esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Assessment of Antioxidant Activity
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Samples Tested**} & \multicolumn{3}{c}{**Parameters**} \\ \cline{2-4}  & **DPPH, IC\({}_{50}\) (\(\upmu\)g/mL)** & **ABTS, IC\({}_{50}\) (\(\upmu\)g/L)** & \(\upbeta\)**-Carotene/Linoleic Acid (\% Inhibition Rate)** \\ \hline OEO & 93.12 \(\pm\) 0.03 & 27.63 \(\pm\) 0.01 & 82.36 \(\pm\) 0.14 \\ \hline BHA & 10.11 \(\pm\) 0.01 & 8.71 \(\pm\) 0.01 & 100 \\ \hline Ascorbic acid & 127.39 \(\pm\) 0.45 & 35.89 \(\pm\) 0.05 & n.d. \\ \hline n.d.—not determined. & & & \\ \end{tabular}
\end{table} 
Table 3: The antioxidant activity of essential oil extracted from _O. vulgare_ var. _aureum_. positive control used (BHA). In the case of the beta-carotene-linoleic acid bleaching assay, OEO was able to inhibit the linoleic acid oxidation (82.36 \(\pm\) 0.14%) effectively but lower than BHA (100%) (\(p\) > 0.05). No previous investigations were documented concerning OEO activity in _the_ beta-carotene/linoleic acid system to allow us to make direct comparisons.
# A Fine-Tuning Based Approach for Daily Activity Recognition between Smart Homes
## 3 The Proposed Approach
### Unify Sensor Space and Activity Space  
To begin with, we give definitions about sensor events and active samples.  
**Definition 1**.: \(s\) _= (d, \(t\), \(sn\), \(sv\), \(ac\)) is a sensor event. \(d\) is the date when \(sn\) was activated, \(t\) is the time when \(sn\) was triggered and \(sn\) is the name of sensor. \(sn\) is made up of an abbreviation for the type of sensor and a number, for example, \"M008\" in Table 1 represents the eighth motion sensor distributed in the smart home. Additionally, \(sv\) is the value of \(sn\) when \(sn\) was activated or not activated. Different types of sensors have different types of values, e.g., values or Boolean values. \(ac\) is the activity that the resident does when triggering the \(sn\). As shown in Table 1, \(s_{1}\) = (2012/7/20, 15/04/49. 047095, LS008, 38, Sleep_Out_Of_Bed). \(s_{1}\) represents the LS008 sensor event that was triggered by a resident during the \"Sleep_Out_Of_Bed\" activity on 20 July 2012, 4 min and 49 s after 3:00 p.m._  
**Definition 2**.: _When a person performs an activity in a smart home environment, the corresponding sensor event stream is triggered. In this paper, we only use the names of sensors to compose the activity samples instead of sensor events. Given \(n\) sensor names \(sn_{1}\), \(sn_{2}\),..., \(sn_{a}\) and a label \(y_{l}\), \(y_{l}\) belongs to the active label space. \(<\)\(sn_{1}\), \(sn_{2}\),..., \(sn_{a}\), \(y_{l}\)\(>\) is said to be an activity sample, if \(\forall 1\leq i\leq a\)-1, \(sn_{i+1}\) is always followed by \(sn_{i}\) in temporal order. An activity, \"Sleep_Out_Of_Bed\", is shown in Table 1; it can be represented as \(<\)M008, LS008,..., M008\(>\)._  
The layout of sensors in different smart home environments is different, but similar locations and types of sensors play similar roles. We mapped the sensors with the same location and type to unify the sensor space, and some of the sensor mappings are shown in Table 2. By using the renaming rule, sensors were renamed as \"Location_Type\". For example, door sensors \"D003\" in the bathroom of the source smart home and B were distributed across i and the bathroom in the target smart home, which were renamed S. Additionally, we merged the activities that live only time difference into one and unified the activity space. In addition, for each active sample, we used a fixed window to cut, and filled the samples with \"no\" if they were less than the window size. This was to ensure that the length of each input sample was the same and to facilitate network training. Then,  
Figure 1: Method flow.  
we transformed the sample into a numerical vector using Word2 Vec algorithm [34]. The entire data processing flow is shown in Figure 2.
## 5 Evaluation and Discussion
### Runtime Evaluation and Comparision
In the second iteration, the TKIFRFM has average runtime of 51.22 s, and the MTKPP has average runtime of 131.12 s, as shown in Figure 5b. In the third iteration, the TKIFRFM has average runtime of 10.64 s, and the MTKPP has average runtime of 121.51 s, as shown in Figure 5c. This is because as the size of a dataset increases, the existence of the patterns of highest support is similarly increased, and could be of variable length; therefore, the MTKPP performs multiple scans to find the support of multiple patterns, and the TKIFRFM does not perform multiple scans for support calculation. Figure 6a,b shows the consolidated results of all iterations for TKIFRFM and MTKPP on the mushroom dataset, where TKIFRFM is faster than MTKPP. The TKIFRFM completes the mining operation in maximum 3 s, whereas MTKPP consumes a maximum 150 s to complete the same mining operation. The results on the accidents dataset are shown in the Figure 7a,b, and the results on the chess dataset are shown in the Figure 8a,b, which signifies that the performance differences are quite clear for the TKIFRFM and MTKPP. The TKIFRFM is significantly faster in the topmost-K IFR itemset mining than MTKPP. This is due to that fact that MTKPP is simply using a breadth-first approach, similar to Apriori, and does not apply any pruning method in the candidate generation. Thus, all the candidates who have common prefixes are merged, and their support is intersected using TIDs sets. However, the TKIFRFM strategy of working with candidates is more effective. The TKIFRFM method first checks the support against the minimum support and the periodicity against the maximum periodicity. If a candidate has less support than the minimum support or periodicity greater than maximum periodicity, then the candidate is pruned from further consideration. Thus, the entire sub-tree with the candidate as root node is removed from the search space. Therefore, the TKIFRFM method uses a pruning method, which makes it working faster than the MTKPP method. Figure 5: _Cont_. Figure 5: (**a**) Performance trends of the first iteration on the chess dataset. (**b**) Performance trends of the second iteration on the chess dataset. (**c**) Performance trends of the third iteration on the chess dataset. Figure 6: _Cont._  
Figure 6: (**a**) Results of three iterations of TKIFRPM on the mushroom dataset. (**b**) Results of three iterations of MTKPP on the mushroom dataset. Figure 7: (**a**) Results of three iterations of TKIFRPM on the accident dataset. (**b**) Results of three iterations of MTKPP on the accident dataset. Conclusively, the performance of TKIFRPM is better than the MTKPP on both small and large datasets, such as mushroom, accident, and chess. It is also observed that on very small dataset, the MTKPP requires only a slight lead in runtime as compared to TKIFRPM. However, as the size of the dataset grows (as in the chess dataset), the performance of MTKPP deteriorates significantly.
## 2 Related Work
### DRL for MEC-Integrated O-RAN Resource Management  
Both radio and computing resources play a crucial role in the performance of task offloading. Radio resources influence the data rate and energy consumption during the transmission process, while computing resources limit the computing time and energy consumption of tasks that offload to the MEC server. Hence, ref. [36] proposes an architecture utilizing xApp, powered by machine learning algorithms that quickly identify the network traffic and intelligently allocate resources within O-RAN architecture. Such a structure addresses the defects of static resource allocation in O-RAN architecture by automatically adapting PRBs to traffic load and QoS requirements, leading to better performance and more economical satisfaction for end users. On the other hand, in ref. [37], the reinforcement learning (RL) algorithm is adopted to allocate and efficiently manage PRBs by utilizing modulation and coding schemes based on traffic flow and channel quality key performance indicator (KPI) requirements. The authors of [38] investigated a mMTC scenario in 5G to troubleshoot IIoT services that ensure efficient resource allocation methods to enhance dynamic and complex environments by conducting intelligent end-to-end self-organizing resource allocation IIoT with the asynchronous actor and critic-driven DRL algorithm.  
However, the above works did not consider the user offloading to the MEC server in computation by serving as energy efficient in resource utilization. In fact, this complex selection strategy cannot be ignored due to the IIoT devices transferring to the MEC server at the same time and different resource demands. Moreover, the problem of minimizing energy consumption and energy efficiency in O-RAN is notably distinct and challenging from traditional RAN. To overcome encounters related to energy efficiency for diverse IIoT resource types in the MEC server, we leveraged O-RAN-enabled MEC to ensure resource awareness and priority on demands.
## 1 Introduction  
The distal femur fracture is a rare but severe injury. Its reported incidence is 3-6% of all femur fractures [1]. The fractures have a bimodal distribution: low energy fractures at old age and high energy fractures in younger individuals. In high energy trauma the fractures are often complex and challenging [2]. About 10% of distal femur fractures are complicated with open wound injuries. The femur is the second most common bone loss site, which amounts to 22% of all traumatic skeletal losses [1; 3; 4].  
The current treatment protocol for these injuries includes multi-staged operations with wound debridement and temporary open fracture fixation, then surgical reconstruction including non-vascularized morselized bone graft, free vascularized bone transfer, strut allograft, and distraction osteogenesis [2; 3; 4]. Overall, traditional methods provide inferior immediate postoperative stability. This is because the chosen bone grafts are often ill-matched with the bone defect, as the bone wound site dimensions and geometry are always irregular and uneven. Subsequent complications are involved, including loss of reduction/fixation, refracture, malunion, nonunion, infection, and knee pain or stiffness.  
Titanium patient-specific implants (PSIs) can be manufactured using three-dimensional (3D) printing (also known as additive manufacturing) techniques. These techniques can be used to predesign and fabricate structures for orthopedic surgery rehabilitation. The customizability of 3D printing with regard to size and shape make it a potential alternative for complex posttraumatic limb reconstructions such as distal femur fractures with segmental large bone defects. Combining CT scanning image techniques, computer assisted design (CAD), and 3D printing, a fit PSI that matches the bone defects can be accurately designed. Clinical applications in mandible defects [5; 6], femur defects [7] and ankle defects [8] are being reported with encouraging results. However, titanium implants are stiffer than natural bones and cause significant stress-shielding effects, leading to bone resorption and eventual implant failure [9]. From a biomechanical point of view, patient-specific 3D implant designs for severe distal femur defect reconstruction must achieve original geometry restoration and macro-comprehensive structures with light optimal structures that can withstand physiological loads.  
This study proposes a novel titanium 3D printing PSI for a distal femur fracture with segmental large bone defect design. The designed implant must fit into the irregularity of the defect area, creating maximum implant-bone contact interface to provide excellent immediate postoperative stability. The implant is optimized to be lightweight with a surface lattice for morselized bone graft protection. This implant is being tested for its biomechanical performance using nonlinear finite element (FE) analysis.
# MAOA: A Swift and Effective Optimization Algorithm for Linear Antenna Array Design
###### Abstract  
This paper presents the modified arithmetic optimization algorithm (MAOA), a swift and effective optimization algorithm specifically designed for electromagnetic applications. Its primary advantage is its ability to avoid local minima by striking a balance between global exploration and local exploitation searches. This equilibrium is maintained through three key improvements: an enhanced initialization process, a distinctive guidance mechanism for steering searches, and an additional learning phase to refine newly found solutions. This process innovation significantly boosts MAOA's performance in addressing both constrained and unconstrained optimization challenges. In this study, MAOA is applied to optimize the spacing and current amplitude of linear antenna array (LAA) elements, with the goal of minimizing peak side lobe level (PSLL), close-in side lobe level (CSLL), and overall side lobe level (SLL), both with and without constraints on first null beamwidth (FNBW), as well as null positioning with SLL minimization. Ten designs, comprising 10 and 20 antenna elements of LAA and one 14-element circular antenna array (CAA), showcase MAOA's proficiency in antenna array pattern synthesis. Optimizing element positions results in a PSLL of \(-\)21.28 dB, a CSLL of \(-\)34.50 dB, and a null depth of \(-\)89.00 dB, while optimizing current amplitude achieves a PSLL of \(-\)24.32 dB, a CSLL of \(-\)29.73 dB, and a null depth of \(-\)77.60 dB across various antenna designs. Simulation results reveal that MAOA significantly surpasses traditional uniform linear arrays (ULA) and established optimization techniques. Its superiority is further confirmed through a Wilcoxon rank-sum and Friedman test.  
pattern synthesis; arithmetic optimization algorithm; optimization; LAA; Wilcoxon; friedman 2025  
6
## 6 Application Areas
### Application Area 2: Food Supply Chain  
BCT holds the potential for diverse applications in the cultivation and preparation of food. By rendering the supply chain transparent and traceable from the farm to the end consumer, BCT ensures a heightened level of accountability [42]. The technology is adept at verifying the authenticity of crops and commodities, while smart contracts bring automation to various processes within the supply chain. Beyond supply chain management, financial transactions can also benefit from the implementation of BCT. The utilization of BCT in the food industry contributes to improved supply chain management, a reduction in fraud, and a decrease in food waste, ultimately enhancing the safety and security of food [43]. The transparency, efficiency, and safety that BCT can bring to food and agriculture production have the potential to boost industrial productivity significantly.
# Research Progress in the Early Warning of Chicken Diseases by Monitoring Clinical Symptoms
## 2 Early Diseases Detection through Physiological Characteristics
### Abnormal Body Temperature
According to the physiological characteristics that the body temperature of dead chickens will decrease, [PERSON] et al. [63] successfully detected dead chickens in poultry production by high-resolution thermal imaging technology, which improved the welfare level of the remaining chickens and reduced the risk of disease transmission. At present, research related to disease detection based on abnormal body temperature in poultry mostly focuses on leg diseases and avian influenza. Thermal infrared temperature is easily affected by environmental factors and chicken overlap. Thermal infrared temperature is easily affected by environmental factors and chicken overlap. How to obtain high-quality thermal infrared images stably under uncontrollable environmental conditions and how to improve the segmentation accuracy of chicken head and leg areas should receive more attention. In addition, the relationship between poultry body temperature and growth performance and behavior should be paid attention to in the future. In addition, wearable temperature sensors will be more portable and lightweight while pursuing better stability and longer transmission distance.
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 5 Challenges in Designing a Flexible Antenna  
This session depicts the various challenges in designing the flexible antenna for various applications with respect to the substrate selection, effect of the human body on the antenna performance, deterioration in the gain, and the miniaturization of the antenna.
# Recent Advances in Optical Detection of Aminoglycosodies
## 6 Multiplex Detection of Antibiotics
### Based on Spatial Resolution  
[PERSON]'s group developed a simultaneous detection method for kanamycin and streptomycin by using CL imaging. Anti-kanamycin antibodies and anti-streptomycin antibodies were spotted in different areas of a nitrocellulose membrane. A competitive-type CL imaging immunoassay was developed for the simultaneous detection of kanamycincin and streptomycin by using the microarray on the nitrocellulose membrane. By using HRP as the label, CL images were recorded by a cooled low-light CCD with a high resolution. For qualitative analysis, the LOD was 0.86 nM for kanamycin and 5.4 nM for streptomycin [93]. In addition, lateral flow assay strips with two test regions were constructed for the simultaneous detection of neomycin and quinolone antibiotics with LODs down to 0.6 pM and 1.5 pM by using SERS [94]. A microchip electrophoresis array combined with aptamer recognition and polymerase chain reaction (PCR) amplification were employed for the simultaneous detection of kanamycincin and chloramphenicol [95].
# Smart Internet of Things Power Meter for Industrial and Domestic Applications
## 3 Proposed Solution
### Hardware Architecture
#### 3.1.1 Microcontroller  
Given the technical requirements of the power meter, it was necessary to select a microcontroller equipped with at least the following components:  
* Two SPI interfaces: one for the microSD card and one for the ADC communication (for parallelizing the communication tasks);
* One UART interface for the debug/programming port or the RS485 transceiver;
* Wi-Fi interface;
* Ten GPIOs for buttons, LEDs, battery charger, and other modules' status or control signals;
* A 3.3 to 5 V supply voltage.  
From the software point of view, a microcontroller with a high-frequency processor and a large amount of memory is needed. For accurate voltage and current measurements, a large number of signal samples are acquired using the ADC and stored in the microcontroller's memory. Our goal is to analyze up to the 40 th harmonic of the signal and calculate the total harmonic distortion (THD), which implies using a Fast Fourier Transformation (FFT) algorithm. Typically, these algorithms use large arrays of data and require significant computational power. The microcontroller needs to handle the data acquisition process, analyze the signal, calculate the required signal parameters, and serve the relevant values to one or multiple clients through the Wi-Fi and RS485 interfaces.  
To select the appropriate microcontroller according to our requirements, we tested several current and largely available models from Espressif Systems and Raspberry Pi, such as ESP32 and Raspberry Pi. On each model, we conducted several tests with different FFT algorithm libraries on the same input data set and measured the time required to complete the signal analysis. Then, we selected the model that executed the algorithm in the shortest time possible while also adhering to the hardware and software criteria imposed by us, as mentioned earlier. The detailed process and results of the benchmark are the subject of a scientific paper that is currently under development.  
The selected microcontroller model was the Espressif Systems ESP32-WROVER-E-N16R8. It complies with the specifications imposed during the device design stage and is officially supported by PlatformIO, integrated in Visual Studio, Arduino IDE, and ESP IDF.
## 1 Introduction
When using MMS for wall-frame structures, an improved result has been attained by modifying the structure's stiffness model ([PERSON] et al. [33]). NTHA showed that MMS results in desired outputs for column forces and wall shears. However, it tended to be over-conservative for wall moments. [PERSON] [34] derived a stiffness matrix for frame members with a plastic hinge at one or both ends using the bilinear moment-curvature relationship for the hinges. To formulate the lateral stiffness of the structure representing the beam-sway mechanism, the post-yield stiffness and initial stiffness of the plastic hinge are needed, which are obtained after the design is completed unless assumed first. Currently, DDBD does not provide design procedures to account for torsional and higher-mode responses of plan-asymmetric RC frame buildings having a mass and stiffness irregularity along the height. In this paper, a seismic design procedure is proposed to incorporate the torsional and higher-mode responses in DDBD of plan-asymmetric buildings with the above-mentioned irregularities. Torsional responses are significantly reduced by assigning frames' lateral stiffness so that eccentricity between centers of supported mass and rigidity is eliminated. Once this task is performed, higher-mode responses are included by a design procedure developed by [PERSON] and [PERSON] [35] for planar frame buildings. In their paper, an equivalent lateral secant stiffness of a building responding in its nonlinear range is determined. Using the stiffness, elastic higher-mode responses can be obtained which are superposed to DDBD's inelastic first-mode responses. The case studies designed as such do not exceed the allowable performance levels when checked by NTHA, thus fulfilling the desired performance objectives. Ultimately, the goal of the proposed method is to render design verification by NTHA less mandatory though more case studies should be investigated.
## 3 Materials and Methods
### Instrumentation  
The experimental plant is interfaced with a computer so that the data acquisition system provides a real-time reading of the temperature, pressure, and velocity of the inlet and outlet air flow. The metering system used for this research is composed of the following elements:  
* Hot wire anemometers: An EE65 Series anemometer is located at the reactor outlet pipe, providing the velocity and the temperature of the inlet air flow. Moreover, a CTV100 anemometer is located at the inlet pipe of the internal combustion engine, providing the velocity and the temperature of the inlet air pipe flow;
* Pressure transducers: For the measurement of the pressure drops, a Series MS Magnesense Differential Pressure Transmitter has been used. The pressure transmitter is located at the outlet pipe of the reactor. The air/syngas mixture's pressure at the engine inlet pipe is evaluated by a Series 616K Differential Pressure Transmitter, which is able to measure the air pressure at this point;
* K-type thermocouples: The gasifier is equipped with a series of thermocouples, differential pressures gauges, and a control unit for the electrical parameters, which makes it possible to evaluate the whole gasifier temperature and pressure regimes;
* Gas analyzer: Finally, the gas composition was determined by means of a gas model cubic analyzer, in particular, the portable Biogas Analyzer Gasboard 3200L. This analyzer, equipped with a proprietary infrared and electrochemical gas sensor, is able to measure the concentrations of CH\({}_{4}\), CO\({}_{2}\), H\({}_{2}\), H, CO, and O\({}_{2}\) using an NDIR sensor.