial x_{j}}=\frac{\partial}{\partial x_{j}}\bigg{(}\mu\frac{\partial u_{ i}}{\partial x_{j}}\bigg{)}-\frac{\partial p}{\partial x}+S_{i} \tag{2}\]  
where \(u_{i}\) represents the components of fluid velocity vector in \(x_{i}\) directions, \(\rho\) is fluid density, \(\mu\) is the dynamic viscosity, \(p\) is the pressure, and \(S_{i}\) represents the generalized source term of the momentum equation in the three velocity directions.
## 4 Results and Discussion
### Analysis of the Influence of Time Interval  
In order to evaluate the influence of different time interval for the simulation result, we have also conducted several comparisons.
## 2 Preliminaries and Problem Description
### Parameter Estimation Algorithm  
This paper employs the dynamic linearization technique to address the nonlinearities present in the MHSTs described in Equation (3). This approach introduces a PPD \(\phi_{i}(\ell)\), which establishes a linear relationship between the increments of input/output data, and \(\phi_{i}(\ell)\) effectively captures the system's overall variation. Additionally, the controller design necessitates the use of \(\phi_{i}(\ell)\) as part of its operation. The primary challenge lies in the fact that \(\phi_{i}(\ell)\) is time varying. Specifically, we consider the following criterion functions related to the PPD \(\phi_{i}(\ell)\) in the same way as in the literature [24]:  
\[J(\phi_{i}(\ell))=\left|v_{i}(\ell)-v_{i}(\ell-1)-\phi_{i}(\ell)\Delta F_{i}( \ell-1)\right|^{2}+\mu\big{|}\phi_{i}(\ell)-\hat{\phi}_{i}(\ell-1)\big{|}^{2} \tag{14}\]  
where \(\mu>0\) is a weighting factor, and \(\hat{\phi}_{i}(\ell)\) is the estimated value of \(\phi_{i}(\ell)\).  
By minimizing the criterion function, we derive the following PPD \(\hat{\phi}_{i}(\ell)\) estimation algorithm:  
\[\hat{\phi}_{i}(\ell)=\hat{\phi}_{i}(\ell-1)+\frac{\eta\Delta F_{i}(\ell-1)}{ \mu+\Delta F_{i}(\ell-1)^{2}}\times\big{(}\Delta v_{i}(\ell)-\hat{\phi}_{i}( \ell-1)\Delta F_{i}(\ell-1)\big{)} \tag{15}\]  
To enhance the capability of the PPD \(\hat{\phi}_{i}(\ell)\) estimation algorithm (15) in tracking time-varying parameters, a reset mechanism is incorporated as follows:  
\[\hat{\phi}_{i}(\ell)=\hat{\phi}_{i}(1),\text{ if }\big{|}\hat{\phi}_{i}(\ell) \big{|}\leq\ell\text{ or }|\Delta F_{i}(k-1)|\leq\ell\text{ or }\text{sign}\big{(}\hat{\phi}_{i}(\ell)\big{)}\
eq\text{ sign}\big{(}\hat{\phi}_{i}(1)\big{)}. \tag{16}\]
# An Indoor Room Classification System for Social Robots via Integration of CNN and ECOC
###### Abstract  
The ability to classify rooms in a home is one of many attributes that are desired for social robots. In this paper, we address the problem of indoor room classification via several convolutional neural network (CNN) architectures, i.e., VGG16, VGG19, & Inception V3. The main objective is to recognize five indoor classes (bathroom, bedroom, dining room, kitchen, and living room) from a Places dataset. We considered 11600 images per class and subsequently fine-tuned the networks. The simulation studies suggest that cleaning the disparate data produced much better results in all the examined CNN architectures. We report that VGG16 & VGG19 fine-tuned models with training on all layers produced the best validation accuracy, with 93.29% and 93.61% on clean data, respectively. We also propose and examine a combination model of CNN and a multi-binary classifier referred to as error correcting output code (ECOC) with the clean data. The highest validation accuracy of 15 binary classifiers reached up to 98.5%, where the average of all classifiers was 95.37%. CNN and CNN-ECOC, and an alternative form called CNN-ECOC Regression, were evaluated in real-time implementation on a NAO humanoid robot. The results show the superiority of the combination model of CNN and ECOC over the conventional CNN. The implications and the challenges of real-time experiments are also discussed in the paper.  
Keywords:social robots; NAO robot; room classification; convolutional neural network; multi-binary classifiers; ECOC +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Sentinel-2 MSI Data Acquisition  
Sentinel 2 MSI Level 2A remotely sensed data were imported from Copernicus in the GEE data catalogue. Sentinel 2 is a pair of twin satellites (Sentinel-2A and Sentinel-2B) in the same orbit, phased 180\({}^{\circ}\) apart, operating at an altitude of 786 km, and designedto provide medium-spatial-resolution imagery in global environmental monitoring [70]. Both satellites are equipped with an MSI deriving data from 13 spectral channels along a 290 km sun-synchronous orbital path [71]. The two-satellite constellation provides a temporal resolution of 5 days under cloud-free conditions [72], which makes it suitable for detecting seasonal changes in grass productivity. Specifically, the GEE provides Level 2A products derived from Sentinel-2A that are readily processed by the Sentinel-2 atmospheric correction processor (Sen2 Cor) as a default configuration to provide orthorectified atmospherically corrected surface reflectance [73].  
Remotely sensed imagery with dates that coincided with the field data collection dates for both seasons were acquired. The filter image collection by date function was used to acquire images in the date range that coincided with the time when field surveys were conducted. This is necessary since changes in atmospheric conditions between on-site data collection and satellite data acquisition may affect grass properties and might alter model accuracy [74]. Prior to image downloading, the cloud pixel percentage was set to 10% to filter out Sentinel-2 scenes with a cloud cover of more than 10%. Subsequently, the median reducer function was applied to create a single image by calculating the median of all values at each pixel across the stack of all matching bands. The study area shapefile was imported, and the image was filtered to narrow it to the location of the region of interest. The ground sampling distance (GSD) of the Sentinel-2 MSI is divided into spectral bands with 4 bands (10 m), 6 bands (20 m), and 3 bands (60 m). Bands acquired at 60 m GSD (1, 9, and 10) were not included in this study because they are designed for detecting atmospheric characteristics and are unsuitable for vegetation analysis [75]. As a result, only 10 bands were selected from the image collection and utilized in this study. The Sentinel-2 MSI bands were resampled to a spectral resolution of 10 m to give all the bands the same pixel size.
# Smart Bandwidth Assignation in an Underlay Cellular Network for Internet of Vehicles
## 1 Introduction
In particular, there has been a reuse of gain stems from the fact that D2D and cellular links can share the same radio resources. Thus, in a D2D underlay cellular infrastructure, the same resources can be simultaneously utilized by the V-UE (Vehicular User Equipment) involved in V2V communication and by the ordinary C-UE (Common User Equipment), that is, a regular mobile user. This could improve the utilization of cellular spectrum and reduce the energy consumption of V2V communications. However, the reuse gain comes at the expense of increased interference level what in turn has to be neatly controlled by the network. In this context, the design of an efficient RRM for V2V communications which underlays the cellular network with minimal impact on cellular communications is a key problem. Therefore, efficient RRM is indisputably an element of paramount importance for deploying ITS applications with different QoS requirements. Consequently, the motivation of this paper is to design an underlay RRM capable of improving cellular spectral efficiency while trying not to affect the performance of C-UEs' communications. In this way, one of the contribution of this paper is that we design a three-step methodology to, first, mitigate the interferences generated by sharing radio resources between C-UEs and V-UEs and, secondly, to ensure the QoS requirements for the transmission of CAMs (Cooperative Awareness Messages) by vehicles. In this methodology, we, first, calculate which are the possible C-UE-V-UE pairs that could share radio resources; then, we define the transmission powers that satisfy both transmitters QoS requirements; and finally, using a metaheuristic, which gives an additional originality to our study, we select the pairs that allow us to provide a more efficient radio resource assignment than the RRMs we are comparing them against. In addition, the main innovation of this paper is that, before applying the presented RRM, we organize the vehicles in clusters to reduce infrastructure connections and improve radio efficiency. That is, we understand each cluster as a moving zone to whom we assign variable-size pools depending on the transmission needs of vehicles of each cluster. In this way, the CH is the vehicle in charge of asking for resources to the infrastructure and distributing them among the CMs (Cluster Members). Therefore, the novelty of our proposal arises from how we allocate resources among V2X clusters with a presence of non-V2X UE (User Equipment)on the same bandwidth. In the remaining sections, the paper is organized as follows. In Section 2, a literature review about RRM for D2D and V2V communications is presented. Section 3 presents the system model and problem definition. Next, Section 4 explains the Radio Resource Allocation procedure and Section 5 the simulation conditions and results obtained to validate our proposal. Finally, Section 6 presents a discussion about results and Section 7 presents the paper's conclusions.