esa-sceva/satcom-chunk-collection huggingface dataset:
# A 3-D Metal-Printed Dual-Polarized Ridged Waveguide Slot Array Antenna for X-Band Applications
## 3 Antenna Design and Analysis Procedure
### Compact Dual-Polarized Ridged Waveguide Array
It can be clearly seen that, comparing RWGs and conventional rectangular waveguides in the first mode, the dispersion characteristics are almost the same but are more extended in the second mode, while the cross-section occupies only half. Thus, both RWGs and conventional rectangular waveguides achieve available bandwidth that is suitable for the design in the X-band.
## 2 Literature Review
### Summary of Existing SER Models  
#### 2.1.1 Traditional Machine Learning Approaches  
Traditional machine learning techniques such as the Support Vector Machine (SVM) and Hidden Markov Models (HMM) have been foundational in SER development. SVMs classify emotions based on features extracted from speech signals, typically including handcrafted features such as prosodic, spectral, and voice quality measures. Research has demonstrated the use of SVMs for emotion classification on the IEMOCAP dataset, achieving robust results by optimizing decision boundaries [8]. SVMs have been utilized in multi-task learning approaches with deep belief networks (DBNs), achieving a weighted accuracy (WA) of 60.6% and an unweighted accuracy (UA) of 62.2% [9]. Other studies have reported a WA of 65.13% with a combination of ConvLSTM-RNN and SVM on the IEMOCAP dataset [10]. Additional methodologies presented a UA of 60.74% using multiclass SVM classifiers with MFCC and MFCC-SDC features [11].  
HMMs have also been widely used in SER. HMMs are probabilistic models that capture the temporal dynamics of emotions in speech. Detailed overviews of HMMs' strengths and limitations for speech recognition have shown that GMM-HMM, SGMM-HMM, and DNN-HMM configurations can achieve significant accuracy in SER tasks, with UAS ranging from 58.02% to 62.23% [12]. Further studies have explored various feature extraction techniques with HMMs, reporting a UA of 59.46% for four emotion classes [13]. These findings are also detailed in Table 1, highlighting the performance of traditional SER approaches.
## 4 Discussion
### Applications of the Research  
* Accurate muscle tone assessment is crucial for making the most effective intervention decisions.
* In a clinical setting, assessing and understanding muscle tone enables physiotherapists to better analyse and establish appropriate interventions for patients with central nervous system disorders.
* Reaching a consensus on the conceptualization, interpretation, and measurement of results is essential to minimize subjective variations during clinical examinations. This contributes to effective communication among professionals and enhances decision-making for interventions.
* Testing procedures based on neurophysiological muscle tone characteristics are needed in order to improve understanding and clear conceptual definitions, which could provide necessary insights for a better approach in practical contact with patients for physiotherapy evaluations and consequent interventions.
# Query Recommendation Using Hybrid Query Relevance
## 3 Our Approach
### Query Relevance Based on URL Information  
Query information is an important factor for understanding query intention. The clicked URLs can also help us better understand query intention. The more the same URLs queries are clicked, the more relevance the queries gets. A higher ranking means that the URL is more important. We count the number of query clicks for each URL and get the average ranking, defined as a tuple, \([Q,URL,C(Q,URL),Ave_{ranking}]\). \(Q\) is a set of queries: \(Q=\{q_{1},q_{2},\ldots,q_{t}\}\). Where \(t\) is the number of queries. \(URL\) is a set of URLs: \(URL=\{u_{1},u_{2},\ldots,u_{h}\}\). Where \(h\) is the number of URL. \(Ave_{ranking}\) is the average ranking of each URL. For query \(q_{i}\) in a set of \(Q\), we can see the structure of query, clicked URLs and ranking in Figure 3.  
Figure 2: The process of calculating query semantics.  
From Figure 3, we can see that query \(q_{i}\) clicks different URLs, and each URL has a different ranking. Therefore, we calculate the average rankings of each URL by Equation (5).  
\[Ave_{ranking}(u_{i})=\frac{\sum_{i=1}^{|N_{i}|}R_{u_{i}}}{|N_{i}|} \tag{5}\]  
where \(Ave_{ranking}(u_{i})\) denotes the average ranking of \(URL_{i}\). \(|u_{i}|\) denotes the total number of rankings in \(URL_{i}\). \(R_{u_{i}}\) denotes one of the rankings when query \(q_{i}\) clicks the \(URL_{i}\). \(|N_{i}|\) denotes the number of ranking in \(|u_{i}|\).  
We combine the number of clicks and the ranking. However, in a query log, the higher ranking means that the URL is in front of the web page and the value of ranking is small. The strength between the query and URL can be calculated as follows:  
\[\begin{split} Str(q,u_{i})&=\frac{C(q,u_{i})}{\sum _{i=1}^{|k|}C(q,u_{i})}*Ave_{ranking}(u_{i})\\ &=\frac{C(q,u_{i})}{\sum_{i=1}^{|k|}C(q,u_{i})}*\frac{\sum_{i=1}^{ |N_{i}|}R_{u_{i}}}{|N_{i}|}\end{split} \tag{6}\]  
where \(C(q,u_{i})\) denotes the number that query \(q\) clicks URL \(u_{i}\). \(|k|\) denotes the number of URL that query \(q\) clicks.  
Given the query and URL, we can get a \(t\times h\) matrix \(S(s_{ij})\) which shows the strength of query and URL. \(s_{ij}\) denotes the strength of query and URL. \(t\) denotes the number of queries and \(h\) is the number of URL.  
\[S(s_{ij})=\left[\begin{array}{cccc}s_{11}&s_{12}&\dots&s_{1h}\\ s_{21}&s_{22}&\dots&s_{2h}\\ \dots&\dots&\dots&\dots\\ s_{t1}&s_{t2}&\dots&s_{th}\end{array}\right] \tag{7}\]  
Figure 3: The structure of query, clicked URLs and ranking.  
The relevance of queries based on URL information can be measured using the cosine measure [21] as follows:  
\[Rel_{URL}(q_{i},q_{j}) =\frac{S(s_{i})\cdot S(s_{j})}{|S(s_{i})||S(s_{j})|} \tag{8}\] \[=\frac{\sum_{k=1}^{h}{(s_{ik}\times s_{jk})}}{\sqrt{\sum_{k=1}^{h}{ (s_{ik})^{2}}\times\sqrt{\sum_{k=1}^{h}{(s_{jk})^{2}}}}}\]  
where \(S(s_{i})\) denotes the \(i\)th row of the matrix \(S(s_{ij})\), \(s_{ik}\) denotes an elements in matrix \(S(s_{ij})\), \(S(s_{j})\) denotes the \(j_{th}\) row of the matrix \(S(s_{ij})\), \(s_{jk}\) denotes an elements in matrix \(S(s_{ij})\).
# An FPGA-Based Performance Analysis of Hardware Caching Techniques for Blockchain Key-Value Database
## 5 Results and Discussion
### Delay When a Cache Hit or Miss Occurs  
The operating frequency for each cache system is fixed as 200 MHz for fair comparison between them. The average access time is also the same for direct-mapped and set-associative cache techniques in the proposed system at the cost of extra resources used in the set-associative cache technique. In the 2-way and 4-way set-associative techniques, the average access time is not increased as the memory indexing depth is the same in all proposed cache systems. The average access time is \(T_{access}=\frac{Number\ of\ clock\ cycles\ used}{frequency}=5\) ns  
Figure 14: Flow chart diagram of set-associative cache technique for cache hit and miss.  
when the cache hits. On the other hand, if the cache is missed, the additional main memory access time will be added, which in this case is equal to 30 ns (i.e., 6 clock cycles). In addition, in the 4-way set-associative cache, the miss rate is lower than the other techniques; so, the chance to access main memory is lower than the other techniques. Eventually, this significantly improves the average access time as the main memory access time can be significantly greater than the cache access time for a large number of keys.  
Figure 15 shows that the cache controller takes one clock cycle to obtain output when a cache is hit, whereas it takes three cycles of a clock to obtain output when a cache is missed. In Figure 15, the first two inputs are the cache miss case and it takes three cycles to obtain output, whereas the other two input addresses are of the cache hit case. Therefore, after one cycle, we instantly obtain the output data. The wait signal shows if we are accessing the memory or not. When the wait signal is zero, it means that the memory is being accessed and there will be no new input data. The wait signal is zero when there is a cache hit. The signal _hit_ shows if it is a cache hit or miss. There is always a cache miss when the data are being read for the first time; so, the cache controller receives output from the server and data are saved in the cache. In the subsequent access request for the same data, we receive output from the cache and the hit becomes one.
# Compression Behavior of Rubber-Bentonite Mixture under Different Salinities
## 2 Results and Discussions
### Compression Coefficient Change of MX80 Bentonite/Rubber Mixture
#### 2.1.3 Effect of Salt Solution Concentration on Compression Modulus under Different Vertical Loads  
From Figure 4a, it can be observed that at vertical loads of 100 kPa and 200 kPa, the compressive modulus of the B6T4 sample significantly decreased as the salt solution concentration increased from 0 mol/L to 0.1 mol/L. When the vertical load increased to 300 kPa and 400 kPa, the compressive modulus of the B6T4 sample remained within a stable range. On the other hand, for the B4T6 sample, under vertical loads of 100 kPa, 200 kPa, and 300 kPa, the compressive modulus decreased continuously with an increase in salt solution concentration. However, when the vertical load reached 300 kPa, the compressive modulus increased.  
From Figure 4b, it can be seen that at any vertical load, the compressive modulus of the B10 sample gradually increased with an increase in salt solution concentration. Especially at a vertical load of 300 kPa, the compressive modulus of the B10 sample showed the most significant variation as the salt solution concentration increased from 0.1 mol/L to 0.5 mol/L NaCl.  
Figure 3: Relationship between compression coefficient and vertical load of B10 (**a**), B8T2 (**b**), B6T4 (**c**), and B4T6 (**d**) samples under different NaCl solutions.  
[PERSON] et al. (2018) observed the consolidation and hydraulic characteristics of rubber-fiber-mixed BC soil in the presence of two inorganic salt leachates (NaCl and CaCl\({}_{2}\)) at two concentrations (0.1 and 1 N). The study found that there was a slight reduction in the compression index when saltwater solutions were used as pore fluids. The reduction in the compression index was slightly greater when the concentration of the solution increased from 0.1 mol/L to 1 mol/L. The compression index values decreased for all fiber-treated soil samples observed under salt solutions.  
These research findings indicate that the consolidation characteristics of rubber-fiber-mixed BC soil are influenced by the presence of saltwater solutions. Increasing the concentration of the solution further decreases the consolidation characteristics, while the addition of rubber fiber has a certain improvement effect on the compressibility performance of bentonite clay soil.  
Overall, samples with different mass ratios responded differently to salt solution concentration and vertical load. The B6T4 and B8T2 samples, with higher rubber content, were less influenced by the salt solution concentration. In contrast, the B4T6 sample was more sensitive to the presence of salt solution ions. The B10 sample exhibited effective resistance to salt solution erosion under various conditions, and as the vertical load increased, its internal structure became more compact, resulting in an increase in the compressive modulus.  
Figure 4: Relationship between NaCl solution concentration and vertical load for B10 (**a**), B8T2 (**b**), B6T4 (**c**), and B4T6 (**d**) samples under different vertical loads.
# Current Status, Issues and Future Prospects of Personalized Medicine for Each Disease
## 4 Alzheimer's Disease
### Establishment of Early Diagnosis Method  
As biomarkers characteristic of AD, a decrease in the ratio of A$42/A$40 in cerebrospinal fluid (CSF) that reflects the deposition of A$ in the brain, amyloid imaging using positron emission tomography (PET), p-tau in CSF, and tau imaging using PET, which reflect the accumulation of tau in the brain, and t-tau, NfL, etc. in CSF, which reflect neurodegeneration, are shown. However, one of the problems of PET is that the cost per examination is high and the number of facilities where the examination can be performed is limited. Furthermore, CSF is a highly invasive examination. The problem is that the throughput of the common issue between PET and CSF examinations is poor. On the other hand, in recent years, the research groups from the National Center for Geriatrics and Gerontology and Shimadzu Corporation measured A$-related peptides (APP669-711, A$\beta$1-40, and A$\beta$1-42) by blood tests, and compared the results with those of PET scans to determine the presence or absence of abnormal accumulation of A$-related peptides in the brain. It was possible to estimate with high accuracy [74]. It is desired that such low-cost, safe, and high-throughput biomarkers will be put into practical use at an early stage, and the diagnosis of dementia will be widely spread among the general public, leading to the early detection and early diagnosis of dementia [75].
## 2 Theoretical Fundaments
The deduced pixel relation can be observed in (3), where \(\alpha_{S}/\alpha_{I}\) is the geometric ratio, which is the relation between the number of pixels of signal respect to the number of pixels of interference within the frame's region of interest (ROI). \[\frac{Pix_{S}}{Pix_{I}}=\frac{\alpha_{S}}{\alpha_{I}}\cdot\frac{\tau\big{\{} \int P_{\mathrm{S}}(\lambda)H(\lambda,0)R(\lambda)F_{\mathrm{S}}(\lambda)d \lambda\big{\}}}{\tau\big{\{}\int P_{I}(\lambda)H(\lambda,0)R(\lambda)F_{ \mathrm{S}}(\lambda)d\lambda\big{\}}} \tag{3}\]  
Since the distance variation between emitters and the receiver is minimal, the geometric related part of \(H(\lambda,0)\) can be considered dependent only of \(D\). Additionally, the scenario is LOS (Line of  
Figure 3: OCC System Diagram. Figure 2: Experimental testbed design for determining the optical interference of close emitters and for validating the relation between projected pixels and real distances. For all the experiments \(\beta\) was set to 90\({}^{\circ}\), \(\gamma\) and \(D\) varied according to the test parameters. Sight) with short distances without aerosols, so the extinction factor is unitary for all \(\lambda\). Therefore the term \(H(\lambda,0)\) can be considered as constant and can be taken out of the integral and simplified. The Geometric Ratio \(\alpha_{S}/\alpha_{I}\), expressed as the Normalized Power Signal to Interference Ratio (NPSIR) can be determined using (4). This interference ratio is independent of the transmitted optical power, the Silicon responsivity, and the camera's Bayer filter; and can be used as a basic measure for simulating more complex scenarios. \[NPSIR\cong\frac{Pix_{S}}{Pix_{I}}\cdot\frac{\int P_{I}(\lambda)R(\lambda)F_{S} (\lambda)d\lambda}{\int P_{S}(\lambda)R(\lambda)F_{S}(\lambda)d\lambda} \tag{4}\]  
Finally, for the simulation of the WSN of Figure 1, the thermal noise can be neglected due to its minimal effect over cameras in environments with controlled temperature conditions. The ambient light is assumed to be set as a low power intensity signal, constant over the transmission time. Therefore the interference of nearby transmitters can be considered the only source that will significantly affect the communication link. Additionally, since the selected dome camera for the WSN scenario has a CCD image sensor, the shutter type is assumed as global.
## 2 Materials and Methods
### Hyperspectral Image Acquisition  
The displacement of the sample translation unit was 210 mm in 0.2-mm increments under illumination (500 mm below the camera lens), and the single-pixel size of the hyperspectral image was 0.2 mm. The hyperspectral data of the mixture samples were acquired simultaneously during translation. The scan area described by one pixel was 0.04 mm  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Materials** & **NaCl (mg/g)** & **Capsaicin (mg/g)** \\ \hline FW fertilizer & 752.52 & 1637.31 \\ Castor oil cake & 57.29 & 0 \\ Rapeseed oil cake & 37.77 & 0 \\ Soybean oil cake & 2.56 & 0 \\ Bone dust & 479.32 & 0 \\ Rice bran & 9.64 & 0 \\ Fish meal & 412.58 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: NaCl concentrations of major materials used for organic fertilizer in Korea.  
[0.2 mm (spatial) \(\times\) 0.2 mm (scan)]. The camera region of interest (ROI) was identical to the translation unit size (25 mm \(\times\) 25 mm). The backgrounds of the sample images were removed by setting the pixels to zero, to exclude unnecessary pixel information. Thus, the specifications of the obtained 3D hypercubes of each sample were 125 (spatial) \(\times\) 125 (scan) pixels with 128 wavelengths (channels), excluding their backgrounds.  
The actual spectral information of the hyperspectral images was obtained through relative reflectance correction (spectral calibration) of the sample images, using a 99% diffuse reflectance white reference image (SRT-99-120, Labsphere, North Sutton, NH, USA) and a dark reference image. The dark reference image was acquired by deactivating the illumination and was obtained to enable correction for noise from the EMCCD camera. The spectral calibration reflectance values were acquired according to the following equation [14; 30]:  
\[I_{R}=\frac{I_{r}-I_{b}}{I_{w}-I_{b}} \tag{1}\]  
where \(I_{R}\) is the relative reflectance image and \(I_{r}\), \(I_{w}\), and \(I_{b}\) are the obtained hyperspectral, white reference, and black reference images, respectively. Before image processing for FWC detection in the mixture samples, the spectral data of the raw materials were acquired for analysis of their chemical characteristics. Then, after background removal and spectral calibration, the hyperspectral images of the samples were used to develop the optimal model.  
Figure 1: Conceptual diagram of HSI system: (**a**) imaging spectrograph with electron-multiplying CCD, (**b**) C-mount lens, (**c**) quartz–tungsten halogen lamps, (**d**) sample holder, and (**e**) programmable monoaxial stage.
# Focused Ultrasound in the Rehabilitation Setting:
## 1 Introduction  
Focused ultrasound (FUS) is a non-incisional ablative medical technology that uses US waves concentrated via a curved transducer, lens, or phased array to achieve precise therapeutic effects [1, 2]. This differs from conventional diffuse US, used for diagnosis or general rehabilitation. This technique enables targeted interventions with minimal impact on surrounding tissues, offering a safer alternative to invasive procedures [3]. Initially applied in oncology for conditions like uterine fibroids and liver lesions, FUS has expanded its applications to neurological and musculoskeletal disorders due to advancements in imaging and acoustic technologies [4, 5, 6]. Its ability to penetrate the skull and interact with brain structures has made it particularly promising for treating brain-related conditions, addressing challenges such as the blood-brain barrier (BBB) and surgical risks associatedwith traditional approaches [7]. FUS operates through various mechanisms, including thermoablation, mechanical disruption, and neuromodulation [8]. These modalities have been applied across diverse clinical domains. For instance, MR-guided focused ultrasound (MRgFUS) has shown efficacy in treating movement disorders like essential tremor (ET) and Parkinson's disease (PD) through precise thalamotomy [9]. In psychiatric disabilities, FUS has been explored for treatment-resistant obsessive-compulsive disorder (OCD) and major depressive disorder (MDD) via anterior capsulotomy [10]. Additionally, FUS has demonstrated potential in opening the BBB for drug delivery in Alzheimer's disease and enabling pain relief in musculoskeletal (MSK) conditions [11; 12].  
The therapeutic implications of FUS extend beyond disease management to rehabilitation. Its ability to modulate neural circuits noninvasively offers opportunities for motor and cognitive rehabilitation following stroke or traumatic brain injury. Furthermore, its application in pain management could enhance functional recovery in patients with chronic pain or neuropathic conditions. Despite these promising prospects, the integration of FUS into rehabilitation remains underexplored, necessitating further research to optimize protocols and broaden its clinical utility. This review aims to evaluate the current applications of FUS across various clinical domains, emphasizing its potential role in rehabilitation. This paper seeks to highlight the translational opportunities of FUS while addressing gaps in knowledge that could guide future research and clinical practice.
# Privacy-Aware Access Protocols for MEC Applications in 5G +
## 3 Statement of the Problem  
In order to formalize the problem of privacy-preserving MEC usage, we introduce a system model, an adversary model, and then identify privacy requirements for the parties in the system model.
###### Abstract  
The area of urban impervious surfaces is one of the most important indicators for determining the level of urbanisation and the quality of the environment and is rapidly increasing with the acceleration of urbanisation in developing countries. This paper proposes a novel remote sensing index based on the coastal band and normalised difference vegetation index for extracting impervious surface distribution from Landsat 8 multispectral remote sensing imagery. The index was validated using three images covering urban areas of China and was compared with five other typical index methods for the extraction of impervious surface distribution, namely, the normalised difference built-up index, index-based built-up index, normalised difference impervious surface index, normalised difference impervious index, and combinational built-up index. The results showed that the novel index provided higher accuracy and effectively distinguished impervious surfaces from bare soil, and the average values of the recall, precision, and F1 score for the three images were 95%, 91%, and 93%, respectively. The novel index provides better applicability in the extraction of urban impervious surface distribution from Landsat 8 multispectral remote sensing imagery.  
Keywords:ratio-based impervious surface index; remote sensing information extraction; urban impervious surface; urbanisation +
Footnote †: journal: Applied Sciences