esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Analysis
#### 5.4.1 Dataset  
In previous section(s) we have noted the performance improvements of _Expert-B_ over Phoenix for the English benchmark, while the performance ratio was not significantly higher, this still demonstrated that the combination of the English instruction dataset and the identify model produced much better answers compared to the basic method of generating answers using only instruction. However, in terms of Vietnamese data generation _Expert-B_ shows an inferior performance as compared to Phoenix with a performance rate of only 96%.  
When compared directly to Phoenix there are two main differences that we consider that lead to the lower performance for _Expert-B_ with respect to the Vietnamese language:  
1. The Vietnamese data generation process consists of two phases: using 'GPT-3.5-turbo' API to translate instructions into Vietnamese, and then generating answers using the pipeline dataset introduced in part \(A\).
2. If the translated instructions are not semantically accurate and in line with the original Vietnamese, the resulting data can significantly affect the quality and accuracy of the translation. An example of the problem and the disadvantage of using the 'GPT-3.5-turbo' API for translation are shown in Figure 11 where the inaccurate (i.e., wrong) translation from English to Vietnamese is demonstrated. The translation shown in Figure 11 was carried out using the Bing Chat function in the Microsoft Edge browser Version 117.0.2045.47 (Official build) (64-bit).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **arc\_vi** & **hellaswag\_vi** & **mmlu\_vi** & **truthfulq\_vi** & **Average** \\ \hline vinai/PhoGPT-7B5-Instruct & 26.41 & 40.55 & 26.24 & 45.82 & 34.76 \\ bkai-foundation-models/vietnamese-llama2- & 29.49 & 43.92 & 33.83 & 45.28 & 38.13 \\ \$p-40 GB & 27.01 & 48.33 & 26.50 & 42.77 & 36.15 \\
**Expert-B** & **33.68** & **49.10** & **35.57** & **51.4** & **42.44** \\ \hline \hline \end{tabular}
\end{table}
Table 5: VLSP benchmark score of aforementioned models.  
The inaccuracy is clear and is the result of a fundamental misunderstanding of the semantics in the question \"Product of 3 and 5\" where the word \"product\" relates to a mathematical function (i.e., \"multiplication\"). Frequently in translation software general English is handled well but scientific terms (not part of the language corpus) are incorrectly translated.
4. For this study, we believe the reason for the incorrect translation is because Phoenix has a conversation dataset consisting of dialogue between users and ChatGPT with the resulting questions being clearer and created by actual human users. Such inaccuracies are all too common in translation software.
# SARAC4N: Socially and Resource-Aware Caching in Clustered Content-Centric Networks
## 2 Literature Review
### Node-Based Caching  
Node-based caching strategies focus on placing content at specific nodes based on their role in the data request path. A notable approach is Leave Copy Down (LCD), where content is stored at a node just one hop downstream from the requester, aiming to reduce retrieval time for repeated requests. Similarly, Move Copy Down (MCD) [15] relocates the cached content closer to the requester after each access, effectively minimizing duplication across the network. Another approach, Cache-Aware Target Identification (CATT), enhances efficiency by selecting cache locations along the original request path based on node relevance and availability. While these strategies improve responsiveness and reduce content fetching delays, they often operate independently of real-time resource usage and ignore the social significance of nodes in the network. They also lack flexibility in dynamic environments where node loads and roles frequently change. SARAC4N addresses these shortcomings by selecting cache nodes not only based on path proximity but also through social centrality metrics and available computational resources, ensuring intelligent, adaptive caching decisions.  
Node-based caching methods like LCD, MCD, and CATT offer improved cache localization by moving content closer to the consumer, thereby lowering retrieval delays. However, these approaches often overlook the real-time load and resource constraints of the caching nodes, leading to potential bottlenecks and uneven resource usage. Moreover, their static movement rules are not adaptive to social interactions or communication frequency patterns. SARAC4N enhances node-based caching by combining network topology with social metrics and dynamic resource monitoring, enabling more intelligent and balanced cache placement decisions that adapt to both traffic and node health.
# Study of 5G-NR-MIMO Links in the Presence of an Interferer
## 3 Alamouti MIMO Link in the Presence of Different Interferer Techniques
### Partial Band Noise Jammer  
The PBN jammer [44] places noise jammer energy across a specific portion of the entire system's target bandwidth. For example, Figure 3 shows a 50% PBN jammer, i.e., 50% of the target bandwidth noise is added. PBN is a straightforward jammer technique in the modern communication world. PBN does not require any computational estimation or channel estimation between the target from the jammer's perspective and the receiver or any knowledge about the time synchronization of the target's preamble packet.  
The influence of the PBN jammer on the receiver in the space domain can be negligible if we use MIMO techniques such as Spatial Multiplexing (SM) decoding technique's, Minimum Variance Distortion-less Response (MVDR), and dynamic beamforming, especially if the channel state information is known to the UE side [27]. The selectivity of the PBN in the space domain depends on the PBN variance and bandwidth and on the signal noise power. In order to reduce the effect of the PBN jammer, advanced MIMO techniques at the space domain can be used [27; 28].
# Blade Shape Optimization and Analysis of a Propeller for VTOL Based on an Inverse Method
## 3 Optimization Problem  
### Chord-Length Distribution Parameterization  
An appropriate parametric method for the chord-length distribution can decrease the number of infeasible designs during the generation of the design variables. General parametric methods for the chord-length distribution of a propeller include direct interpolation [36], polynomial curve fitting [15] and Bezier curve fitting [23]. A new parametric method using a piecewise quadratic polynomial was put forward, as shown in Figure 12. P was the tangency point of two quadratic polynomials AP and BP as well as one straight line CD. Meanwhile, the straight line CD was parallel to the straight line AB. The spanwise location of the P point was \(r_{mid}\) and the chord-length at the P point was calculated by \(c_{mid}=(p+1)c_{mid}^{linear}\) with a given parameter \(p\). The virtual linear chord-length \(c_{mid}^{linear}\) was expressed as:  
\[c_{mid}^{linear}=\frac{c_{tip}-c_{root}}{R-R_{hub}}(r_{mid}-R_{hub})+c_{root} \tag{9}\]  
where \(R_{hub}\) is the radius of the hub and \(c_{root}\) and \(c_{tip}\) are the length of the chord at the root and tip of the propeller, respectively. A \(p>0\) ensured that the chord-length distribution had a non-convex form consistent with most practical propellers in reality and eliminated large numbers of infeasible designs. The revolutionary speed during the cruising stage \(\omega_{4}\) should be given to optimize the twist angle distribution using the inverse design method mentioned above. Thus, the design variables of the VTOL propeller optimization framework were denoted as \(\mathbf{x}=\left(c_{root},c_{tip},r_{mid},p,\omega_{4}\right)\).
## 2 Materials and Methods
### Data-Processing Method  
#### 2.4.1 Difference Processing  
The TIR change caused by stress is an energy conversion process in which part of the mechanical energy is converted to infrared radiation. The average infrared radiant temperature (AIRT) can reflect the overall TIR intensity of the loaded sample surface. To extract the changes in TIR intensity related to the stress accurately and to reduce the influence of the environmental and instrumental noise, the difference method was performed firstly [38; 47; 49]. The thermal infrared image at the initial moment was selected as the reference image, and the entire thermal images during the loading process were subtracted from the reference image. Then, the AIRT difference (\(\Delta\)AIRT) at each moment could be calculated during the loading process.
## 3 Results and Discussions
### Dielectric Constant and Permeability of Composites  
The electromagnetic parameters of the nickel-plated carbon fiber in the X-band are shown in Figure 5, where Figure 5a shows the complex permittivity of the nickel-plated carbon fiber, Figure 5b shows the complex permeability, Figure 5c shows the tangent of the dielectric loss angle, and Figure 5d shows the tangent of the magnetic loss angle. EM(R) and EM(I) are the real and imaginary parts of the complex dielectric constant, and MU(R) and MU(I) are the real and imaginary parts of the complex permeability, respectively.  
Figure 4: The morphology of fibers pre- and post-electroless nickel plating: (**a**) carbon fiber; (**b**) nickel-plated carbon fiber; (**c**) High-Magnification SEM Image of Compact Nickel Particles.  
Figure 5: Electromagnetic parameters of nickel-plated carbon fibers: (**a**) dielectric constant; (**b**) permeability; (**c**) tangent of the dielectric loss angle; (**d**) tangent of the magnetic loss angle.
# Variables Affecting Peri-Implant Radiographic Bone Loss-8-23 Years Follow-Up
## 2 Materials and Methods
### Radiographic Data  
* Mesial and distal bone level were measured at T0 and T_f_. (Figures 1a and 1b, respectively). Radiographs were analyzed using a computer program (VixWin ProVrs. 1.5e Gendex Dental Systems. KAVO DENTAL 200 S Kraemer Blvd, Building E2 Brea, CA 92821, USA). Analogue radiographs were digitalized with a flatbed scanner (Epson Expression 1680 pro; Seiko Epson Corp.) with 600 dpi resolution and eight-bit grey values. The image files were stored as TIFF files and analyzed using the computer program VixWin on a 24' flat screen (Philips 243V5Q, 1920 \(\times\) 1080; Philips) in a particular room under exclusion of natural or artificial light except the screen. After calibration using the implant's diameter, mesial and distal distances between implant shoulder and bone crest were measured using the ruler measurement tool. The degree of accuracy was 0.05 mm. A single trained and calibrated investigator (MS) performed the evaluation of all radiographs. Replicate measurements were done on 23 implants.
* Radiographic bone level change (the differences in radiographic bone level between T_f_ and T0 were calculated separately for mesial and distal of each implant. The larger of them was chosen for further calculation of mean implant bone loss).
* For the construction of a model of factors associated with peri-implant radiographic bone loss a new variable, \"excess bone loss\" was defined, consisting of peri-implant bone loss over the first year, of 1.5 mm, and the subsequent loss per year of 0.1 mm. The threshold for each implant was therefore calculated as 1.5 mm + 0.1
* T0) years. Implants were categorized as 1 if the threshold was exceeded and 0 otherwise. With this variable, logistic regression was performed.
# Numerical Simulation of Water Migration during Soil Freezing and Its Resulting Characterization
## 4 Results and Analysis
Here, sample (**a**) weight water content was 56%, temperature of cold end was \(-10\)\({}^{\circ}\)C; (**b**) weight water content was 50%, temperature of cold end was \(-5\)\({}^{\circ}\)C; (**c**) weight water content was 50%, temperature of cold end was \(-10\)\({}^{\circ}\)C.  
Figure 5: Schematic diagram of the pore pressure after freezing of the kaolin clay column, with the cold end temperature at \(-5\)\({}^{\circ}\)C, initial weight water content of 50%. (**a**) Within 5 h of freezing, no ice lenses were generated; (**b**) after 20 h of freezing, ice lenses were generated. maintained at \(-7\)\({}^{\circ}\)C, and during the thawing phase, it was raised to 3 \({}^{\circ}\)C. Pore water pressure was continuously measured throughout the experiment. In this study, the same boundary conditions were applied to the model. To make the model solvable, we smoothed the boundary conditions at the transition phase of the freeze-thaw cycle, ensuring a gradual change when the temperature abruptly shifted from \(-7\)\({}^{\circ}\)C to \(+3\)\({}^{\circ}\)C. The transition period was set to approximately half an hour. Based on the experimental and modeling results, we drew the following conclusions. The pore water pressure sensor may not function optimally in transmitting the pressure in the frozen soil, which could account for the irregular fluctuations observed in the experimental results during the freezing period. In contrast, the outcomes from the numerical simulation tended to be more idealized. Additionally, the temperature boundary conditions in the model required transitional processing during the abrupt phase changes of the freeze-thaw cycle to render the model solvable. As a result, the outcomes will differ in some ways. However, the overall peak value of the pore water pressure simulation results is in agreement with the experimental results' trend. During the soil water freezing process, the phase change of water to ice and the formation of cold suction (negative pore water pressure) at the ice lens interface resulted in the movement of water from the unfrozen zone towards the vicinity of the freezing front. This movement increased the liquid water content during the thawing process, consequently elevating the pore water pressure.
# A Communication-Efficient Secure Routing Protocol for IoT Networks +
## 3 System Model  
We first describe the network configuration, security requirements, and attacker model. This study considers a situation in which the source node S constructs a route to the destination node D, as shown in Figure 1. Assuming \(n\) intermediate nodes between 5 and D, each intermediate node can be denoted by \(\mathsf{N}_{i}(i=1,2,...,n)\). We assume that the nodes use digital signatures based on an ID-based signature. To this end, a server issues a secret key and unique ID to generate a digital signature for each node in advance. An ID-based signature enables the nodes to sign packets based on their secret keys and verify them based on the signers' IDs in the packets. This feature removes the requirement for any centralized server, including public key infrastructure (PKI), unlike public key-based methods [20]. Subsequently, the nodes construct a route in a fully distributed manner. Furthermore, it is assumed that all nodes employ a tamper-resistant global positioning system (GPS) to append their location information to the routing packets.  
Based on the security requirements in [10; 26], we set the following requirements in our proposed method:  
* Attackers cannot spoof other nodes;
* Attackers cannot forge route information;
* Only authorized nodes can join the network, preventing packet injection attacks [27];
* The routing protocol can mitigate the effects of packet dropping and delaying attacks;
* The node that generates the RREP on behalf of the destination node can prove to a third party that the route from this node to the destination correctly exists.  
Note that unlike the other methods, the proposed method is characterized by an intermediate node that can generate the RREP. Thus, we set a new fifth condition to guarantee the validity of the intermediate node. The proposed protocol is designed based on the aforementioned requirements.
## 4 Experimental Results and Analysis
### Simulation Results and Analysis
#### 4.1.2 ARC Controller Parameter Tuning  
Based on empirical adaptive law, the adaptive gain matrix is set as \(\Gamma=\) diag(10, 1, 10\({}^{-13}\)), and the parameter ranges for the ARC controller are defined as follows: k1 \(\in\) [80, 300], k2 \(\in\) [2000, 145,000], and k3 \(\in\) [10, 1000]. The sum of squared errors for the best parameters in each iteration of the PSO algorithm is shown in Figure 8.  
The error converges from the fourth iteration and remains stable thereafter, with a minimum sum of squared errors of 2.28 obtained at a sampling interval of 0.01 s. The optimized parameters are as follows: k1 = 210.95, k2 = 141,776.61, and k3 = 881.57. Other parameter combinations with near-minimum objective values and their corresponding cost functions are shown in Table 2.  
Figure 7: Performance comparison of optimized PID controller: (**a**) tracking performance without wind disturbance; (**b**) tracking error without wind disturbance; (**c**) tracking performance with wind disturbance; and (**d**) tracking error with wind disturbance.  
The performance comparison of the optimized ARC controller under conditions with and without wind disturbance is shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**k1** & **k2** & **k3** & **Cost** \\ \hline
289.41 & 144,353.04 & 744.41 & 2.29 \\
170.85 & 141,003.76 & 676.88 & 2.30 \\
252.89 & 140,169.72 & 10 & 2.37 \\
218.52 & 144,235.10 & 1000 & 2.43 \\
141.46 & 145,000 & 1000 & 2.47 \\
148.04 & 143,786.98 & 847.09 & 2.53 \\
300 & 143,518.45 & 1000 & 2.53 \\
300 & 145,000 & 10 & 2.55 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Near-optimal ARC parameter combinations and their cost functions.  
Figure 8: The convergence process of squared errors during PSO-based ARC parameter optimization.  
Figure 9: Performance comparison of optimized ARC controller: (**a**) tracking performance without wind disturbance; (**b**) tracking error without wind disturbance; (**c**) tracking performance with wind disturbance; and (**d**) tracking error with wind disturbance.  
The comparative results in Figure 8 demonstrate the tracking performance of the ARC controller under different conditions. In the disturbance-free case (Figure 9a,b), the ARC controller achieves precise tracking with a maximum error of 0.1\({}^{\circ}\), exhibiting excellent steady-state performance. When subjected to wind disturbance (Figure 9c,d), the maximum tracking error reaches approximately 0.12\({}^{\circ}\) at around 8 s due to the sudden onset of disturbance, and then decreases. This performance can be attributed to the adaptive mechanism of the ARC, which estimates and compensates for system uncertainties through real-time parameter adjustment. The adaptive law effectively handles both the inherent nonlinearities of the pump-controlled system and the external wind disturbances, demonstrating robust tracking capability under varying operating conditions.
###### Abstract  
_Background and Objectives_: Dovitinib (DVB) is a pan-tyrosine kinase inhibitor (TKI) that can be administered orally. In September 2023, the FDA granted Oncoheroes approval to proceed with an Investigational New Drug (IND) application for dovitinib. This application is intended for the treatment of relapsed or advanced juvenile solid tumors, namely, osteosarcoma. _Materials and Methods_: The target of the present study was to develop a rapid, green, accurate, and sensitive UHPLC-MS/MS method for measuring DVB levels in human liver microsomes (HLMs). The validations of the HLMs were performed via the established UHPLC-MS/MS approach, as stated in the US FDA reported guidelines for the standards of bioanalytical method validation protocol. The StarDrop in silico software package (version 6.6), which involves the DEEK and WhichP450 in silico modules, was used to check the DVB structure for hazardous alerts and metabolic instability. The DVB and encorafenib (EFB), internal standard, and chromatographic peaks were successfully separated using a reversed phase column (an Eclipse Plus Agilent C8 column) and an isocratic mobile phase. The production of DVB parent ions was accomplished by utilizing the positive ionization mode of an ESI source. The identification and measurement of DVB daughter ions were conducted using the MRM mode. _Results_: The inter-day accuracy and precision exhibited a spectrum of values in the range of \(-0.56\%\) to \(9.33\%\), while the intra-day accuracy and precision showcased a range of scores between \(0.28\%\) and \(7.28\%\). The DVB calibration curve showed a linear relationship that ranged from 1 to \(3000\) ng/mL. The usefulness of the currently validated UHPLC-MS/MS method was approved by the lower limit of quantification (LLOQ) of \(1\) ng/mL. The AGREE findings demonstrate that the UHPLC-MS/MS method had a noteworthy degree of ecological greenness. The in vitro half-life (t\({}_{1/2}\)) and intrinsic clearance (Cl\({}_{\text{int}}\)) of DVB were calculated to be \(15.48\) min and \(52.39\) mL/min/kg, respectively, which aligned with the findings from the WhichP450 software (version 6.6). _Conclusions_: Via the usage of in silico software, it has been observed that making small changes to the structure of the aryl piperazine ring and quinolinone moieties, or replacing these groups in the drug design process, shows potential for enhancing the metabolic safety and stability of newly developed derivatives compared to DVB.  
Dovitinib; DEEK software; WhichP450 software; pediatric osteosarcoma; ADME profile; UHPLC-MS/MS +
Footnote †: journal: medicine
## 4 Proposed System
### Trajectory Sharing on the Cloud  
Let \(T_{d}=\{loc_{1}\rightarrow...\to loc_{t}\}\) be the driving trajectory of a vehicle \(V_{d}\) consisting of some specific locations such as main roads or intersections on \(V_{d}\)'s preference driving routes. When \(V_{d}\) participates in VANETs, \(V_{d}\) will expects to receive service messages provided by the socialspot RSUs (\(SR_{d}\)) of \(V_{d}\)'s interest through some contact point RSUs deployed on \(V_{d}\)'s driving trajectory \(T_{d}\). To securely share \(V_{d}\)'s trajectory data and pseudonyms used for message delivery in VANETs with the socialspot RSUs in \(SR_{d}\) through the cloud storage, \(V_{d}\) uploads encrypted trajectory data and pseudonyms to SM as follows:  
1. Choose a pseudonym \(pid_{di}\in PID_{d}\) to be used for connecting to a contact point \(RSU_{i}\).
2. Compose a pseudonym-location pair message \(trj_{d}=\{(pid_{di},loc_{i})|1\leq i\leq t\}\).
3. Generate a ciphertext \(C\) for \(trj_{d}\) as \(C\leftarrow\)_clEnc(trj\(,pid_{d0},\)\(pk_{V_{d}}\))_ under \(V_{d}\)'s own public key.
4. Upload \(TM_{d}=\{pid_{d0},C,ts,S_{d}\}\) to SM, where \(S_{d}\gets idSig(vsk_{d0},\)\(pid_{d0}|C|ts)\) is \(V_{d}\)'s signature under the id-based secret key \(vsk_{d0}\) of \(pid_{d0}\).  
Once \(V_{d}\) uploads its trajectory sharing message \(TM_{d}\) to the cloud storage, SM controls to transform and provide \(V_{d}\)'s encrypted trajectory to the sRSUs in \(SR_{d}\) specified by \(V_{d}\) as follows:  
1. Parse \(TM_{d}\) as \(\{pid_{d0},C,ts,S_{d}\}\) and verify the signature \(S_{d}\) as \(idV\)_rf_(\(pid_{d0},\)\(pid_{d0}|C|ts,\)\(S_{d}\)) under the given \(pid_{d0}\).  
Retrieve \(RSM_{d}=\{pid_{d0},pk_{V_{d}},SR_{d},RK_{d}\}\) for the given \(pid_{d0}\) from SM's storage.
3. For each \(sRSU_{j}\in SR_{d}\), extract \(rk_{V_{d}\to sRSU_{j}}\in RK_{d}\) and transform the ciphertext \(C\) to \(C_{j}^{\prime}\) as \(C_{j}^{\prime}\gets clReEnc\) (\(pid_{d0}\), \(pk_{V_{d}}\), \(c,rk_{V_{d}\to sRSU_{j}}\)).
4. Store \(\{C_{j}^{\prime},ts\}\) to \(sRSU_{j}\)'s directory on the cloud.  
While SM maintains vehicles encrypted trajectory information, each socialspot RSU, \(sRSU_{j}\), periodically access its directory to get the updated trajectory of \(V_{d}\) as follows:  
1. Downloads \(\{C_{j}^{\prime},ts\}\) from its directory on the cloud.
2. Decrypt \(C_{j}^{\prime}\) to get \(trj_{d}\) as \(trj_{d}=\{(pid_{di},loc_{i})|1\leq i\leq t\}\gets clReDec(sk_{sRSU_{j}},C_{j }^{\prime})\).
3. Add \((pid_{di},loc_{i})\) pairs to the vehicle list \(VList\).