esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper proposes a self-insulating joint design based on the cable-driven parallel-series (CDPS) mechanism and electrical insulation analysis. The design provides the motions, mechanic support, and electrical insulation for robotic arms in live-line operation, which can maintain the equipment without manual intervention and power interruption. This CDPS mechanism can integrate four degrees of freedom (DOFs) motion in one joint, while the traditional series joint can only realize one DOF independently. The cable forces in the CDPS are calculated by the inverse kinematics to ensure the safe and flexible operation of the mechanism. The self-insulating joint has certain advantages over other designs because the electrical insulation is integrated into the joint instead of the traditional extra insulation layer. This integration reduces the weight of the arm mechanic structure. In addition, the structural complexity and weight are further reduced by separating the actuators and motors from the joint by using CDPS. Electric field distribution near the joint is calculated by the charge simulation method to analyze the insulation performance under the voltage of 35 kV. The cable forces and electric field distribution of the mechanism are measured to validate the simulation models. The inverse kinematics and insulation models of the self-insulating joint can provide detailed information for the mechanic and insulation design of the robotic arms.  
cable-driven parallel-series; electric field calculation; inverse kinematics model; self-insulating joint +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussion
### Simulated Vehicle Experiments
#### 3.1.4 Simulated Vehicle Experimental Observations and Conclusions  
Clearly, the sand surface provided the largest potential for transfer based on the sand having the largest %R and %Res during the NSC experiments. Overall, in most cases, the stabilization technologies provided improved stabilization compared to the specific NSC results, with CaCl\({}_{2}\) and MVP-Fx generating lower average %R values and %T values in most cases. Both CaCl\({}_{2}\) and MVP-Fx reduced the %R by at least a factor of 2 for all three surfaces  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Surfaces** & **Stab. Tech.** & **CaCl\({}_{2}\)** & **MVP-Fx** & **Soil\({}_{2}\)O** \\ \cline{2-5}  & Control & 0.0036 & \textless{}0.0001 & 0.9709 \\ \cline{2-5} Asphalt & CaCl\({}_{2}\) & & 0.0267 & 0.2282 \\ \cline{2-5}  & MVP-Fx & & & 0.0907 \\ \hline \multirow{3}{*}{Concrete} & Control & 0.0089 & 0.1401 & 0.0743 \\ \cline{2-5}  & CaCl\({}_{2}\) & & 0.0388 & 0.0404 \\ \cline{2-5}  & MVP-Fx & & & 0.7378 \\ \hline \multirow{3}{*}{Sand} & Control & 0.0002 & \textless{}0.0001 & 0.0056 \\ \cline{2-5}  & CaCl\({}_{2}\) & & 0.1503 & 0.0601 \\ \cline{1-1} \cline{2-5}  & MVP-Fx & & & 0.0169 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical results (_p_-values) for simulated vehicle experiments percent residual. Gray shading indicates a significant difference (_p_ < 0.05).  
Figure 12: Percent Residual Activity of Sr-85 Contaminated Particles for Vehicle Experiments.  
tested, while Soil2O had a milder impact, demonstrating a factor of 0.4. However, there were a few instances in which one or more of the three stabilization technologies did not demonstrate a statistically significant improvement at the 95% confidence level. It is a limitation imposed by the radiological containment enclosure that vehicle speeds and vehicles representative of what would be used on a response location could not be studied. Airflow caused by the movement of vehicles and by the exhaust and fans coming from within the vehicle is extremely difficult to be simulated within radiological containment.  
For data interpretation purposes, the %R and %T results are more reliable than the %Res results because of the nature of the measurement. Specifically, the %R and %T measurements were made by locating the detector probe in pre-marked positions on paves with the same geometry with respect to the detector probe. For the %Res measurements, the detector probe was held near the tire, which had a curved contour both vertically and horizontally. In addition, the tires were measured at the center location of the expected tire contamination when the tire was oriented perpendicularly to the floor. Therefore, occasionally some particles, estimated < 10% in most cases, were observed to have fallen off the tire before the measurement was made.
# A Digitalization Algorithm Based on the Voltage Waveform of the Multifunction Vehicle Bus
## 1 Introduction
Thus, it is important to acquire a proper sampling rate for applications. This research is based on MVB physical-layer voltage waveform data, and an MVB voltage waveform digitization algorithm was written to achieve frame data extraction and waveform feature extraction of MVB communication data, simplifying the effective information. By setting different sampling rates, a proper sampling rate for applications has been determined in experiments and the algorithm has been proved to be effective.
## 4 Enhancement of Energy Recovery (Biogas/Methane Yield) from the Anaerobic Digestion of Rice Straw
### Temperature
The biogas/methane yields at both temperatures in these studies [25; 42; 54; 57] are shown in Figure 4. About 13-100% more methane is yielded at thermophilic as compared to mesophilic conditions in the four experiments, while in one AD test, 62% more methane was produced at mesophilic conditions. The effect of increasing temperature on the CH\({}_{4}\) yield from co-digestion of dairy manure (DM), chicken manure (CM), and RS has been investigated [26], and the results are shown in Figure 5. RS was co-digested with DM, CM, and also in a mixture with both manures in batch digesters at 20, 30, 40, 50, and 60 \({}^{\circ}\)C. CH\({}_{4}\) yield continuously increased by increasing the temperature. CH\({}_{4}\) yield in three mixtures was on average 2.5 times higher at 40 than at 20 \({}^{\circ}\)C, while only about 1.2 times higher at 60 than at 40 \({}^{\circ}\)C. No significant difference was observed in CH\({}_{4}\) yields between 50 and 60 \({}^{\circ}\)C [26]. The results of these studies demonstrate that psychrophilic AD of RS has the lowest CH\({}_{4}\) yield compared to mesophilic and thermophilic conditions. It illustrates the critical role of temperature in AD digester concerning energy recovery. Compared to mesophilic conditions, the AD of RS under thermophilic conditions yielded up to 100% more biogas yields. Moreover, the quality of biogas in terms of CH\({}_{4}\) content is also higher at higher temperatures. Thus, optimal energy recovery from the AD of RS is attained under thermophilic conditions. Apart from the advantages of higher energy recovery, there are some disadvantages to the thermophilic AD process. For instance, variation in temperature should not exceed 0.6 \({}^{\circ}\)C/day to maintain a stable digestion process [18]. The mesophilic process is more robust and less sensitive to environmental changes than the thermophilic process [58]. Figure 4: Effect of temperature on biogas/methane yield [25; 42; 54; 57].
## 4 Implementation
### Exploration Algorithms
#### 4.1.7 Task Allocation based on Utility Function Approach  
Two methods are used as input for obtaining the cost and gain without requiring high computational resources due to neural networks use to model the utility function. The Euclidian distance from each agent to the specific cell represents the cost. As a new approach, the gain is obtained with the correlation level with the image of the environment in a specific cell by applying the correlation filter described above. High correlation levels point out positions in the environment where the agent can obtain maximum coverage.  
The utility function with extensive computation is used to train the neural network, obtaining good performance with feed-forward back-propagation network architecture, three hidden layers, and sigmoid tangential transfer functions. On the other hand, it is proposed to optimize the running of the utility function exploiting the parallel computing nature of genetic algorithms (GAs).  
The implemented GAs have:  
1. A genetic representation of the solution domain: populations or chromosomes are the grid cells not visited by the agents.
2. The fitness function to evaluate the solution domain is the utility function with extensive computation.
3. A standard representation of the solution is an array of bits with the size of the number of grid cells in the environment. The position set to 1, is the chosen cell. The main property that makes these genetic representations practical is that their components are easily aligned due to their fixed size, simplifying crossover operations.
4. The fitness function is defined over the genetic representation and measures the quality of the represented solution. The fitness function is always problem dependent. Once the genetic representation and the fitness function are defined, a GA proceeds to initialize a population of solutions (usually randomly); and then, improves it through repetitive application of the selection, crossover, and mutation operators.  
Figure 8: Example case and environment correlation levels and decision for correlations filter exploration.  
Figure 12 shows how the modeled function with neural networks does not represent exactly the utility function with extensive computation optimized with GA, but the decision obtained is the same. Figure 13 shows the need to model and optimize the utility function given the response times of each algorithm. To illustrate this, 100 tests are performed of the decision in different environments for each algorithm obtaining the response time of each decision.  
Figure 11: Correlations levels for neural networks and the proportional gain in (2) for GAs.  
Figure 12: Utility values for each approach, indicating the chosen cell with the star.
## 2 Materials and Methods  
### Experimental Data  
Original data deriving from previous experiments [49] were used to quantify the interaction forces resulting from the puncture of in vivo peripheral nerves with W microneedles. As previously described [49; 50], microneedles were obtained from 20-mm-long W rods (A-M Systems) with diameters of \(\varnothing\)75 \(\upmu\)m, which were manually electro-sharpened keeping a \(\sim\)8 VAC potential between extremities. A large carbon counter electrode was used to etch tips in a 2N KNO\({}_{3}\) solution for 20-40 s through the anodic dissolution of tungsten oxides [62]. Moreover, the microneedle tips were visually inspected during etching and then cleaned in deionized water before experiments. W micromeedles were inserted through a surgical access in the upper forelimb to the unbar and median nerves (\(\sim\)\(\varnothing\)3-5 mm) of three adult Danish Landrace pigs. More specifically, the site of insertion was chosen \(\sim\)50 mm above the elbow, and the nerve was supported by a holed platform (hole diameter \(\sim\)4 mm). A servo-controlled motor (Maxon DC Motor 22-60-881, JVL Industri Elektronik A/S, DK) and a hydraulic micromanipulator (Narishige MMO-220) were used to control the microneedle position, and a constant velocity (2 mm/s) [63] ramp-hold-ramp profile was used to insert, hold, and retract the needle into/out of the nerve. The interaction force was quantified by using an in-line load cell (Model 31/1435-02, max load 0.1 kg, resolution 1.31 mV/g, Sensotec Inc., Columbus, OH, USA), and the signal was filtered with a third-order Butterworth lowpass filter (F3 dB = 12.5 Hz). Eleven experiments were analyzed, and the resulting interaction force-indentation depth curves were studied.
###### Abstract  
Background: The Sella Turcica is a critical structure from an orthodontic perspective, and its morphological characteristics can help in understanding craniofacial deformities. However, accurately extracting Sella Turcica shapes can be challenging due to the indistinct edges and indefinite boundaries present in X-ray images. This study aimed to develop and validate an automated Sella Morphology Network (SellaMorph-Net), a hybrid deep learning pipeline for segmenting Sella Turcica structure and extracting different morphological types; Methods: The SellaMorph-Net model proposed in this study combined attention-gating and recurrent residual convolutional layers (AGM and RrCL) to enhance the encoder's abilities. The model's output was then passed through a squeeze-and-excitation (SE) module to improve the network's robustness. In addition, dropout layers were added to the end of each convolution block to prevent overfitting. A Zero-shot classifier was employed for multiple classifications, and the model's output layer used five colour codes to represent different morphological types. The model's performance was evaluated using various quantitative metrics, such as global accuracy and mean pixel-wise Intersection over Union (IoU) and dice coefficient, based on qualitative results; Results: The study collected 1653 radiographic images and categorised them into four classes based on the predefined shape of Sella Turcica. These classes were further divided into three subgroups based on the complexity of the Sella structures. The proposed SellaMorphNet model achieved a global accuracy of 97.570, mean pixel-wise IoU scores of 0.7129, and a dice coefficient of 0.7324, significantly outperforming the VGG-19 and InceptionV3 models. The publicly available IEEE I8 BI 2015 challenge dataset and our dataset were used to evaluate the test performance between the state-of-the-art and proposed models. The proposed model provided higher testing results, which were 0.7314 IoU and 0.7768 dice for our dataset and 0.7864 IoU and 0.8313 dice for the challenge dataset; Conclusions: The proposed hybrid SellaMorph-Net model provides an accurate and reliable pipeline for detecting morphological types of Sella Turcica using full lateral cephalometric images. Future work will focus on further improvement and utilisation of the developed model as a prognostic tool for predicting anomalies related to Sella structures.  
Sella Turcica; lateral cephalogram; morphological types; Sella segmentation; SellaMorphNet +
Footnote †: journal: Applied Sciences
## 3 Grid-Connected PV/BESS System Description
### Storage System  
In this study, the high-energy-density and high-safety lithium ferrous phosphate battery (LFP) was used for economic benefit analysis. The battery cell used in this study has a rated specification of 3.2 V/280 Ah. The upper limit of the current can allow for continuous charging or discharging with 140 A, and the time would require to fully charge or discharge with 2 h, which is a 0.5 C-rate battery. The HOMER simulation software adjusts the current according to the power demand for charging or discharging, and the upper limit is 140 A, which is not a constant value. The battery module is made up of 14 battery cells in series with a rated power of 44.8 V/280 Ah, and the battery rack is made up of 17 battery modules in series with a rate of 761.6 V/280 Ah and a rated capacity of 213.2 kWh. The operating voltage of the battery cabinet is 666.4-856.8 V, the minimum state of charge (SOC) is set to 20%, and the round trip efficiency is set to 92%.  
The lifetime presents the number of discharge-charge cycles to meet specific performance criteria of the battery. The operating lifetime throughout of 967.765 MWh of the battery is affected by the parameters (the rate and depth of cycles) and by the environmental conditions (temperature and humidity). The warranty of a commercial battery is in the range of 5-10 years according to industrial experiences. The related standard such as IEC62620 can provide the parameters to lifetime according to the customer's declarations. The battery wear cost is the cost of cycling energy through the storage system calculated by Equation (13), and the storage life is limited by throughput. HOMER assumes the storage requires replacement when its total throughput equals its lifetime throughput, and the battery storage approaches its required replacement. HOMER calculates the storage wear cost using the following equation:  
\[C_{bw}=\frac{C_{rep,batt}}{N_{batt}\times Q_{lifetime}\times\sqrt{\eta_{rt}}}, \tag{13}\]  
where:  
\(C_{bw}=\) the battery wear cost ($);  
\(C_{rep,batt}=\) the replacement cost of the storage ($);  
\(N_{batt}=\) the number of batteries in the storage;  
\(Q_{lifetime}=\) the operating lifetime throughput of a single storage (kWh);  
\(\eta_{rt}=\) storage roundtrip efficiency (fractional).
## 3 The Proposed Optimization Hybrid Algorithm
### The Proposed TLTFWO Algorithm  
Trapping in the local optima and low accuracy are two major disadvantages of the original TFWO algorithm. The current article presents the TLTFWO algorithm to strengthen the weak points of the TFWO and facilitate information exchange among the population. Each of the members or individuals is constantly communicating with others in other populations. This helps advance the searching step within the search space and prevent trapping in the local optima. Thereby, the performance of the TFWO is remarkably improved, and the TLBO algorithm's ability to search the decision space is enhanced, as well as its exploitation potential.  
Equation (44) describes the modified and improved searching process in the hybrid TLTFWO algorithm. In this equation, \(Wh_{f}\) and \(Wh_{w}\) are used in the learning phase of the \(i\)th particle and the whirlpool to which \(i\)th particle belongs, i.e., \(Wh_{j}\), is adopted for the teaching phase. In this equation, it moves towards the global and local optima and between them based on different movement equations and different accelerations so that the searching range is somehow improved, and this leads to the algorithm effectivelyavoiding from trapping in the local optima. This new equation helps enhance local and global searching potential and thus reaches the final solution.  
\[\Delta X_{i}=\left(1+\left|\cos\left(\delta_{i}^{new}\right)-\sin \left(\delta_{i}^{new}\right)\right|\right) \tag{44}\] \[*(\cos(\delta_{i}^{new})*(Wh_{j}-T_{F}*X_{mean})-\sin(\delta_{i}^ {new})*(Wh_{f}-Wh_{w}))\]  
\[X_{i}^{new}=X_{i}+\Delta X_{i} \tag{45}\]  
**4. TLTFWO for Different OPF Problems**  
The IEEE 30-bus system is used to test TFWO, TLBO, and TLTFWO algorithms by examining eight cases of OPF problems. The maximum number of iterations is set at 600 in all the algorithms, the TFWO with \(Npop=45\) (population size) and \(N_{Wh}=3\) (number of whirlpools), TLBO with \(Npop=30\), and TLTFWO with \(Npop=45\) and \(N_{Wh}=3\). Power systems parameters are given in [56]. MATLAB 8.3 (R2014a) is adopted for simulations in a PC with a Corei7 CPU 3.0 GHz and 8.0 GB RAM configuration.
## 3 Methodology  
### Overview  
An overview of our proposed approach is shown in Figure 2. Following the tracking-by-detection paradigm, the online MOT can be formulated as an optimization problem. Trajectories in the set \(\mathbb{T}=\{track_{1},track_{2},\cdots,track_{k}\}\) are forked by the bounding boxes \(B_{k}=\{x,y,w,h\}\) of every object inthe past frames. At frame \(t\), the set of the objects' locations \(\mathbb{B}=\{b_{k_{1}},b_{k_{2}},\cdots,b_{k_{n}}\}\) of the previous frame \(t\)-\(1\) are associate with the \(\mathbb{D}^{t}\) detection candidates to maximize a final score:  
\[B_{k}=\operatorname{argmax}f_{k}\left(t,B_{k}^{t-1},D_{j}^{t},\mathcal{A}^{t},\mathcal{W}_{v_{d_{k}j}}^{t}\right). \tag{1}\]  
Here, \(\mathcal{A}^{t}\) denotes the final association results between the \(k\)-\(th\) object in \(B\) and the \(j\)-\(th\) detection in \(D\) at frame \(t\). \(\dashv_{k_{j}}^{t}=1\) indicates that the association of track \(k\) and detection \(j\) is valid, while \(\dashv_{k_{j}}^{t}=0\) otherwise. Furthermore, \(\dashv_{k_{j}}^{t}=0.5\) means that frame \(t\) is in a complex environment and the association result needs to be post-process by the delay processing model. \(\mathcal{W}\) is a set of parameters to model each object's state, including previous information, deformation, and interaction status, which is learned using the appearance and location information through previous frames. The function \(f(*)\) calculates the overall results of the tracking procedure for all the existing objects at frame \(t\), defined as follows:  
\[f(t,B_{k})=\sum_{k_{j}}\mathcal{A}_{s_{T}s_{O},k_{j}}^{t}\cdot SOT\left(t,b_{k }^{t-1},d_{j}^{t},\sqsupseteq_{v,d,k_{j}}^{t}\right). \tag{2}\]  
Therefore, solving the online MOT problem is done by solving Equation (1) from frame to frame.
## 5 Conclusions  
In this paper, we investigated the ability of LLM to handle NLR tasks for LRLs. Set up in a zero-shot environment, three LLMs (ChatGPT, Gemini, and PaLM) were exposed to three sets of questions with limited contamination, covering basic arithmetic, time, days and months of the year, and the numeral systems in the Yoruba language. The best-performing model of the three (based on the total number of questions correctly answered), ChatGPT, was able to generate correct answers, showing logical steps to attaining the answers in two of the three question sets (basic arithmetic, time, days, and months) but failed significantly in numeral systems. Our assessment of the models based on their reasoning completeness, language understanding, and numerical representation accuracy reveals the two fundamental errors that led to the sub-par results produced by the three models. The first is the misrepresentation of the Yoruba numerals in figures used for calculation by the models. This was the case in 29/50 questions (58%) for ChatGPT and in 27 of 50 questions (54%) for Gemini in the basic arithmetic question set. The second error was shown by the language understanding metric, where we assessed if the models understood the main gist of the questions. Again, ChatGPT misinterpreted 13 of 50 questions (26%), with Gemini only misinterpreting the questions on 4 occasions (8%) in the basic arithmetic question set.  
The tasks involving the accurate representation of Yoruba numerals presented the greatest challenge for the three models. The reasons for this included the difference between the vigesimal setup of the Yoruba numerals and the subtlety involved in deriving the various Yoruba numerals, unlike most LLMs, which have been trained using the decimal system. In the Yoruba numeral system, the numbers 20 (ogun), 200 (igba), 2000 (egba), and 20,000 (oke) are critical bases or reference points. They are multiplicative foundations from which other numerals are constructed [37]. Also, the insufficient or limited training on Yoruba-specific data or numeral-reasoning tasks contributes glaringly to the results achieved. ChatGPT and Gemini showed better exposure to training on the Yoruba dataset and could generalize better than PaLM.  
The performance of LLMs in tasks involving LRLs will be significantly improved if LLMs can understand LRLs more and can perform NLR in LRLs. To this end, there is a need for the creation of benchmarks to assess how well LLMs understand, can reason, and generalize in specific LRL domains such as culture, tradition, technology, education, etc., in LRLs. Also, there is a need for the curation of datasets in the Yoruba language that addresses the numeral and counting systems, as well as time, days, and months.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.