esa-sceva/satcom-chunk-collection huggingface dataset:
# Structure of All-Digital Frequency Synthesiser for IoT and IoV Applications
## 2 Phase Noise in All-Digital Frequency Synthesisers
As is seen from Equations (1) and (3), the quantization noise of TDC and DCO decreases with increased reference frequency and \(\Delta\Sigma\) dithering frequency (in case of DCO). But increasing these frequencies will also increase power consumption of the system, which is undesirable in IoT and IoV applications. Therefore, there is present a classical task of searching for compromise between quantization noise and power consumption. Total frequency synthesiser's phase noise can be defined as [11]:  
\[\mathcal{L}_{\text{FS}}=\mathcal{L}_{\text{Osc,REF}}(f)\cdot|H_{\text{cl,REF }}(f)|^{2}+\mathcal{L}_{\text{TDC}}\cdot|H_{\text{cl,TDC}}(f)|^{2}+\mathcal{L} _{\text{Osc,DCO}}(f)\cdot|H_{\text{cl,DCO}}(f)|^{2}+\mathcal{L}_{\Delta\Sigma }(f), \tag{4}\]  
where \(H_{\text{cl,REF}}\), \(H_{\text{cl,TDC}}\), \(H_{\text{cl,DCO}}\), are respectively closed-loop transfer functions of reference signal noise, TDC quantization noise and oscillator's component of DCO noise, whose \(s\)-domain representations are shown in Equations (5)-(7) [15]. Expression of \(\Delta\Sigma\) quantization noise, shown in Equation (3) takes into account its noise transfer function, therefore it is not multiplied by the power transfer function in Equation (4). \[H_{\text{cl,REF}}(s)=N\frac{H_{\text{ol}}(s)}{1+H_{\text{ol}}(s)}, \tag{5}\]\[H_{\text{d,DCO}}(s)=\frac{1}{1+H_{\text{d}}(s)}, \tag{6}\]  
\[H_{\text{d,TDC}}(s)=\frac{H_{\text{d}}(s)}{1+H_{\text{d}}(s)}. \tag{7}\]  
\(H_{\text{ol}}\) in Equations (5)-(7) are open loop transfer functions of the type-II higher order frequency synthesiser. If four additional IIR filters are used in the structure of the synthesiser, it is defined by Equation (8). \[H_{\text{ol}}(s)=\frac{\rho f_{\text{REF}}^{2}}{s}\cdot\frac{1+s/(\rho f_{ \text{REF}}/\alpha)}{s}\cdot\prod_{i=0}^{3}\frac{1+s/f_{\text{REF}}}{1+s/ \lambda_{i}f_{\text{REF}}}, \tag{8}\]  
here \(\alpha\) and \(\rho\) are respectively proportional and integral gain of the synthesiser's filter and \(\lambda_{\text{i}}\) is the transfer coefficient of each IIR filter stage. Transition from \(s\)-domain to linear frequency can be made by using \(s=j2\pi f\) substitution.
## 3 Results  
### SOC Content  
SOC content is illustrated in Figure 3. As the soil profile deepens, the SOC content for each treatment exhibits a consistent pattern of gradual decrease from the surface layer to the bottom layer. Throughout the research process, the SOC content across the 0-60 cm range was highest in the FB treatment and lowest in the MSR treatment. In the 0-20 cm soil layer, the variation in SOC content among all treatments was most pronounced, with the order of abundance being FB \(>\) GB \(>\) MC \(\approx\) SC \(>\) MSR. Notably, the FB treatment was significantly higher than the others, with increases of 22.88%, 52.35%, 60.64%, and 80.12% compared to GB, MC, SC, and MSR, respectively. Each treatment experienced a substantial decrease in SOC content in the 20-40 cm and 40-60 cm soil layers, with trendsmirroring those observed in the surface layer. These results indicate that the forest belt has a more pronounced effect on enhancing soil organic carbon levels compared to other land use patterns.
# Investigating the Effect of Motion Capture Suits on the Test-Retest Reliability of Gait Parameters
## 2 Materials and Methods
### Statistical Analysis  
For each participant, the mean and standard deviations (SD) of each metric (ankle, knee and hip angle at toe-off, heel-strike, maximum and minimum) for each leg was calculated from each trial, resulting in six values (left and right ankle, knee and hip) across the four metrics (HS, TO Max and Min). The SD of the means across the 3 trials was also calculated.  
Intraclass correlation coefficient (ICC) values [20] were used as a measure of relative reliability. Interpretation of ICC values was conducted according to published guidance [21]. An ICC of less than 0.5 was considered poor, between 0.5 and 0.75 moderate, between 0.75 and 0.90 good and above 0.90 excellent [21]. The Minimal Detectable Change (MDC) is the minimal amount of change that would be accepted as a real change greater than that seen due to random variation or error [22]  
The ICC (2) Two Way Random Effects [20] was calculated using the ICC function in SPSS (Version:29.0.0.0) [23] using the three mean values from each suited and non-suited trial. Standard error of measurement (SEM) was calculated using Equation (1) [24], after which MDC was calculated (Equation (2)).  
\[SEm=SD(\sqrt{1-ICC}) \tag{1}\]  
SD = Standard deviation of the mean values across 3 trials  
ICC = Intra Class Correlation Coefficient  
\[MDC=SEm(1.96(\sqrt{2})) \tag{2}\]  
ICCs and MDCs for each metric and average values over the four metrics were tabulated for each joint. Paired \(t\)-tests [25] were used to check for a statistically significant difference between the mean ICC and MDC of the suited and non-suited trials.  
A paired \(t\)-test was used to identify if the alternative PiG foot segment definition made a difference in the ICC and MDC results. A result was considered significant if the \(p\) value was less than 0.05. A paired t-test was also used to identify whether the ICCs and MDC of joint angles were significantly different using the CGM2 model compared to the PiG.  
Figure 1: (**a**) PiG foot segment definition using standard PiG instructions. The AJC is used as the proximal joint centre and the toe as the distal joint centre. The heel marker is then used as a posterior marker to define the orientation. Cone arrows indicate segment coordinate system. (**b**) Alternative foot segment definition. The AJC is used as the proximal joint centre and the toe as the distal joint centre. The lateral ankle marker is then used as a lateral marker to define the orientation. Cone arrows indicate segment coordinate system.
# Efficient SMC Protocol Based on Multi-Bit Fully Homomorphic Encryption
## 5 Secure Multi-Party Computation Protocol Based on NFHE Scheme
### Correctness  
The correctness of the agreement mainly depends on two aspects:  
* It has been proven that it is right to use the NFHE scheme in the protocol, so it is only necessary to verify whether the parameters used are correct. Besides, it can be seen from this scheme that through setting the parameter mentioned above, the noise does not exceed \(pN+p\) times of the original ciphertext after each homomorphic operation. Therefore, when \(|\langle C_{m-1},s^{\prime}\rangle|<q/\Big{[}4p(pN+p)^{L}\Big{]}\), if it does not exceed \(L\) homomorphic operations, \(|\langle C_{m-1},s^{\prime}\rangle|<q/(4p)\), and the scheme can be decrypted correctly.
* The correctness of the encryption and decryption of the protocol mainly involves three issues. Upon analyzing the key homomorphism in the previous scheme, it can be seen that the key pair used in protocol \(\pi_{f}\) is effective. Besides, from Theorem 1, it can be seen that the joint error in protocol \(\pi_{f}\) also obeys \(B_{\chi}\) bounded distribution. Then, prove the correctness of the protocol joint decryption.  
**Proof.** According to \(\mathbf{\eta}=\frac{1}{N}\sum\limits_{i=1}^{N}\mathbf{\eta}_{i}=\bar{\mathbf{t}}\mathbf{C}+\frac{1} {N}\sum\limits_{i=1}^{N}\mathbf{\gamma}_{i}=\bar{\mathbf{t}}\mathbf{C}+\mathbf{\gamma}\)  
\[\begin{array}{l}\text{There is }\mathbf{\eta}\mathbf{G}^{-1}\big{(}\mathbf{w}^{T}\big{)}=(\bar{ \mathbf{t}}\mathbf{C}+\mathbf{\gamma})\mathbf{G}^{-1}\big{(}\mathbf{w}^{T}\big{)}\\ =\bar{\mathbf{t}}\mathbf{C}\mathbf{G}^{-1}\big{(}\mathbf{w}^{T}\big{)}+\mathbf{\gamma}\mathbf{G}^{-1} \big{(}\mathbf{w}^{T}\big{)}\\ =\mu\big{[}\frac{\sigma}{2}\big{]}+\Big{(}\mathbf{\gamma}_{1}^{\prime},\cdots,\mathbf{ \gamma}_{(\mathbf{\eta}-l)}^{\prime},0,\cdots,0\Big{)}\mathbf{G}^{-1}\left(\begin{array} []{c}0\\ \vdots\\ \big{[}\frac{\sigma}{2}\big{]}\end{array}\right)=\mu\big{[}\frac{\sigma}{2} \big{]}\\ \text{Since }\mathbf{G}^{-1}\big{[}\frac{\sigma}{2}\big{]}\text{ is a bit decomposition of }\big{[}\frac{\sigma}{2}\big{]}\text{, the maximum decomposition length of }\big{[}\frac{\sigma}{2}\big{]}\text{ is }\lfloor\log q\rfloor+1\text{. Moreover, from }l=\lfloor\log q\rfloor+1\text{, the maximum length of }\mathbf{G}^{-1}\left(\begin{array}{c}\frac{\sigma}{2} \big{]}\\ \vdots\\ \big{[}\frac{\sigma}{2}\big{]}\end{array}\right)\end{array}\]  
is \(l\). Then, as the last \(l\) bits in \(\gamma\) are all \(0\), and the protocol can correctly perform joint decryption. \(\square\)
## 1 Introduction
conducted extraction experiments on REEs in fly ash from a coal-fired power plant in China using 12 mol/L HCl or 16 mol/L HNO\({}_{3}\), and the leaching rate of rare earth elements was less than 20% [33]. Clearly, this will inevitably lead to an increase in the use of acid or ammonia during the leaching step and additional energy consumption by reactions between the CFA silicon-aluminum and leaching acids. Moreover, the neutralization step is generally necessary for the extraction process. Therefore, it is inevitably complex and inefficient in operation as well as poor in the economy. Consequently, stepwise desalination is significant in the synergistic utilization of CFA with the simultaneous extraction of Si, Al, and rare earth elements [34]. From the perspective of amplification of the extraction process, the collaborative utilization of various resources in CFA should be considered when researching and designing REEs extraction technology. For example, cement additives, building materials, and high value-added products, such as silica oxide powder and porous zeolite can be made based on their properties and composition characteristics during the REEs extraction process of CFA. In addition, the acid and alkali reagents consumed in the extraction process are the main source of cost, and it is necessary to consider the recycling of the extraction reagents. Therefore, we proposed an improved process for REEs extraction, i.e., prior to the conventional acid leaching, a CFA activation was realized. This consisted of two steps: CFA calcination at elevated temperatures using Na\({}_{2}\)CO\({}_{3}\) and water washing followed by alkali washing. This new additional procedure helped in the development of pore structures in CFA, the later-on access of leaching solution to REEs inside CFA, and the less acid consumption for the later-on REEs leaching. Simultaneously, the aforementioned water and alkali washing also generated aqueous solutions, leading to the generation of two new valuable products including the purified silica oxide powder and porous zeolites. The REEs in solid residue, which were generated from previous calcination and washing procedures, can be easily and successfully applied in a higher extraction efficiency via the conventional leaching method in an acid saving manner. Meanwhile, the involved Na\({}_{2}\)CO\({}_{3}\) can be recyclable. Moreover, a multiple of value-added products can be obtained by this new extraction method with great economic significance. In this study, various process parameters (i.e., NaOH concentration, heating temperature, solid-liquid ratio, and holding time) will be investigated and optimized to maximize the silicon and aluminum recovery through alkali leaching of the activated CFA sample. Additionally, the obtained value-added products will be evaluated, and the effect of water and alkali washing operation on REEs extraction efficiency as well as acid saving rate will be investigated, in order to achieve the process coupling of REEs extraction process and effective utilization of all substances in CFA.
## 2 Materials and Methods  
### Plant Material and Chemicals  
_E. globulus_ leaves were collected from the local farm (Haman-gun, Republic of Korea). Gallic acid, quercetin, dimethyl sulfoxide, and Folin-Ciocalteu's phenol reagent were purchased from Sigma Aldrich (St. Louis, MO, USA). Sodium carbonate, sodium nitrite, and aluminum nitrate were purchased from Daejung chemicals (Daejeon, Republic of Korea). For the quantitative analysis, identified authentic samples such as salicylic acid \(\beta\)-D-glucuronide (Toronto research chemicals Inc., Toronto, ON, Canada), chlorogenic acid (ALFA AESAR, Ward Hill, MA, USA), epicatechin, quercetin (Sigma Aldrich), quercetin-3-O-glucuronide, and 2\({}^{\prime\prime}\)-O-galloylhyperin (Wuhan ChemFaces Biochemical Co., Ltd., Wuhan, China) were used. HPLC-grade water, ethanol, and acetonitrile from Fisher (Fisher Scientific Korea Ltd., Waltham, MA, USA) were used. All standards were extra-pure analytical grade.
# Personalized Contextual Information Delivery Using Road Sign Recognition
## 3 Proposed System
### Contextual Information Integration
#### 3.3.1 User Context  
User-specific data provide critical context for interpreting road sign information and generating personalized outputs. These data include the user's current location, vehicle speed, and intended destination. An overview of the flow of user contextual data utilized by the proposed system is illustrated in Figure 5. These elements are crucial for transforming static information into dynamic and actionable insights [8; 11].  
For example, the user's location determines which road sign is relevant in a given scenario, while the vehicle's speed provides essential context for estimating travel times. The destination ensures that the system filters road sign information to prioritize those directly related to the user's route. By incorporating these user-specific data points, the system aligns its outputs with the user's real-world needs, enhancing its practical utility [15; 16].
# 3D Approaches and Challenges in Facial Expression Recognition Algorithms--A Literature Review
## 3 Conventional and Deep Learning-Based Approaches
### 2D [PERSON] et al. [101] developed the first facial expression recognition model with the property of subject independence as well as translation, rotation, and scale invariance. They employed a CNN, more efficient and compact than the [PERSON]'s model [102] presented the year before, to \"detect smiling or laughing faces based on differences in local features between a normal face and those not.\" In his paper, [PERSON] proposed two independent CNNs, one for facial expressions, and the other for face identity recognition, combined by a Multilayer Perceptron (MLP). FER systems are traditionally evaluated in a subject-independent way or with a cross-database approach, rarely in a subject-dependent way. This last technique, in which only a single person builds each classification model, is used in limited cases [103] where greater importance is given to recognition accuracy than generalization. The subject-independent manner, on the other hand, trains the classifier on a subset of images and evaluates it on the remaining part of the same database, while the cross-database method trains and evaluates the classifier on all of the pictures in different databases. Cross-database tasks are more complicated to satisfy as each database presents different settings of illumination, pose, resolution, etc. Deep neural networks are able to recognize subtle features, and for this reason, perform well in flexible learning tasks reaching high levels of accuracy in FER problems [104].  
In 2014 there was one of the largest and the most challenging computer vision competition, ImageNet 2014 challenge. GoogLeNet, a new architecture of CNN inspired by LeNet [105] and implemented by Google, won the classification and object recognition challenges, achieving a top-5 error rate of 6.67%. In the following years, this same network, trained initially to distinguish between 1000 objects, was re-trained for different objectives, including facial recognition [106]. The performance of 2D facial expression recognition is degraded by pose variation and illumination problems, not present with a three-dimensional approach.
# Deterministic Propagation Modeling for Intelligent Vehicle Communication in Smart Cities
## 3 Simulation Results
### Multipath Metrics
#### 3.3.2 Mean Excess Delay, RMS Delay Spread, and Coherence Bandwidth (CB)  
The mean excess delay (the first moment of the PDP) and the RMS delay spread (the square root of the second central moment of PDP) quantify the time dispersive properties of multipath channels while the Coherence Bandwidth (CB) is the range of frequencies over which two frequency components have a strong potential for amplitude correlation [34]. The NLoS scenarios have much higher mean excess delay than LoS scenarios given that the transmitted signals will encounter many reflections along their path to the receiver.  
Figure 11 shows the mean excess delay, RMS delay spread and CB (for frequency correlation of 0.9). Figure 11(a1,a2) depict the mean excess delay for TX1 and TX2 respectively, where higher values in the extent of distance is increased from the TX are observed, while lower values are related to low RSS due to the blocking of the propagated signal by concrete walls (signal absorption) and glass windows (signal reflection) of the buildings. The roundabout, AV3, AV4, AV5, ST3 and the rightmost segment of AV1-AV2 display the highest mean excess delay for TX1, while the leftmost segment of AV1-AV2 display this behavior for TX2.  
Figure 11(b1,b2) illustrate the RMS delay spread. Lowest values keep a close relationship with lowest values of RSS due to the NLoS conditions. Some factors such as the presence of obstacles (trees, lampposts, cars) and the LoS conditions cause high RMS delay spread values near the TXs. As the TX-RX distance is increased, the amplitudes of the reflected signals relative to the direct path become larger, and this produces the increase of the mean excess and RMS delay spread as is depicted in Figure 11a,b.  
Figure 10: Power Delay Profile: (**a**) measured points; (**b**) transmitting TX1; (**c**) transmitting TX1 and TX2.  
Figure 11(c1-c3) depict the CB values of TX1, TX2 and TX1 and TX2 respectively. High CB values, in areas such as AV3, AV4, AV5, ST2-ST3 (NLoS), etc., correspond with low RMS delay spread (given its inverse relationship) and high CB could be considered as an indicator of channel availability for TXs other than TX1 or TX2. On the other hand, low CB values are present at the vicinity of the TXs due to its multipath environment, which means high channel occupancy. Figure 11(c3) illustrates a superimposed CB contour map of TX1 and TX2 to gain insight of the joint CB and low CB values along  
Figure 11: (**a1–a2**) Mean Excess Delay; (**b1–b2**) RMS delay spread; (**c1–c3**) Coherence Bandwidth.  
AV1-AV2 and ST1 are observed. The CB analysis must be complementary with the analysis of RSS, and coverage and RMS delay spread mentioned above.
## 6 Security and Efficiency Analysis
### Efficiency Analysis
#### 6.2.3 Energy  
Energy analysis is also performed to see the additional load of the proposed protocol on the WSN. A digital multimeter is used for energy measurements. An Excel Alkaline battery is used as energy in the nodes. The following procedure is followed for comparison. Before starting the nodes, the first energy measurements (first) of the nodes are made.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Increase** & **Normalize** \\ \hline Traditional & 5.16 ms & 1.00 \\ \hline Blockchain-based & 6.27 ms & 1.21 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Latency.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Increase** & \\ \hline  & **ROM** & **RAM** \\ \hline Blockchain-based & 25\% & 6.64\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Memory usage.  
Then, the nodes are started and run for 1 h. After 1 h, the nodes are closed and energy measurements (last) are made again. The result of the last-first operation gives the total energy consumption in 1 h. This process is repeated 5 times and average results are obtained. As already stated in the literature [67], making measurements based on the mAH value increases the accuracy of the measurement when two batteries have an equal amount of voltage. The mAH value denotes the total amount of energy consumption in one hour. The results are given in Table 4.  
While the energy consumption was 0.000147 mAH in the traditional case, this value is obtained as 0.000156 mAH when the proposed protocol is implemented. There is an increase of 6%.
# An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing
## 2 Related Work
### IoD Security Frameworks  
The special nature of drone networks requires dedicated security architectures to deal with their specific issues. [PERSON] et al. [35] discussed the security architecture for dronecommication networks, revealing flaws in current protocols and recommending a multi-layered security scheme. Likewise, [PERSON] et al. [36] presented an extensive study of cyber threats in UAV networks, classifying attacks according to their effects on confidentiality, integrity, and availability of drone services. [PERSON] et al. [37] presented a self-deployment UAV network structure with security measures tailored for emergencies, highlighting the significance of secure communication in dynamic settings but without comprehensive authentication mechanisms. Closing this shortcoming, [PERSON] et al. [38] created a hierarchical security framework for swarms of drones that integrated cryptographic primitives and secure routing protocols. [PERSON] et al. [39] proposed a fog-supported secure Internet of Drones (IoD) scheme that promotes better data security along with less latency, exhibiting more efficiency than existing cloud-based designs, but demanding great amounts of edge computing hardware. Following this theme, [PERSON] et al. [40] advanced software-defined networking (SDN)-powered security solutions for anIoD system based on flexible enforcement of security policy on heterogeneous networks of drones. Recent developments involve zero-trust architectures [41] for ongoing identity authentication [42] and blockchain-supporting frameworks for tamper-evident activity logging with efficiency preservation [43]. Our solution implements ongoing verification with smaller computational needs compared to blockchain-based implementations.