esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Variation of Doppler Signal
#### 3.6.3 The Doppler Frequency Shift Measurement Data  
The DFS measurements on the inclined radio pass \"Dushanbe-orzu--Institute of Ionosphere (Almaty)\" at the frequency \(f=7245\) kHz were made during the period from 1 February up to 15 March 2024. The data obtained every day in the daytime between 5:00 and 17:00 UT were used for the calculation of the mean DFS value for the corresponding date. These data are presented in Figure 10, after additional smoothing of the distribution of DFS daily means with a four-point running average filter.  
As a result, a cove-like decrease in DFS variation is seen in the plot of Figure 10, which starts on 25 February, 8 days before the earthquake, and reaches maximum on 2-3 March, just on the eve of it.  
To evaluate the statistical significance of the observed anomaly, the time series of DFS data gained since 25 February and up to 4 March, i.e., during the appearance of the anomalous effect before the earthquake, was compared against the background record made in the foregoing period, 1-25 January 2024. For comparison, the mean values and standard deviations were defined for both datasets. Then, based on the calculated Student criterion \(t=20.04\), a high level of statistical confidence \(p<0.001\) was estimated for  
Figure 9: An example of the profile of electron concentration \(Ne\) in the middle point of the radio path “Dushanbe-orzu—Institute of Ionosphere (Almaty)” (**left**) and corresponding trajectory of radio wave propagation at various daytime moments (**right**).  
the difference between the background time series and the effect observed in DFS before the earthquake.  
The appearance moment of the anomaly in DFS, 8 days before the earthquake on 4 March 2024, agrees well with the times when ionospheric disturbances preceding major earthquakes were reported by other ground-based and satellite observations [21; 23; 45; 46].
# An Action Classification Method for Forklift Monitoring in Industry 4.0 Scenarios
## 2 RFID Gates
It is noteworthy that phase-based techniques can also allow to discriminate tags carried out by a forklift [27] or moving along a conveyor belt [28] with respect to static tags in the warehouse/plant scenario. The concept of phase measurements applied to conveyor belts was also explored in [29], where a two-antenna architecture was proposed for measuring the Direction of Arrival (DoA) of moving RFID tags for localization purposes. The Doppler Effect can be indeed profitably exploited for the tag localization on conveyor belts, as demonstrated by [30]. More recently, machine learning techniques were investigated in RFID systems both for localization purposes [31; 32] and RFID Smart Gate implementation [33; 34; 35]. In [33], a single antenna architecture was proposed to determine the direction of people crossing an indoor RFID gate based on an Artificial Neural Network (ANN). Consecutive RSSI data are aggregated within frames, and the mean RSSI for each time frame is fed as an input feature for the neural network. The obtained accuracy is higher than 99%. Machine Learning solutions were also employed to solve the issue of stray reads [34], where a 97.5% classification accuracy among actual RFID tags crossing the gate and static or other tags moving close to the gate without crossing it was achieved with a single antenna architecture. However, such a system does not allow the crossing direction estimation. In [35], both the RSSI and the phase are processed through different machine learning techniques to discriminate among moving and static RFID tags. In fact, when the relative distance between the reader antenna and the tag changes, both the received power and phase change significantly. A concept for asset tracking was proposed in the patent [36] as a device-free user localization scheme. Basically, a set of antennas is attached at the ceiling facing the floor, whereas a set of tags is placed on the floor. A moving object can shadow the tags and create a signature of the motion of the object itself. The same scheme is applicable for RFID gates, as proposed in [37; 38] to solve the problem of pallet trucks crossing a key point (e.g., to monitor the charging of goods on a truck). In both solutions, an antenna is placed at the ceiling facing downwards, and a regular grid of 24 tags is placed on the floor. When a metallic cart crosses the target area, the tags are shadowed. Such information is given in input to a Long-Short Term Memory (LSTM) [39] Recurrent Neural Network (RNN) [37] or a convolutional neural network [38]. In both cases, a classification accuracy of 100% is obtained. Despite the robustness of these solutions, the deployment of the tags on the floor is unfortunately not always possible in warehouse scenarios, as the tags cannot stand high pressures caused by the weight and encumbrance of industrial vehicles such as forklifts.
# Simulation and Design of Three 5G Antennas
## 5 Design of an Eight-Element MIMO Array Antenna for Mobile Terminals with a Metal Frame  
Fifth-generation mobile terminal antennas are designed to meet specific performance requirements while considering channel distribution and the impact of the human body on antenna performance [28]. To meet the demands of future cellular communication systems, there is a need to continually increase the channel capacity of smartphones. The design trend is to integrate six or eight antenna elements to form MIMO array antennas. However, as smartphones become thinner and lighter, physical space in real-world scenarios is increasingly limited, leading to reduced space for antenna utilization. This compression in design dimensions results in significant coupling between MIMO components. Therefore, the following exploration will focus on how to enhance the isolation between MIMO array antennas.
## 3 Simulation Results
### Pulse Train Current Excitation  
The second example of signal \(i(t)\) to consider is a periodic train of rectangular pulses followed with period \(T=0.1\) us, with exponentially smoothed leading and trailing edges. The current \(i(t)\) can be defined as:  
\[i(t)=\sum_{n=-\infty}^{+\infty}i_{1}(t-nT)[\text{A}], \tag{60}\]  
Figure 6: Average power distribution for the case of finite sum of harmonics current.  
Figure 7: The spectrum of \(p(t)\) for the case of finite sum of harmonics current.  
Figure 11 shows the diagram of CSCD \(S_{\rm nl}(\alpha_{f})\) found in accordance with (21). It has more components than the diagram in Figure 5b because of the richer spectral composition of the rectangular pulse train current (60).  
Figure 12 shows the distribution of the real and imaginary parts of average power. The weights of the delta function of the real part are maximum at frequencies \(\pm\)10 MHz, which match the circuit resonant of the fundamental frequencies of the signal under investigation.  
Figure 13 shows the spectrum of instantaneous power \(p(t)\). The delta functions forming the spectrum of \(p(t)\) are at the same frequencies as the delta functions forming the spectrum of instantaneous current, \(i(t)\), and voltage, \(v(t)\), signals.  
Figure 11: Diagram of CSCD for the case of pulse train current.  
Figure 12: Average power frequency distribution for the case of pulse train current.  
The following quantitative power characteristics were calculated: the average power (33) \(P_{avg}=199.4\)\(\upmu\)W, apparent power (39) \(P_{app}=264.7\)\(\upmu\)VA, power factor (43) \(PF=0.75\).
## 4 Probabilistic Analyses
### Sensitivity Analyses
#### 4.6.1 Cross-Sectional Level  
Sensitivity analysis at the cross-section level is performed based on the FORM probabilistic method for reliability analysis. The Limit State Equation (LSE) is defined as an equilibrium equation for the cross-section simultaneously exposed to axial force and bending moment. Equilibrium of inner forces (Figure 7) is achieved when both the sums of bending moments and axial forces are equal to zero.  
The axial force applied to the column is equal to the sum of forces in the concrete and steel reinforcement:  
\[N=F_{c}+F_{s1}-F_{s2} \tag{22}\]  
and the LSE for cross-sectional analysis is then equal to the sum of moments:  
\[\sum M=F_{c}\cdot k_{c}+F_{s1}\cdot k_{s1}+F_{s2}\cdot k_{s2}-N\cdot k_{N}=0 \tag{23}\]  
where:  
\[k_{c}=x\cdot k_{a};k_{a} \tag{24}\]  
\[k_{s1}=x-d_{1} \tag{25}\]  
\[k_{s2}=h-x-d_{2} \tag{26}\]  
\[k_{N}=e_{1}+e_{2}+x-h/2 \tag{27}\]  
The probabilistic analysis was conducted for multiple steps where the normal force was increased incrementally in each step in order to compute the sensitivity factor of each random variable, as detailed in Table 3, and their variations with different load sizes. The sensitivity factor values \(\alpha_{i}\) are used to provide the relative importance of each individual random variable. By definition, the sum of squares of sensitivity factors for each random variable is equal to 1:  
\[\sum_{i=1}^{n}(\alpha_{i})^{2}=1 \tag{28}\]  
Figure 7: Column cross-section with strains, stresses and inner force diagrams.  
Figure 8a shows the sensitivity analyses and sensitivity factors, calculated via the First Order Reliability Method (FORM), versus \(N/Nmax\) with \(Nmax\) = 335.5 kN while Figure 8b shows the maximum and minimum strains in the column longitudinal axis up to the maximum bearing capacity of \(N\) = 335.5 kN for the column made of C45/55. It can be seen from the graphs that at the beginning of the loading procedure, the concrete modulus of elasticity X\({}_{1}\)(\(E_{cl}\)) has a major impact on results but as the force increases, its impact diminishes as the concrete is entering a non-linear state of behavior. Due to small uncertainties in the statistical parameters of areas of reinforcement, their impact on the results can be neglected. The axis distance of reinforcement in compression \(X_{7}\)(\(d_{1}\)) has a major impact at the beginning of the loading, but reduces as the force increases, while the impact of axis distance of reinforcement in tension X\({}_{8}\)(\(d_{2}\)) increases with the force and deformation of the concrete in tension. Its impact reaches its peak values as the concrete deformation in tension reaches its maximum, just before the cracking of the concrete. The impact of the concrete compressive strength \(X_{11}\)(\(f_{c}\)) increases along with the loading and reaches its peak value at the point when the concrete deformation in compression and tension are equal (absolute values). After the peak (when the force is around 280 kN), its impact decreases as the tension area of the concrete increases.
# Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment
## 5 K-Means Clustering Results and Discussion
### Finding the Ideal Number of Clusters
#### 5.4.3 Third Method: Clusters with at Least 50% Attack Data as Attack Clusters  
From the previous set of experiments, specifically Table 7, since the best results were obtained with a seed of 200 for clusters with at least 50% attack data, the seed of 200 was used for the following set of experiments. And, looking back at Table 3 and Figure 2, the best results were obtained using cluster sizes around 300, hence cluster sizes between 100 and 400 were tested for this experimentation.  
Analysis of the Third Method:  
* Impact of Clustering on Performance Metrics
* Similar to the 25% labeling method (the Second Method), increasing clusters led to steady improvements in accuracy, precision and recall.
* At 325 clusters, precision and recall reached 98.77% and 98.74% respectively, with no significant improvements beyond this point, confirming 325 as the best balance between performance and efficiency.
* Optimal Number of Clusters
* At 400 clusters, no further improvements were observed, leading to the conclusion that 325 clusters are the most effective choice.  
* A seed of 200 with 325 clusters will produce the best set of results for clusters with at least 50% attack data. These best results are bolded in Table 10.
* The 25% and 50% labeling methods produced almost identical results, demonstrating consistent detection accuracy at higher cluster counts for clusters with greater than 25% attack data.
* In terms of computational time, as in the First and Second Methods, although the lower number of clusters (250 clusters or less) had lower computational times and the higher number of clusters (above 250 clusters) had higher computational times, there was no definite pattern.
# Aiding ICD-10 Encoding of Clinical Health Records Using Improved Text Cosine Similarity and PLM-ICD
## 4 Methodology
### Palm-Icd  
PLM-ICD [12] is a deep-learning based model for automatic encoding of clinical texts that contain biomedical terms into ICD-10 codes.  
General PLMs are pretrained on large texts that may or may not contain biomedical text. PLM-ICD uses BioBERT [7], PubMedBERT [9], and RoBERTA-PM [10] databases. This model uses these domain-specific PLMs and fine-tunes them on the automatic ICD coding.  
PLM-ICD also tackles the long input text problem, proposing segment pooling by splitting the whole document into segments shorter than the maximum length, and encodes them into segment representations. These segments are inputted into PLMs separately to compute hidden representations, and after concatenated to obtain the hidden representations of all tokens.  
This model also tries to solve the problem of large datasets with the label-aware attention mechanism proposed by [28] to capture the important text fragments relevant to certain labels.  
The PLM-ICD code and documentation are available at [[https://github.com/MiuLab/PLM-ICD/tree/master](https://github.com/MiuLab/PLM-ICD/tree/master)]([https://github.com/MiuLab/PLM-ICD/tree/master](https://github.com/MiuLab/PLM-ICD/tree/master)) (accessed on 3 December 2023), only for the MIMIC datasets previous to MIMIC-IV. In order to train to this specific dataset, adaptations were performed. Preprocessing of the MIMIC-IV dataset was performed following the setting from [22], available at the link [[https://github.com/jamesmullenbach/caml-mimic](https://github.com/jamesmullenbach/caml-mimic)]([https://github.com/jamesmullenbach/caml-mimic](https://github.com/jamesmullenbach/caml-mimic)) (accessed on  
Figure 1: Flowchart of the algorithm developed, showing the bucket category concept of ICS.  
3 December 2023). The PLM used was the BioLM RoBERTa-base-PM-M3-Voc-distill-align-hf, available at [[https://github.com/facebookresearch/bio-lm](https://github.com/facebookresearch/bio-lm)]([https://github.com/facebookresearch/bio-lm](https://github.com/facebookresearch/bio-lm)) (accessed on 3 December 2023).
## 6 Conclusions  
In this paper, we proposed a novel fused clustering algorithm that combines polarization and spectral information for the optical detection and identification of space debris targets. Our algorithm achieved a high classification accuracy of 96.92% for six types of samples and outperformed classical methods in an actual scene. The combination of HCA and FCM algorithms provided a stable and robust initial state for the FCM algorithm, thereby improving the effectiveness, adaptability, accuracy, and robustness of clustering for multidimensional space debris datasets. The proposed clustering method demonstrates superior performance in distinguishing spatial target materials with different spectral polarization properties, fully utilizing color discrimination ability, and providing a favorable output format for human vision. Our research provides a better theoretical basis and practical results for space debris detection and identification. In the future, the proposed method has the potential to be further improved with more advanced algorithms as better instruments become available and larger datasets of space objects become available.  
Conceptualization, F.G. and J.Z.; methodology, F.G.; software, F.G.; validation, F.G., H.L. and J.D.; formal analysis, F.G.; investigation, F.G.; resources, F.G.; data curation, F.G.; writing--original draft preparation, F.G.; writing--review and editing, L.H., H.J. and X.H.; visualization, F.G.; supervision, F.G.; project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 61890961, the National Natural Science Foundation of China, grant number 62127813 and the National Natural Science Foundation of China, grant number 62001382.  
Not applicable.  
All data are contained within the article.  
The author thanks the China Xi'an Satellite Control Center State Key Laboratory of Astronautic Dynamics for the space debris experiment sample materials.  
The authors declare no conflict of interest.
###### Abstract  
Dermatology and cosmology currently prioritize healthy, youthful-looking skin. As a result, research is being conducted worldwide to uncover natural substances and carriers that allow for controlled release, which could aid in the battle against a variety of skin illnesses and slow the aging process. This study examined the biological and physicochemical features of novel hydrogels containing cannabidiol (CBD) and \(\alpha\)-terpineol (TER). The hydrogels were obtained from \(\epsilon\)-caprolactone (CL) and poly(ethylene glycol) (PEG) copolymers, diethyhene glycol (DEG), poly(tetrahydrofuran) (PTHF), 1,6-diisocyanatohexane (HDI), and chitosan (CHT) components, whereas the biodegradable oligomers were synthesized using the enzyme ring-opening polymerization (e-ROP) method. The in vitro release rate of the active compounds from the hydrogels was characterized by mainly first-order kinetics, without a \"burst release\". The antimicrobial, anti-inflammatory, cytotoxic, antioxidant, and anti-aging qualities of the designed drug delivery systems (DDSs) were evaluated. The findings indicate that the hydrogel carriers that were developed have the ability to scavenge free radicals and impact the activity of antioxidant enzymes while avoiding any negative effects on keratinocytes and fibroblasts. Furthermore, they have anti-inflammatory qualities by impeding protein denaturation as well as the activity of proteinase and lipoxygenase. Additionally, their ability to reduce the multiplication of pathogenic bacteria and inhibit the activity of collagenase and elastase has been demonstrated. Thus, the developed hydrogel carriers may be effective systems for the controlled delivery of CBD, which may become a valuable tool for cosmetologists and dermatologists.  
transdermal active substance delivery systems; hydrogels; biodegradable polymers; cannabinoid; \(\alpha\)-terpineol; antioxidants; metalloproteinase inhibitors; antimicrobial activity; cytotoxicity; skin cells +
Footnote †: journal: Molecular Sciences
## 2 Materials and Methods
### Mathematics Foundations
#### 2.1.1 Random Forest  
The mathematical foundations of Random Forest [16] are as follows:  
_Ensemble learning:_ to boost predictive accuracy, ensemble learning merges the results from various models, with random forest specifically using a collection of decision trees.  
_Individual tree prediction:_ let \(T1(x)\), \(T2(x)\), \(\ldots\), \(Tn(x)\) be the predictions of \(n\) individual decision trees for an input \(x\).  
_Aggregation_ (_classification_): for classification, A majority vote is commonly used to arrive at the final prediction.  
\[PredictionRF(x)=argmax\;\Sigma i=1n\;I(T\!i(x)=c)\]  
where:  
_PredictionRF_(_x_) is the final random forest prediction for input \(x\).  
\(c\) represents the class labels.  
_I_(_Ti_(_x_) = _c_) is an indicator function that equals 1 if tree _Ti_ predicts class \(c\) and 0 otherwise.  
_argmax_ determines the class label that received the most votes.  
_Aggregation_ (_regression_): in regression, the final prediction is usually calculated as the mean of the predictions from each tree.  
\[PredictionRF(x)=(1/n)\;\Sigma i=1n\;T\!i(x)\]  
where:  
_PredictionRF_(_x_) is the final random forest prediction for input \(x\).  
Bagging (bootstrap aggregating): to ensure diverse training, bagging uses sampling with replacement to create unique datasets for each tree.  
Bootstrap sample: Given a training dataset \(D\) of size \(N\), a bootstrap sample \(D^{\prime}\) of size \(N\) is created by randomly sampling \(N\) data points from \(D\) with replacement. As a result, specific data points can appear repeatedly in a training set, while others are excluded.  
Tree training: dach tree \(Ti\) is trained on its bootstrap sample \(D^{\prime}i\).  
Effect: bagging's strength lies in reducing variance; training trees on different bootstrap samples mitigates the impact of individual, highly sensitive data points, smoothing out their influence.  
Feature randomness (random subspace): to enhance tree diversity, feature randomness restricts each split to a random subset of features.  
Feature subset: To determine the best split at each node, XGBoost selects a random subset of \(f\) features (where \(f<F\)), rather than considering all \(F\) features. A common practice is to set\(f=\sqrt{F}\).  
Split selection: the splitting criterion is calculated solely based on the \(f\) randomly chosen features.  
Effect: Feature randomness minimizes correlation among trees; strong predictor features would dominate splits without it, resulting in similar trees. Trees are forced to diverge by restricting feature selection, further decreasing variance.
## 4 Proposed DanRETwin Solution
### Technical Approach
#### 4.2.3. AUSTRET Retro-Commissioning Service  
Regarding retro-commissioning, there was a recent initiative to establish a systematic and comprehensive approach for existing buildings through the development of an innovative tool called 'AUSTRET'. This tool, previously developed by the authors [43], focuses on automated step response testing for building energy systems and automation and control systems. It assesses how specific systems respond to operational modifications and verifies their ability to achieve the desired outcomes. The tool offers the required input for configuring parameters, selecting building systems, and generating output results for each test conducted. The framework's objective is to control the ventilation, heating, and cooling of rooms, as well as the heating and cooling of water modules within a building. Figure 7 illustrates a screenshot of a heating radiator test conducted with AUSTRET. As shown in the figure, this specific test targets the room space heating system's response to the change in the heating temperature setpoint, which is changed from 17 \({}^{\circ}\)C to 21 \({}^{\circ}\)C. The test success criteria rely on the evaluated parameter, in this case the room temperature, to attain a stable occurrence with the set success bandwidth, as shown in the dotted line in the figure. The tool offers the required input for configuring parameters, selecting building systems, and generating output results for each test conducted. The framework's objective is to control the ventilation, heating, and cooling of rooms, as well as the heating and cooling of water modules within a building. In the proposed digital twin solution, certain features and services within AUSTRET will be utilized as part of the retro-commissioning framework. This will ensure that the building management system operates and integrates correctly after the retrofitting process and that energy supply systems function effectively.