esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Research on Utilizing Various Video Sources  
Utilizing various video sources for object detection is a crucial area, considering the unique characteristics of each source for fast and accurate detection.  
In modern society, diverse video sources such as CCTV, dashcams, and smartphones are rapidly spreading. Each of these sources has distinct characteristics, requiring special considerations for object detection. CCTV is widely installed in public places, commercial facilities, and residential areas. Dashcams are used to record situations on the road, while smartphones serve as portable video recording devices for swiftly capturing events in one's surroundings.  
Research on object detection in CCTV videos: Studies [8; 9; 11] address the detection and tracking of objects in real-time CCTV scenarios. One study [9] specifically explores detecting weapons using various models on real-time CCTV footage. Another study [10] introduces a research approach utilizing heterogeneous training data and data augmentation to maximize detection rates in CCTV scenes. This research focuses on modeling and predicting the evolution of unique camera parameters using spatial transformation parameters of objects, optimizing the detector accordingly.  
Research on object detection in dashcam videos: One study [11] presents an example of using state-of-the-art image processing algorithms on dashcam videos to safely detect traffic signals while driving. Another study [12] proposes the Dynamic Spatial Attention (DSA) Recurrent Neural Network (RNN) to collect and publish datasets for predicting accidents using dashcam videos.  
Additionally, in another study [13], the utilization of dashboard cameras is proposed to develop a practical anomaly detection system. Focusing on driver safety issues such as lane departure and following distance, traditional model-based computer vision algorithms have limitations in addressing the diversity of risks on the road. This study emphasizes the importance of an approach specialized for dashcam data. Furthermore, another research study [14] aims to detect road anomalies, such as potholes, in dashcam videos to alert drivers about road irregularities and reduce accidents.  
Research on object detection in smartphone videos: Various studies explore object detection using smartphone videos. One study focuses on detecting pests using smartphone videos [15], while others [16; 17] propose real-time object recognition systems optimized for speed and minimal performance degradation in the constrained resource and power consumption environment of smartphones.  
Furthermore, research [6; 7] aims to improve the accuracy of object detection by combining information from various devices at different viewpoints. The growing number of studies on object detection and tracking using videos collected from multiple cameras demonstrates continuous and effective development in the field. These diverse studies underscore ongoing efforts to understand and enhance object detection in various environments, from CCTV and dashcam to smartphone images.
## 3 Novel Approach EKF Tuning
### Central Limit Theorem  
The Central Limit Theorem (CLT) supports the proposed method by ensuring that, as the number of samples increases, the distribution of the sample tends to approach a normal distribution. This is particularly beneficial in Monte Carlo simulations, where the noise modeled by numerous random processes aggregates into a Gaussian distribution, simplifying the analysis and enhancing the reliability of statistical measures such as the covariance matrices derived from the simulation. This alignment of the sample distribution with the properties of Gaussian noise helps in making more accurate and robust predictions about the behavior of INS errors.  
As discussed by [PERSON] [46] and [PERSON] [47], the application of the CLT to the Markov process is particularly relevant, as it allows for the modeling of complex stochastic processes where the transition probabilities change over time, ensuring that asymptotic normality can be achieved under certain conditions. This is especially pertinent for inertial sensor errors, which often follow a Markov process. These errors, including biases and random drifts in accelerometers and gyroscopes, can be modeled as first-order Markov processes [1; 10; 30]. This modeling captures the temporal correlations inherent in the sensor errors, allowing for more accurate tracking and prediction of navigation parameters.  
In conclusion, the CLT within Monte Carlo simulations ensures that the distribution of tuning parameters estimates tends toward normality, even in complex stochastic processes like those modeled by first-order Markov processes. This approach simplifies the analysis and ensures that predictions regarding INS errors converge to values closer to the real ones, leading to more reliable INS/GNSS integrations.
## 3 Results and Discussion
### Metabolic Products during Degradation  
The metabolic products of beta-cypermethrin in BCC01-involved degradation were detected by GC-MS. As shown in Figure S1, six by-products were identified as \(\alpha\)-hydroxy-3-phenoxy-benzeneacetonitrile, 3-phenoxybenzaldehyde, methyl-3-phenoxybenzoate, 3,5-dihydroxybenzoic acid, 3,4-dihydroxybenzoic acid, and 3,5-dimethoxyphenol. After five days, the parent compound and its metabolites were hardly detected, showing that strain BCC01 decomposed beta-cypermethrin entirely into chemicals with simple structures. According to the metabolites obtained, a potential degradation pathway for the degradation of beta-cypermethrin by BCC01 was devised (Figure 3). In it, \(\alpha\)-hydroxy-3-phenoxy-benzeneacetonitrile and permethric acid were the first products to come through the cleavage of the ester linkage of beta-cypermethrin and spontaneously convert into 3-phenoxybenzaldehyde, which is consistent with a previous report [36]. Methyl-3-phenoxybenzoate was formed by the oxidation of 3-phenoxyzaldehyde and then broken into 3,5-dihydroxybenzoic acid and 3,4-dihydroxybenzoic acid via the ester cleavage. Next, 3,5-dimethoxyophenol was produced as the result of group addition or substitution of 3,5-dihydroxybenzoic, which can be mineralized into CO\({}_{2}\) and H\({}_{2}\)O. This degradation pathway was slightly different from that reported previously. The different degradation pathways of micro-organisms isolated under different conditions indicated that it is feasible to use micro-organisms from different conditions to degrade different insecticides, and the optimal cultivating condition could be illuminated as the method used in this study.
# Study on the Anti-Interference Performance of Substrate-Free PEDOT:PSS ECG Electrodes
## 2 Materials and Methods
### Signal-to-Noise Ratio  
To further determine the degree of influence of different factors on ECG, in addition to spectral analysis, signal-to-noise ratio (SNR) was calculated. In this paper, 0.5 Hz high-pass filtering was performed on the original ECG data to filter out the baseline drift [26; 27]. Next, a low-pass filter of 35 Hz was applied to filter out high-frequency interference [28]. Then, wavelet variation thresholding was performed using DB8 wavelet type for noise reduction [29] and finally the waveform was smoothed to obtain the ECG data after most of the interference was filtered out and the SNR was calculated for the signal data before and after. The larger SNR indicates that the interference to the ECG is smaller. Due to the differences in the body, skin condition, and electrode attachment position of the subjects in different groups of experiments, only the data of the same group were compared. The SNR calculation equation [30] is as follows:  
\[SNR=10 lg\Bigg{\{}\frac{\sum_{i=1}^{n}{y(i)}^{2}}{\sum_{i=1}^{n}{[x(i)-y(i)]}^{ 2}}\Bigg{\}} \tag{1}\]  
where \(x(i)\) is the original ECG data, \(y(i)\) is the denoised ECG data, and n is the number of ECG sampling points.
# Noise-Free Security Assessment of Eviction Set Construction Algorithms with Randomized Caches
## 5 Analysis of Eviction Set Construction Algorithms
### Behavior Analysis of Eviction Set Construction
#### 5.2.4 Prime-Prune-Probe Algorithm  
Figure 7 depicts the success rate of the PPP algorithm as a function of the candidate set size. As shown, the PPP algorithm built an eviction set from smaller candidate sets and therefore, was faster than the group testing algorithm. This was due to the use of different candidate sets in each iteration (see Algorithm 3). Intuitively, we expected that by increasing the candidate set size \(n\), the success rate of the algorithm would keep increasing. Our experiments suggested the opposite in the case of PPP, as shown in Figure 7. When the size of the candidate set was greater than \(2N\) (\(N=SW\) being the number of cache lines), we found that the algorithm failed to find an eviction set despite the presence of sufficient congruent addresses in the candidate set. This effect is not studied in [53].  
By analyzing the memory accesses of the Prime-Prune-Probe algorithm, we observed that the _Prune_ filter became more aggressive when the candidate set contained more than \(W\) congruent addresses among the \(n\) elements. It turns out that it removed all congruent addresses from the candidate set. Consequently, the algorithm could not detect collisions within the victim's accesses.  
Figure 8 illustrates this effect by using a small cache memory with an LRU replacement policy. We used the Prime-Prune-Probe algorithm to construct a minimal eviction set targeting set one. As shown in the first step, the attacker accessed the candidate set containing four congruent addresses. Since the cache was a two-way set-associative cache, the cache memory only contained the last congruent addresses (addresses C and D) in set one after the Prime step. In the Prune step, the attacker accessed the same candidate set again and removed the missing addresses from the candidate set to avoid any noise caused by self-eviction. Since the first two addresses, A and B, were evicted from the cache, the Prune filter found them missing from the cache after accessing them. The other two congruent addresses, C and D, were then evicted from the cache due to accessing addresses A and B. The Prune filter considered them as missing addresses and therefore, removed them from the candidate set. In this case, all congruent addresses were removed from the candidate set, which explains why the Prime-Prune-Probe algorithm failed when the candidate set was large.  
Figure 7: The success rate of the Prime–Prune–Probe algorithm with different candidate set sizes.
###### Abstract  
The surface cold working (SCW) of austenitic stainless steel (SS) causes martensitic transformation in the surface layers, and the percentage fraction of the strain-induced martensite depends on the degree of SCW. Higher content of \(\alpha^{\prime}-\)martensite increases the surface micro-hardness and fatigue strength, but deterioration of the corrosion resistance is possible. Therefore, the desired operational behaviour of austenitic SS can be ensured by the corresponding degree of SCW and heat treatment. This article evaluates the effects of SCW performed by diamond burnishing (DB) and heat treatment on the surface integrity (SI), rotating fatigue strength, and corrosion resistance of AISI 304 austenitic SS for two initial states: as-received hot-rolled bar and initially heat-treated at 1100 \({}^{\circ}\)C for one hour followed by quenching in water. Then, DB was implemented as a smoothing and hardening process, both alone and in combination with heat treatment at 350 \({}^{\circ}\)C for three hours after DB. The electrochemical performance was examined by open circuit potential measurements, followed by potentiodynamic tests. For both initial states, smoothing DB provided the lowest roughness, whereas an improvement in the maximum surface micro-hardness was obtained after hardening DB and subsequent heat treatment. The maximum fatigue strength was obtained by hardening multi-pass DB without subsequent heat treatment for the as-received initial state. Smoothing DB and subsequent heat treatment maximised the surface corrosion resistance for the two initial states, whereas a minimum corrosion rate was obtained for the initially heat-treated state. For the as-received state, smoothing DB and subsequent heat treatment simultaneously lead to a high fatigue limit (equal to that obtained by hardening single-pass DB) and a low corrosion rate.  
304 austenitic stainless steel; diamond burnishing; surface integrity; fatigue behaviour; corrosion resistance +
Footnote †: journal: Applied Sciences
## 4 Proposed Approach
### 5G Networks  
The exceptional revolution in wireless technology aims to connect societies and professionals with excessive responsiveness. Fifth generation (5G) networks are expected to achieve a 1000 times upgrade in data rate with almost zero latency as compared to the present generation. The latency of 5G is anticipated to be up to 1 ms, i.e., 20 times improved from 4G [19]. The upcoming standard of 5G will also generate numerous pioneering health services. Consequently, the care of patients will increase in quality, while the medical staff workloads and costs will be reduced.  
5G will boost the prospects for the remote educational courses of doctors and surgeons by implementing visual and haptic communication. This will create an Internet of medical skills, where medical staff will be able to transfer their expertise and share their experience overcoming the obstacle of distance by using robotic and haptic devices. Thus, a main advantage is that amateur surgeons will have the opportunity to attend and experience surgical procedures by experienced doctors, even if they are located in different places. Furthermore, robots will be manipulated by surgeons with the utilization of haptic gloves or devices, which can transfer haptic feedback to the operating doctor despite the distance. This surgical operation of the future will become feasible due to the 1 ms latency that 5G promises to offer.  
An additional innovative application that 5G networks are anticipated to bring to light is bio-connectivity. This technology refers to the on-the-move delivery of medical care, which will offer medical services to patients without being transferred to hospitals. Thus, it will help to the decongestion of the hospitals. Evidently, the benefits of implementing such a technology start from improving performance in hospitals to novel methods of monitoring the patients' health and the offer of individualized medication.  
The development of 5G will engage several technologies, of which some of them are already being used by present generations. In addition, it will take advantage of extremely high-frequency bands in order to deliver faster mobile broadband speed. A crucial issue of 5G technology is to become widely accepted by humanity. The smooth operation of healthcare applications demands a broadly reliable coverage of 5G networks, which is feasible with the placement of numerous antennas in specific spots. Though, some groups of skeptics believe the opinion that electromagnetic waves straightforwardly affect human lives, negatively influencing some parts of the population. Thus, research on this topic that prove the safety of 5G technology is fundamental for facilitating a variety of e-health applications [20].
# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
###### Abstract  
In the field of communication, autoencoder (AE) refers to a system that replaces parts of the traditional transmitter and receiver with artificial neural networks (ANNs). To meet the system performance requirements, it is necessary for the AE to adapt to the changing wireless-channel conditions at runtime. Thus, online fine-tuning in the form of ANN-retraining is of great importance. Many algorithms on the ANN layer are developed to improve the AE's performance at the communication layer. Yet, the link of the system performance and the ANN topology to the hardware layer is not fully explored. In this paper, we analyze the relations between the design layers and present a hardware implementation of an AE-based demapper that enables fine-tuning to adapt to varying channel conditions. As a platform, we selected field-programmable gate arrays (FPGAs) which provide high flexibility and allow to satisfy the low-power and low-latency requirements of embedded communication systems. Furthermore, our cross-layer approach leverages the flexibility of FPGAs to dynamically adapt the degree of parallelism (DOP) to satisfy the system-level requirements and to ensure environmental adaptation. Our solution achieves 2000\(\times\) higher throughput than a high-performance graphics processor unit (GPU), draws 5\(\times\) less power than an embedded central processing unit (CPU) and is 5800\(\times\) more energy efficient compared to an embedded GPU for small batch size. To the best of our knowledge, such a cross-layer design approach combined with FPGA implementation is unprecedented.  
autoencoder; communication systems; artificial neural networks; backpropagation; cross-layer approach; FPGA 202211138. [[https://doi.org/10.3390/electronics11071138](https://doi.org/10.3390/electronics11071138)]([https://doi.org/10.3390/electronics11071138](https://doi.org/10.3390/electronics11071138))
## 5 Discussion
### Method of Corrected Similarity
This threshold serves as a criterion for distinguishing between the dominance of the static moment and the web thickness in the model. For the static moment-dominant structure (\(P_{1}/P_{cr-e}<\) 1.55), Criterion 1 ensures that the dimensionless number \(\Pi_{6}\) (the sectional area) and \(\Pi_{7}\) (the static moment) are preserved, partially maintaining consistent geometric properties with the distorted model. For the web thickness-dominant structure (\(P_{1}/P_{cr-e}>\) 3.21), Criterion 2 was employed, focusing on achieving consistency in the web thickness and sectional area, as per previous studies on web girders. It is important to note that for the intermediate range of \(P_{1}/P_{cr-e}\), specifically 1.55 to 3.21, the proposed criteria exhibit significant deviations when predicting the behaviors of the reference model. The empirical method, denoted as Criterion 3, to obtain a distorted model involves designing a proper flat bar that satisfies the empirical ratio \(P_{1}/P_{cr-e}\) through Equation (28), while maintaining a consistent web thickness, to achieve a similar tendency in buckling behavior (Figure 31 [7]). In the case where the target distorted small-scaled model exhibits distortion solely in the stiffener configuration, the corresponding distorted model I is established with dimensions identical to the reference model, resulting in a series of related scaling factors (\(\lambda_{X}\)) equal to 1, as previously defined (Figure 31 [3]). Subsequently, this distorted model I is subjected to the same correction procedure to calculate the ratio of \(P_{1}/P_{cr-e}\), as defined earlier (Figure 31 [5]). By implementing the same correction procedure stated in Figure 31 [7], distorted model II, featuring flat-bar stiffeners, is designed to meet the proper criteria based on the threshold ratio of \(P_{1}/P_{cr-e}\). Consequently, the dynamic behaviors of distorted model II faithfully replicate those of distorted model I. Subsequently, the resistance responses can be employed to predict those of the prototype using Equation (17), along with reference to Tables 4 and 5 (Figure 31 [8]).
# Short-Range Transportation Using Unmanned Aerial Vehicles (UAVs) during Disasters in Japan
## 4 Discussion
UAVs can provide an optimal solution for critical air transport of medical supplies from points where HEM and DMAT vehicles have reached the vicinity of disaster areas. Thus, UAVs offer one of the best disaster medicine and disaster mitigation and intervention strategies at the present time. After determining the acute phase of a disaster, which is the initial life-saving period, the primary issue becomes finding useful transportation methods to supply food and drugs to secure local healthcare services. For example, medicine transportation to temporary pharmacies in affected areas was disrupted during the Great East Japan Earthquake [24]. UAVs could also provide an important means of transporting drugs in such large earthquakes. In particular, if it is possible to use a mobile pharmacy to perform tests on transfusion and other products, it would be possible to use UAVs for the temporary but stable transport of blood for transfusion. Considering the amount of time that has elapsed since a disaster has occurred and the response from the frontline base involving DMAT, HEM, mobile pharmacy, and other organizations in the disaster area, UAVs could transport emergency supplies for disaster medical care, including so-called DMAT equipment, emergency blood transfusions, and so on. After some time has passed, UAVs could transport food and other items, as well as non-emergency drugs and blood for planned transfusions (Figure 2). We believe that this process could build greater health security within a community. Figure 2 is a conceptual diagram of the movement of goods to a transport base, which can vary in terms of the time taken based on distance from the disaster and whether HEMs, DMATs, mobile pharmacies, or logistics trucks are used. Then, UAVs can be used to circumvent short-distance traffic disruptions. UAVs are dispatched from the frontline base to the disaster area and first transport emergency disaster medical care supplies, so-called DMAT equipment, emergency blood transfusion products, and so on. Next, UAVs transport food, drugs, and other items.
## 2 Acquisition of Tunnel Face Images
### Method of Partitioned Image Acquisition
#### 2.1.2 Principles for Selecting Light Sources  
To improve image quality under low-light conditions, various lighting equipment such as flash, reflector lamps, and mechanical equipment light sources are used. Each has its advantages and limitations.  
Flash is the most direct complementary light source for digital cameras. It produces a strong lighting effect at the moment of exposure. However, in the dusty environment of tunnel engineering, it can easily cause diffuse reflection of dust particles, which affects the imaging quality. Reflector lamps are a type of spotlight with a wide lighting range and a stable light source. They provide better lighting for the tunnel working surface but are less portable and require a power supply that may be inconvenient at the construction site. Mechanical equipment at the tunnel construction site, such as loaders, wet spray trucks, and dump trucks, generally have lighting systems. These light sources have wide coverage and stable illumination, which can provide a good lighting effect for the tunnel working surface. Although they may be interfered with by mechanical shadows, they can be effectively avoided in practice. Therefore, in this study, mechanical equipment shown in Figure 2 is selected for lighting and fill light.
## 2 Methods  
### Sliding-Window Analysis  
For the overall proposed method, SWA is important for real-time estimation. SWA sets a certain sample range sliding-window, and the most recent data are used for computing statistics [16]. When a new sample is measured, the sliding-window slides to the next sample, which includes the newest and discards the oldest sample (Figure 1). As the sample window is maintained at the same size, it has the advantage of constant dynamics estimation for the same parameters. In this study, we set a 350-sample wide sliding window and it covered at least a single-trial [5; 16]. The sliding-window was applied for each measuring time (i.e., excluding only the oldest sample). We also tested different window length simulations to assess the effect of length (ranging from 50 to 2077 samples with 50-samples step).  
Figure 1: Diagram of the sliding-window analysis (SWA). Samples placed in the sliding-window, including the newest sample, are applied signal processing or used for analysis. A sliding-window will move to the next when the time instant \(k\) is increased.