esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Inclusion and Exclusion Criteria  
The inclusion criteria were as follows: (1) KL classification of knee OA grades I-III, diagnosed on radiograph and MRI imaging; (2) pain in the knee joint (VAS \(\geq\) 1); and (3) the patient age between 18 and 80 years. The exclusion criteria were as follows: (1) grade IV degenerative changes as determined by the KL classification, (2) if the grade of arthritis could not be determined from the medical records, (3) inflammatory arthropathies such as rheumatoid arthritis and connective tissue diseases, (4) the presence of another source of significant pain or dysfunction affecting the ability to isolate symptoms of the affected knee when evaluating the effectiveness of the treatment and (5) if the patients had already received a similar type of injection or surgical intervention within 12 months prior to treatment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Age** & \multicolumn{2}{c}{**Sex**} & \multicolumn{2}{c}{**Laterality**} \\ \hline
**Treatment** & \multirow{2}{*}{**Mean \(\pm\) SD**} & **Male** & **Female** & **Left** & **Right** \\
**Group** & & **N (\%)** & **N (\%)** & **N (\%)** & **N (\%)** \\ \hline HA & 51 \(\pm\) 15 & 88 (52) & 83 (48) & 76 (44) & 95 (56) \\ ACS & 51 \(\pm\) 13 & 121 (57) & 101 (43) & 109 (49) & 113 (51) \\ BMAC & 52 \(\pm\) 10 & 66 (59) & 46 (41) & 52 (46) & 60 (54) \\ Total & 52 \(\pm\) 13 & 287 (54) & 247 (46) & 251 (47) & 283 (53) \\ Baseline & & & & & \\ differences & & & & & \\ between & & & & & \\ groups & & & & & \\ \hline \hline \end{tabular} SD = standard deviation; N = number of cases; HA = hyaluronic acid; ACS = autologous conditioned plasma; BMAC = bone marrow aspirate concentrate.  
\end{table}
Table 1: Demographic data of the included cases.  
Figure 1: Flowchart of the study showing the number of eligible patients, number of included patients and number of included knees, reasons for exclusion and follow-up percentage at 3 and 12 months. HA = hyaluronic acid; ACS = autologous conditioned serum; BMAC = bone marrow aspirate concentrate.
# The Conditional Probability of Correlating East Pacific Earthquakes with NOAA Electron Bursts
###### Abstract  
A correlation between low L-shell 30-100 keV electrons precipitating into the atmosphere and M \(\geq\) 6 earthquakes in West Pacific was presented in past works where ionospheric events anticipated earthquakes by 1.5-3.5 h. This was a statistical result obtained from the Medium Energy Protons Electrons Detector on board the NOAA-15 satellite, which was analyzed for 16.5 years. The present analysis, utilizing the same database, translated into adiabatic coordinates during geomagnetic quiet periods, lead to another significant correlation regarding East Pacific strong earthquakes. This new correlation is still observed between high energy precipitating electrons detected by the NOAA-15 0\({}^{\circ}\) telescope and M \(\geq\) 6 events of another very dangerous seismic region of the Pacific ring of fire. The particle precipitation that contributed to this correlation was characterized by electron L-shell, pitch-angle, possible disturbance altitudes, and geographical locations. This correlation occurred circa 57 h prior to the East Pacific earthquakes, according to past single cases of reports. The conditional probability corresponding to the cross-correlation peak of 0.024 per binary events reached a value of 0.011. A probability gain of 2 was calculated for earthquakes after an independent L-shell EBs detection, it is therefore applicable for future earthquake forecasting experiments. Moreover, a time-dependent probability gain approaching the correlation peak was estimated.  
electron bursts; NOAA satellite; East Pacific earthquakes; conditional probability; risk reduction Article  
[PERSON] The Conditional Probability of Correlating East Pacific Earthquakes with NOAA Electron  
Bursts. _Appl. Sci._**2022**, _12_, 10528.  
[[https://doi.org/10.3390/app122010528](https://doi.org/10.3390/app122010528)]([https://doi.org/10.3390/app122010528](https://doi.org/10.3390/app122010528))
# An Obstacle-Tolerant Path Planning Algorithm for Mobile-Anchor-Node-Assisted Localization
###### Abstract  
The location information obtained using a sensor is a critical requirement in wireless sensor networks. Numerous localization schemes have been proposed, among which mobile-anchor-node-assisted localization (MANAL) can reduce costs and overcome environmental constraints. A mobile anchor node (MAN) provides its own location information to assist the localization of sensor nodes. Numerous path planning schemes have been proposed for MANAL, but most scenarios assume the absence of obstacles in the environment. However, in a realistic environment, sensor nodes cannot be located because the obstacles block the path traversed by the MAN, thereby rendering the sensor incapable of receiving sufficient three location information from the MAN. This study proposes the obstacle-tolerant path planning (OTPP) approach to solve the sensor location problem owing to obstacle blockage. OTPP can approximate the optimum beacon point number and path planning, thereby ensuring that all the unknown nodes can receive the three location information from the MAN and reduce the number of MAN broadcast packet times. Experimental results demonstrate that OTPP performs better than Z-curves because it reduces the total number of beacon points utilized and is thus more suitable in an obstacle-present environment. Compared to the Z-curve, OTPP can reduce localization error and improve localization coverage.  
Keywords:localization; mobile anchor node; path planning; wireless sensor networks +
Footnote †: journal: Sensors
## 1 Introduction
In contrast, we proposed an MLP-based feature spectra selection method that addresses these challenges by utilizing the multilayer perceptron's ability to learn intricate interactions, thereby enhancing classification performance and making it suitable for real-time food safety applications. This research aimed to develop an efficient and reliable HSI-based method for detecting aflatoxin B1-contaminated almonds to ensure food safety in the almond industry. Also, various feature selection techniques were investigated for hyperspectral image classification of aflatoxin B1-contaminated almonds for comparison.
# Optical Camera Communication as an Enabling Technology for Microalgae Cultivation +
## 4 Methodology
### Experiments  
Two experiments were conducted at the BEA facilities to evaluate signal reception and deterioration due to the increase in biomass concentration. The first experiment evaluated the container surface's radiance in a controlled indoor environment, whilst in the second experiment, the system's performance in an outdoor scenario was assessed. The shared materials, resources and equipment are summarised in Table 1.  
The following subsections describe each scenario separately to facilitate understanding the motivation, methods and data analysis for each experiment.
# EL-MTSA: Stock Prediction Model Based on Ensemble Learning and Multimodal Time Series Analysis
## 4 Experimental Results and Discussion
### Experimental Results and Analyses  
#### 4.4.1 Reliability Analyses of Sentiment Indicators  
To evaluate the reliability of sentiment indicators, we conducted a comparative study using benchmark models, including the LSTM, GRU, Bi-LSTM, and CNN-LSTM. Table 2 presents the comparative experimental results for sentiment indicators on the CSI 300, SSE 500, and CSI A50 datasets, where \"s\" denotes the inclusion of sentiment data as an additional feature. The visualization of these results is provided in Figure 5.  
Through comparative experiments, it is observed that on the CSI 300, SSE 500, and CSI A50 datasets, the prediction performance of the four models significantly improves after incorporating sentiment features compared to using only the six original features (excluding sentiment data). Specifically, on the CSI 300 dataset, MAE decreases by 8.85%, SMAPE by 10.80%, and RMSE by 10.38%. On the SSE 500 dataset, MAE decreases by 5.75%, SMAPE by 6.80%, and RMSE by 6.57%. On the CSI A50 dataset, MAE decreases by 7.83%, SMAPE by 14.82%, and RMSE by 14.23%. These results demonstrate that integrating sentiment indicators into the model and dataset used in this study effectively enhances the model's predictive performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Range** & **CSI 300** & **SSE 500** & **CSI A50** \\ \hline Batch size & [8; 128] & 32 & 64 & 16 \\ Layers & [2; 5] & 3 & 3 & 2 \\ Hidden units & [16; 128] & 64 & 128 & 16 \\ Epoch & [30; 100] & 55 & 80 & 36 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specific parameter setting.
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## The Proposed Scheme  
To mitigate the flaws and inefficiencies mentioned above, we propose a robust mutual authentication with a key agreement scheme for SIP. Compared with [PERSON] et al.'s scheme, the proposed scheme has the following advantages:  
1. If a user desires to update his password, he does not need help from the server.
2. We add a verification mechanism for a wrong password. This can reduce needless communication and computation cost.
3. To prevent an offline password guessing attack or a stolen memory device attack, we redesign the equations used in the login and authentication phase.  
Our proposed scheme contains four phases: Initialization phase, registration phase, login and authentication phase, and password change phase.
## 3 Case Study  
### Comparison of Calculation and Experiment  
The vertical acceleration of the diesel engine feet obtained by calculation is shown in Figure 6, wherein the x-axis is time, the unit is seconds, the y-axis is acceleration, and the unit is gravitational acceleration g. Figure 6a is the acceleration result of diesel engine feet in 0-0.9 s, and the acceleration amplitude decays with time due to damping. The EXCITE algorithm is an iterative calculation affected by the initial value and has a gradual convergence process. The results of very high acceleration at 0 s in Figure 6a is the numerical abnormalities caused by the initial value. Figure 6b is the acceleration result of the diesel engine feet in 0.5-0.9 s; it can be seen from Figure 6b that within 0.5 to 0.9 s, the acceleration amplitude is mainly distributed between \(-8\) g and 8 g and the acceleration amplitude decays with time due to damping. The acceleration signal is composed of low-frequency vibration components and high-frequency vibration components. The low-frequency vibration components mainly refer to the large fluctuation period in the acceleration curve, which is mainly caused by the external shock applied to the diesel engine body through the shock absorber. The high-frequency vibration component mainly refers to the dense vibration signal parts on the acceleration curve, which is caused by the high-frequency periodic excitation, such as gas explosion pressure, the exciting force of the valve system, and the inertial force of the crankshaft, connecting rod, and piston.  
Figure 7 shows the comparison between the calculation results and the experiment results of the acceleration at the diesel engine feet. The red curve in Figure 7 is the  
Figure 6: The calculation results on vertical acceleration of the diesel engine feet. (**a**) The calculation results on vertical acceleration of the diesel engine feet in 0–0.9 s. (**b**) The calculation results on vertical acceleration of the diesel engine feet in 0.5 to 0.9 s.  
calculation result and the dark blue curve is the experiment result. It can be seen from Figure 7 that the experiment acceleration amplitude is mainly distributed between \(-5\) g and \(5\) g and decays with time due to damping. In 0.5-0.9 s, the experiment curve lasted for about seven cycles. The calculated acceleration amplitude is mainly distributed between \(-8\) g and \(8\) g and decays with time due to damping. In 0.5-0.9 s, the calculation curve lasted for about eight cycles. The calculated result has a similar trend with the experimental result and has a difference in acceleration amplitude and vibration frequency with the experimental result. The reason for the difference, on the one hand, may be that the calculation modeling is not accurate enough; on the other hand, the calculation parameters may be incorrectly selected, resulting in large errors in the results. The calculation model can be used to predict the diesel engine response trend in the external impact condition and can be used as an alternative to the impact test.
## 4 Problem Formulation
The Optimization GoalThe optimization goal for the proposed MADDQN is to reduce the latency and improve throughput for the whole link; hence, the proposed optimization goal is defined as  
\[\max\omega_{1}\frac{1}{\tau_{T_{n},T^{\prime}n}}+\omega_{2}C_{T_{n},T^{\prime}n} \tag{27}\]  
\[s.t:\ C1:\ \gamma_{i,j}^{k}>\gamma_{th}\,\]  
\[C2:\ D_{i,j}^{k}>D_{th}\,\]  
\[C3:\ d_{T_{n},i}<R_{\max}\.\]  
Here, \(\tau_{T_{n},T^{\prime}n}\) and \(C_{T_{n},T^{\prime}n}\) are the whole-link latency and throughput between train \(T_{n}\) and train \(T^{\prime}n\), respectively. \(\omega_{1}\) and \(\omega_{2}\) are the weight factors of the delay and throughput (\(\omega_{1}+\omega_{2}=1\)). \(C1\) is to ensure that the SNR of the channel is greater than threshold value. \(C2\) indicates that each hop delay needs to be less than the target transmission time. If the one-hop delay takes too much time, the transmission is considered to fail. \(C3\) indicates that, when the train is transmitted with the wayside node \(i\), the distance should be less than the maximum transmission distance. The defined reward function comprehensively considers the throughput and delay of each hop, as well as adds the indicator \(k_{i,j}\) in Section 3.2.3. Therefore, the reward function is defined as  
\[F_{t}= \begin{cases}\omega_{1}\frac{1}{k_{i,j}D_{i,j}^{k}}+\omega_{2}C_{i,j}^{k }+r_{s}+r_{c},&\text{ \ \ if $C1$-$C3 are satisfied}\\ \omega_{1}\frac{1}{k_{i,j}D_{i,j}^{k}}+\omega_{2}C_{i,j}^{k}+r_{s},&\text{ \ \ otherwise}\end{cases} \, \tag{28}\]  
where \(r_{s}\) is an additional reward for the final hop directly to the train. This reward is established to prevent other wayside nodes close to the train from being selected, which may lead to an increase in hops and delay. \(r_{c}\) is the outage penalty caused by the next-hop node out of the communication range and a long single-hop delay under \(C1\)-\(C3\).
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 2 Principles and Classification of Reflector Antennas
### Types and Characteristics of Reflector Antennas
#### 2.2.4 Dual-Reflector Antennas  
A dual-reflector antennas is an extended configuration of a parabolic reflector antenna system that incorporates a secondary reflector. The most common forms include the Cassegrain antenna and the Gregorian antenna. Originating from optical telescope designs, the Cassegrain antenna is the most prevalent dual-reflector configuration, as illustrated in Figure 5a. Its standard structure consists of a primary parabolic reflector and a secondary hyperbolic reflector, both formed by rotating a parabola and a hyperbola about their respective axes.  
In operation, electromagnetic waves from the feed reflect off the secondary reflector, then the primary reflector, and finally propagate outward. Similar to a conventional parabolic reflector antenna, the rays originating from the feed remain parallel to the optical axis after two reflections.  
Figure 4: (**a**) Schematic diagram of elliptical reflector antenna; (**b**) position of phased array feed.  
To better understand the operation of a Cassegrain antenna, the equivalent feed method (also known as the virtual feed method) can be employed. As shown in Figure 5b, using a geometric optics approximation, rays emitted from the real feed \(F_{2}\) are reflected by the secondary reflector in such a way that they appear to originate from a virtual focal point \(F_{1}\). This implies that the combination of the real feed and the secondary reflector can be equivalently represented as a single virtual feed located at the virtual focal point. Consequently, a Cassegrain antenna with a reduced longitudinal dimension can achieve the same performance as a long-focal-length parabolic reflector antenna, thereby reducing the overall length while maintaining an effective focal distance.  
Dual-reflector designs reduce the distance between the feed and transceiver, minimizing noise. The dual reflections also enable the use of symmetric feed patterns, improving aperture field distribution and overall efficiency. Positioning the feed behind the primary reflector further reduces feed blockage. Despite added complexity, the dual-reflector antenna offers key advantages: reduced blockage, improved aperture efficiency, aberration correction, better beam shaping, and lower sidelobe and cross-polarization levels [25, 26].  
Dual-reflector designs such as Cassegrain and Gregorian antennas are valuable when compact feed placement and high aperture efficiency are required. They are especially suitable for missions requiring symmetric beam shaping and low cross-polarization. However, their structural complexity increases deployment risk and cost in spaceborne SAR applications.
# A Novel Longitudinal Speed Estimator for Four-Wheel Slip in Snowy Conditions
## 2 Materials and Methods
### Longitudinal Speed Estimator of Four-Wheel Drive Slipping Vehicle on Split Roads
#### 2.1.2 Vehicle Speed Estimator Based on Wheel Speed
Similarly,  
Figure 3: Fuzzy logic vehicle speed estimator output variables based on wheel speed. Figure 2: Fuzzy logic vehicle speed estimator input variables based on wheel speed. \(j=1,2\) indicate the vehicle's left and right sides. To use slip level to account for each input situation, the logic rules are shown in the following Table 1.  
To take the number and slip level of wheels into account based on input variables, fuzzy control logic is used to calculate the confidence level of the vehicle longitudinal speed estimated from the minimum wheel speed. The confidence level is converted to a weighting coefficient to modify the estimated value of the minimum speed, and in the case of large slip, a more accurate speed estimate is obtained. \[\mathrm{V}_{E,w}=\min\left(V_{i,j}\right)/\sigma\]  
in which, \(\sigma\) is the fuzzy logic weight.
## 3 Results and Discussion  
### Heavy Metals Content in Raspberry Fruit  
The content of heavy metals (As, Cd, Pb, Al, Cr, Zn, Co, Mo, Cu, Mn, V, B, Sr, Sb, Ti, Tl) in raspberry fruits from conventional, organic and wild crop and two years of cultivation are presented in Table 1.  
The result of the study showed that the concentrations of all heavy metals were among the analysed fruit samples without aluminium and thallium. The heavy metals content in the analysed samples was varied. In the second year, the levels of arsenic (As), chromium (Cr), cobalt (Co), molybdenum (Mo) and antimony (Sb) were significantly lower in all examined cultures compared to 2020. In addition, there were considerable and statistically significant differences in the content of lead (Pb) and titanium (Ti) between the harvest years of conventionally grown raspberries. Furthermore, only the level of lead was higher in 2021. In organic raspberries, considerable and statistically significant differences were found between the contents of cadmium (Cd), manganese (Mn) and vanadium (V) depending on the harvest year. Most of the elements determined in wild-growing raspberry showed statistical significance compared to two years of cultivation, except for copper (Cu) and boron (B). This means that the compounds accumulation in the fruit did not vary significantly in the successive cultivation year. The wild-growing raspberry fruits were characterised by significant contents of cadmium, aluminium and manganese. More importantly, this occurred in both harvest years and was statistically significant compared to the remaining crops. In the second year of the study, less rainfall was observed, and consequently, there was less sorption of mineral nutrients by the plants. The presence of heavy metals in a plant depends on many factors, including the total and accessible content of these minerals in the soil, soil pH and organic matter content. In addition, the type and species of plant also have an influence on the uptake of heavy metals [26; 27]. The arsenic content varied significantly between the examined years, the results being similar and independent of the cultivation type. In the available literature there are no studies on the As level in raspberries. [PERSON] et al. [28] investigated its quantities in raspberries growing on arsenic-contaminated land. The As content in _Rubus ideas_ raspberry was 1.3 mg kg\({}^{-1}\) DW (dry weight). However, our results were much lower.
# Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning
## 4 Results and Discussions  
### Trained Models  
As highlighted in Section 3, each model was run using the TensorFlow environment and during the training, the evaluations were observed and followed until the model achieved an acceptable loss level below 1. This, however, does not represent an accuracy of the model and evaluation needed to be carried out on the model regardless of the loss value. This was observed through the TensorBoard environment on as shown in Figure 11.  
By utilizing the TensorBoard system, it allowed the user to monitor the progress of the model throughout its training. Of particular importance to monitor in this case was the loss which is shown in Figure 12. The graphs in Figure 12 display the loss over the training iterations time with respect to different characteristics of the model i.e., object detection, localization, and classification. Within the figure, it can be observed that this loss reduces over time and it is important for the model to have a sufficiently low value for the total loss. The platform also allowed the user to monitor the progress of the model on test data during the training which is also helpful as the training could be stopped if it was seen that the model was not producing appropriate results.  
Figure 11: TensorBoard environment utilized during the training of the network.  
Each model was run on the same dataset. After training, the models produced were tested on prediction images run from the test data set and examples of these can be seen in Figures 13-15. The model produces a bounding box on the image of the distress type and severity along with a percentage assumption of how accurate this bounding box is based on the calibrations of the model. This value would provide users with an overall assessment of how good the hotspot analysis is based on the sum of the total possible errors on a survey over a road network.  
Figure 12: The use of TensorBoard to monitor the loss and check on accuracies within the models during evaluation.  
Figure 13: Faster R-CNN with inceptionv2 model predictions on test data.