esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology Setup
### Measurement Data Processing
And \(y\) is restricted to the surface \(Y\) using the same indicator function \(I_{A}\) (\(b\)). Unlike traditional ICP, this method used the P-order norm for optimization, and the order \(p\) in sparse ICP is set to \(0\leq p\leq 1\), where \(p\) is suggested to be set to 0.4 according to this article [15]. For verifying the actual effect of using the sparse ICP algorithm in this situation, we registered a set of DOE measured data after preprocessing to the 2D DOE design model. The registration result of the ICP algorithm is shown in Figure 5. The red line represents the unfolded design model data, and the blue line represents the matched measured data. Here we used the root mean square (RMS) value of the distances between the registration pairs. The RMS value of the result was 56.58 nm, which proves the effect of the algorithm is good. Then, we did another experiment to test the effect of the algorithm when it is used to register the same set of measured data with a 3D design model of the same DOE. We registered the set of measured data to the 3D DOE design model. Figure 6 illustrates the registration results on the 3D design surface. The RMS value of this result was 68.72 nm, which is acceptable registration accuracy. These two tests can prove that the sparse ICP algorithm is suitable for analyzing the measured data of DOEs. Figure 4: Measured data filtering. (**a**) Before filtering; (**b**) after filtering. Based on the results of data matching, the error of the alignment between the optical axis of the probe and the DOE central vertex of the curved base was determined. The iterative optimization method was used to find the best alignment data, and data processing was performed to verify the accuracy of the measurement optical axis alignment, and to ensure the surface contour measurement accuracy after three-dimensional reconstruction. The data processing method effectively solved the problem that the inclination of the measured data and the measurement path did not cross the vertex of the center of the base. After ICP algorithm processing, the mapping relationship between measurement data and design model could be obtained, and then the measured data could be \"restored\" back to the design model base. The piecewise linear interpolation method was used for the restoration. The unfolded model base and the unexpanded model base data were used as known data, and the processed measurement data were calculated as a value that needed to be interpolated. Finally, the matched measurement data should be restored to the design model to obtain the complete three-dimensional shape of a curved-base DOE. Figure 5: Illustration of the data matching process. (**a**) Pre-processed before registration; (**b**) after registration. Figure 6: The effect of the measured data matching the 3D design model.
## 2 Materials and Methods
### Methods
#### 2.2.8 HPLC-DAD Analysis of Phenolic Compounds [10]  
Phenolic compounds were extracted as follows: 1 g of oil was mixed with 1 mL of hexane, and then a methanol/water mixture (3:2, \(v/v\)) was added, and the sample wasvortexed and centrifuged at 3000 rpm for 3 min. The extraction was performed in an ultrasonic bath at room temperature, with a frequency of 21 kHz and a heating power of 300 W for 20 min. The procedure was repeated four times. Subsequently, 2 mL of hexane was added to the extracted solution, vortexed, and centrifuged at 3000 rpm for 5 min. The hexane was then evaporated using a rotary evaporator, and then dried under a stream of nitrogen. The final extract was reconstituted in 500 \(\mu\)L of a methanol/water mixture (1:1, \(v/v\)).  
The analyses were conducted using a Shimadzu HPLC system (Shimadzu, Kyoto, Japan), comprising an LC-40D XR solvent delivery module, an SIL-40C XR autosampler, a CTO-40C column oven, and a DGU-405 degasser. The system was coupled with an SPD-M40 photodiode array (PDA) detector.  
Phenolic compounds were separated using a Luna pentafluorophenyl column (150 mm \(\times\) 4.6 mm i.d., five \(\upmu\)m particle size; Phenomenex, Torrance, CA, USA). The mobile phase consisted of solvent A (water with 0.1% formic acid) and solvent B (methanol/acetonitrile 1:1, \(v/v\), with 0.1% formic acid), with a constant flow rate of 0.2 mL/min at 40 \({}^{\circ}\)C. The gradient elution program was as follows: 0-5 min, 5% B; 5-15 min, linear increase to 30% B; 15-40 min, gradient from 30% to 50% B; 40-50 min, ramp to 100% B. The injection volume was 35 \(\upmu\)L. Data acquisition with the PDA detector was performed over a wavelength range of 210-400 nm, with chromatograms extracted at 280 nm for quantification.  
Standard compounds were selected based on the typical phenolic profile of extra virgin olive oil. A mixture of three phenolic standards--4-hydroxyphenylacetic acid, oleuppein, and 2-(4-hydroxyphenyl)ethanol--was used for quantitative analysis and method validation. Method validation parameters included linearity, repeatability (intra-day and inter-day precision), limits of detection (LOD) (hydroxytrosol: 0.75 ng/mL; tyrosol: 0.91 ng/mL; 3.75 ng/mL), and limits of quantification (LOQ) (hydroxytrosol: 2.28 ng/mL; tyrosol: 2.75 ng/mL; 11.46 ng/mL). Linearity was assessed through calibration curves generated from HPLC analysis of standard solutions (ranges for tyrosol and hydroxyytrosol were 3-100 ng/mL and, for oleuppein, 25-100 ng/mL). Stock solutions of each standard were prepared in methanol at a concentration of 1 mg/L.
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 4 Experimental Evaluation and Analysis
### Corpus of crypto-ransomware binaries  
The dataset used in this study consists of a total of 9152 binary samples, comprising 8152 crypto-ransomware binaries sourced from the public repository, VirusShare ([[http://www.virusshare.com](http://www.virusshare.com)]([http://www.virusshare.com](http://www.virusshare.com)), accessed on 14 March 2023) repository [60; 61; 62; 63; 64]. This corpus comprises 8152 samples, representing diverse ransomware families including Cerber, TeslaCrypt, CryptoWall, Petya, and WannaCry. These samples were collected between September 2016 and August 2017. Additionally, 1000 benign programs were downloaded from informer.com [63; 64; 65; 66], a widely used repository for Windows-based applications. These files, commonly referred to as portable executable (PE) files, are fundamental to malware analysis. PE files serve as the standard executable format for object code and dynamic-link libraries (DLLs) in Windows operating systems. Both static and dynamic analyses of PE files are crucial for comprehending the structure, functionalities, behavior, and potential impact of ransomware. The dissection of PE files enables the extraction of valuable metadata, the identification of malicious signatures, and the understanding of ransomware behavior, including encryption methods, server communication, and persistence mechanisms. Binary analysis is essential for developing countermeasures and detection signatures, as well as for understanding ransomware tactics. In this study, dynamic analysis was employed, utilizing the PE file within a sandboxed environment.
## 3 Proposed Cascadable Voltage-Mode Inverse Filters
### First-Order VM Inverse Filter Realization  
Figure 2a depicts the realization of the first-order VM inverse filter. The circuit requires one VCII-, one resistor (\(R_{1}\)), one capacitor (\(C_{1}\)), and one impedance (\(Z_{A}\)). It is worth noting that the output voltage terminal of the circuit is directly obtained from the z terminal of VCII-. This configuration offers the benefit of a cascadable terminal with a low output impedance. A straightforward circuit analysis assuming ideal VCII yields the following inverse filter transfer function:  
\[G_{v1}(s)\ =\ \frac{V_{out1}(s)}{V_{in}(s)}\ =\ \frac{sR_{1}C_{1}+1}{sC_{1}Z_{A}} \tag{2}\]  
Equation (2) clearly indicates that, by appropriately configuring the impedance \(Z_{A}\), the first-order ILP and IHP filter responses could be realized in a single configuration, as demonstrated in Table 3. As can also be seen from the table, the gain (\(G_{0}\)) values of ILP and IHP filters can be tuned independently through \(C_{A}\) and \(R_{A}\), respectively. An additional advantage of this realization is that no element-matching realizability conditions are necessary.
## 3 Results and Discussion
### Force SRS Profile Correlation  
The force SRS profile correlation was performed to ascertain its effectiveness in identifying frequency changes occurring prior to maintenance for the more common repairs--quality adjustments and die and non-die damage. The results for 24 to 25 February are shown in Figure 6. According to the 4P SRS correlation in Figure 6a, the SRS profile deviated at 09:28--time to maintenance of 2 h 41 min--and remained at sub-optimal levels until quality adjustment was done. After the quality adjustment, the SRS profile correlation declined for 44 min prior to the material spool change. Upon restarting the machine, the correlation values remained stable until a small but steady decline associated with increased die wear and damage to the fourth clamp was initiated at 01:48--1 h 11 min prior to unplanned maintenance.  
Figure 5: 9 March force impact correlation of machine start-up to subsequent force signal profiles for (**a**) 4P and (**b**) 9P.  
Figure 6: 24–25 February force SRS correlation of machine start-up to subsequent force SRS signal profiles for (**a**) 4P and (**b**) 9P.  
The 9P results in Figure 6b reflected the 4P behaviour. The first indicator of quality degradation occurred 2 h 51 min before maintenance. Detection of the wire change was manifested in falling correlation values. After the wire change, all appeared well until 00:08--2 h 51 min before collision of non-die parts. Correlation values showed a steady decline up to machine failure. During this period, non-die faults progressively worsened until the collision of the clamp and moulds caused machine failure.  
For 9 March, the SRS profile correlation values for 4P in Figure 7a displayed various indicators that the force delivery to the workpiece had been compromised. Ten minutes into the start of the manufacturing day marked the beginning of a spate of fluctuating declines in the correlation values that occurred over 1 h 51 min until quality was adjusted and raw material was replenished. The first indicator of impending die failures occurred at 8 h 53 min before machine breakdown. This was followed by repeated spates of sustained low correlation values, until fluctuations ended at 19:51 with the breakage of 4P.  
The 9 March result for 9P is shown in Figure 7b. After the first 20 min of manufacturing, the force SRS profile correlation started fault deviations at 08:48--1 h 21 min before the needed quality adjustment and wire change. Indications of die faults occurred 2 h 23 min before their failures at 19:51.  
The correlation of the force SRS profiles was useful to identify fault initiation in the die and non-die components. Quality deterioration was also detectable in its initial stage due to the change in the shock distribution from the changed mould dimensions. Hence, the correlation provided indications of impending damage to die and non-die machine components and quality deterioration in advance of parts' failures or scheduled quality maintenance.
###### Abstract  
BackgroundActive mobilization post-elbow fractures reduces the incidence of complications. Occupational therapists use tele-rehabilitation, incorporating technology into their practices. There is a lack of evidence-based trials regarding the integration of tele-rehabilitation during treatment. We therefore aimed to compare tele-rehabilitation treatment outcomes with conventional rehabilitation in improving the upper limb function post-elbow fractures. **Methods**: Eighteen participants post-elbow surgery due to fracture were divided into two groups according to age and fracture type. The groups received one month of treatment: the tele-rehabilitation group (N = 9, median age 33.0 \(\pm\) 27.9 years, range 18.5-61.0) received 1-2 tele-rehabilitation treatments per week via a biofeedback system of elbow motion (the ArmTutor and 3D Tutor systems, MediTouch Ltd., Netanya, Israel) and 1-2 treatments in an outpatient clinic, and the control group (N = 9, median age 60.0 \(\pm\) 37.0 years, range 20.5-73.0) received 3-4 treatments per week in the clinic. Both groups were instructed to self-practice at home. Four evaluations were performed: before and after the intervention, and 3 months and 1 year from surgery. The outcome measures included the Jebsen-Taylor hand function test; the disabilities of the arm, shoulder, and hand questionnaire; the patient-rated elbow evaluation; satisfaction; passive and active range of motion (ROM); and strength measurements. **Results**: Findings demonstrated a significant improvement in the ROM and in functional assessments in both groups. No statistically significant differences were found between the groups. The subjects in the tele-rehabilitation group reported a higher level of satisfaction and needed less help from a family member during practice. **Conclusions**: Tele-rehabilitation programs could be incorporated in the framework of treatment following elbow fractures. Tele-rehabilitation is a cost-effective treatment, suitable for patients with accessibility difficulties or who have difficulty arriving at the clinic.  
biofeedback; artholysis; tele-rehabilitation; elbow +
Footnote †: journal: Medical Medical Cancer Research
###### Abstract
The inverse problem is formulated as the reconstruction of the geoelectric structure, namely, the determination of the spatial distribution of the medium's physical properties based on the recorded response data. In the context of electromagnetic geophysics, the key information is provided by the horizontal component of the electric field vector measured at the observation point. The numerical algorithms developed in this paper address both the direct and adjoint problems in a discrete form. Moreover, the adjoint problem preserves key physical properties of the model, including energy conservation at the discrete level, thereby enhancing the reliability of the derived results. To enhance the reliability of interpretation, preprocessing of experimental data is essential for isolating informative signals and suppressing noise [25]. The measurement component of the GPR system facilitates the recording of reflected waves, implements automatic time-varying gain control, performs gating, and conducts signal digitization. Gating is performed by the control and synchronization unit. One radar trace is generated through the emission and recording of 512 pulses. Sequential recording of all traces along the profile generates the final radar image. The processing of GPR data comprises the following key steps [25]:  
* data acquisition and validation;* spectral analysis of direct and reflected waves;
* sliding-window summation to mitigate common-mode noise;
* frequency-domain filtering to suppress interference components;
* amplitude correction to account for signal attenuation;
* static corrections accounting for topographic variations;
* spatial interpolation of data along the profile;
* velocity analysis of common-midpoint gathers;
* signal migration to enhance the spatial resolution of detected objects. Based on the theoretical and applied aspects discussed above, the primary objectives of this study are as follows:  
* development of algorithms for the primary processing of GPR data;
* solution of direct and inverse problems for subsurface radar;
* implementation of discrete analogs for optimization methods.
## 3 Energy Storage Life Decay Model
### Capacity Decay Model  
Capacity is a key parameter to measure the battery life decay model. The main factors affecting capacity decay are calendar capacity decay and cycle capacity decay, calendar capacity decay is small, and this paper focuses on the study of battery cycle capacity decay. Battery cycle capacity decay is influenced by the charge/discharge rate, ambient temperature, and cycle times. During the use of the battery, the temperature can be maintained at a constant temperature by the corresponding equipment; as the time-sharing tariff mechanism is used in the dispatching process, the charge/discharge multiplier can also be constrained to about 1C, so this paper mainly studies the influence of the number of cycles on the battery capacity decay [46].  
During the operation of the battery, the capacity of the battery will keep changing as the time and the frequency of charging and discharging increase; it was found through the research that the capacity loss rate of the battery shows a positive relationship with the testing time [47]. As shown in Equation (6).  
\[C(t)=C_{R}(1-b(t-1)) \tag{6}\]  
where \(C(t)\) indicates the capacity of the battery at the time \(t\), \(C_{R}\) is the initial capacity of the battery, and \(b(t)\) is the capacity loss rate at the moment of 1C discharge of the battery.
# Arabic Gloss WSD Using BERT
## 4 Background
### Transformers  
A transformer [8] is a celebrated Neural Network (NN) architecture that was introduced to approach translation tasks. Figure 2 depicts the transformer's main components. It consists of two components: an encoder and a decoder. The encoder is used to encode a source sentence into a sequence of real vectors. The decoder, on the other hand, has the goal of decoding a set of vectors into a target sentence. Both the encoder and the decoder consist of 6 blocks. Each of these blocks consists of two layers: the first layer is a _multi-head attention_ layer, while the second is a fully connected layer. The layers are equipped with a residual connection that connects the input of a layer to the output of that layer. After each block, a Layer Norm [29] is applied.  
Now, we elaborate more on the multi-head attention layer. The intuition behind this layer is that the sense of a given word in a sentence is usually affected by different words in the same sentence (self-attention) or the target sentence, with different degrees. This intuitive idea is implemented in the attention layer by associating three vectors: a key \(k\), a query \(q\), and a value \(v\) with each word. The degree of dependence between a given word \(w_{s}\) and a target word \(w_{t}\) is quantified by the dot product between the query of the word \(q_{w_{s}}\) and the key of the target word \(k_{w_{t}}\). Finally, the word is represented by a weighted average of the values of target words, using \(\langle q_{w_{s}},k_{w_{t}}\rangle\) as weights. The weights are further normalized and a Softmax is applied to ensure that the weights sum to 1 before the weighted average is taken. To obtain a compact formula, keys of target words are put in rows of a matrix \(K\), queries are arranged similarly into a matrix \(Q\), while \(V\) denotes the concatenation of values of target words. Then, the layer output is realized using Equation (1)  
\[\text{Attention}(Q,K,V)=\text{softmax}(\frac{(QK^{T})}{\sqrt{d_{k}}})V, \tag{1}\]  
where \(d_{k}\) represents the dimensionality of the vector \(k\).  
As can be noticed in the above computation of the attention-based word representation, the position of a word in the target sequence is lost as a result of the weighted average over targeted words. This can be alleviated by adding a vector to each word representation that encodes the position of the given word. Such a process is called Positional Encoding (\(PE\)). The \(PE\) vector is achieved using Equation (2).  
\[PE_{(\text{pos},k)}=\begin{cases}\sin(\frac{\text{pos}}{10^{k/d_{k}}})\text{, if }k\text{ is even,}\\ \cos(\frac{\text{pos}}{10^{k/d_{k}}})\text{, if }k\text{ is odd,}\end{cases} \tag{2}\]  
where \(k\) is the index of the value calculated of for the positional vector and pos is the position of the word.
## 2 System Model and Problem Formulation
### Single Q-Learning  
Motivated by [22; 24; 25], we think the single Q-learning method is suitable for the case where the UAV group is not influenced by co-channel mutual interference. In the traditional single-Q learning algorithm, every user maintains and updates its independent Q table \(Q^{n}\); for user \(n\), the updating process of Q function is shown as:  
\[Q_{t+1}^{n}\left(s_{n}\left(t\right),a_{n}\left(t\right)\right)=\left(1- \lambda_{n}\right)Q_{t}^{n}\left(s_{n}\left(t\right),a_{n}\left(t\right) \right)+\lambda_{n}\left[r_{n}\left(t\right)+\gamma_{n}V_{n}\left(s^{\prime} _{n}\right)\right], \tag{9}\]  
where \(\lambda_{n}\) is the learning rate of user \(n\) and \(\gamma_{n}\) represents the discount factor for Q table update. \(r_{n}\left(t\right)\) is the immediate reward of user \(n\) while taking action \(a_{n}\left(t\right)\) under environment \(s_{n}\left(t\right)\), which also can be viewed as the normalized utility, which is:  
\[r_{n}\left(t\right)=\left(1-f\left(a_{n}\left(t\right),a_{j}\left(t\right) \right)\right)\frac{1}{I_{n}\left(c_{n},t\right)}-w_{s}\delta_{s}\left(a_{n} \left(t\right),a_{n}\left(t-1\right)\right)-w_{c}\delta_{c}\left(I_{n}\left(c _{n},t\right)\right), \tag{10}\]  
where \(w_{s}\) and \(w_{c}\) are the normalized switching cost unit and normalized cooperation cost, respectively. The relationship between \(W_{s}\) and \(w_{s}\), \(W_{c}\) and \(w_{c}\) is:  
\[\begin{array}{l}w_{s}=\frac{W_{s}}{\log_{2}\left(1+\frac{B_{s}d_{s}^{\alpha} }{N_{\alpha}}\right)},\\ w_{c}=\frac{W_{c}}{\log_{2}\left(1+\frac{B_{s}d_{s}^{\alpha}}{N_{\alpha}} \right)}.\end{array} \tag{11}\]\(V_{n}\left(s^{\prime}{}_{n}\right)\) is the value function of user \(n\); in single Q-learning, \(V_{n}\left(s^{\prime}{}_{n}\right)\) can be expressed as:  
\[V_{n}\left(s^{\prime}{}_{n}\right)=\max\left\{Q_{t}^{n}\left(s^{\prime}{}_{n},a \right)\right\}. \tag{12}\]  
The defined value function \(V_{n}\left(s^{\prime}{}_{n}\right)\) can be viewed as finding the highest benefit in user \(n\)'s \"memory\" under state \(s^{\prime}_{n}\). Each user in the UAV group adopts independent Q-learning via a \"decision-feedback-adjustment\" and can converge to an optimal channel selection strategy [34]. As the channel switching cost is introduced, it makes sense for users to decrease their channel switching number.
## 6 Profound Impact Potential on Decision-Making Processes
### Advances in Solar Weather Forecasting  
Solar weather events, including solar flares and coronal mass ejections, pose significant risks to satellite operations, space-based communication networks, and space domain awareness. Accurate solar weather forecasting is essential, as solar events can alter atmospheric density, leading to increased drag on satellites--particularly those in very low Earth orbit (vLEO)--and can disrupt critical operations such as tracking adversarial space assets during geomagnetic storms. Booz Allen Hamilton's researchers have developed advanced solar weather forecasting models by leveraging data from NASA's Solar Dynamics Observatory (SDO), which provides continuous high-resolution solar imagery. Using autoencoder-based machine learning algorithms, these models efficiently process vast solar datasets, detecting patterns indicative of emerging solar activity. Trained on over 300,000 high-resolution solar images spanning more than a decade, these models have improved the prediction accuracy of solar phenomena, including sunspots and flares [42].  
By adopting an open-source development framework, researchers ensure continuous model refinement through the integration of diverse datasets. These AI-driven modelscontribute to enhanced forecasting of atmospheric drag effects on satellites, more precise orbit determination, and unified frameworks for space domain awareness.