esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### Implementation Details  
* Hardware setup: The experiments have been performed on our local personal computer, which contains an AMD Ryzen 3600x, 6-Core processor (3.80 GHz-4.4 GHz) with 16 GB of DDR4 RAM (3200 MHz). For faster computation, a single Nvidia RTX 3070 GPU (5888 CUDA-Cores) with 8 GB of video memory was also used.
* Preprocessing: For video preprocessing and 2D global temporal feature extraction, the model was tested similarly by dividing 25 frames for every video. A VGG-16 model [42] pre-trained on the ImageNet dataset [57] was used for separating visual appearance highlights. We resized the frames to 224 \(\times\) 224 resolution. The VGG-16 model selected an arrangement of 4096-dimensional element vectors delivered by the completely associated with the fully connected fc7 layer. For the InceptionV3 [47] and Xception [48], the pre-trained model, we resized the frames to 299 \(\times\) 299 resolution. Moreover, to extract the motion, we used an Inflated 3D network (I3D) pre-trained in kinetics [50] dataset. Then, the classifier layer was tweaked from all the models. The model was very efficient for action identification. We used faster R-CNN [51] for local features such as face detection. In addition, for the sentence portion, we have used word tokenizer NLTK [54] based tool and set the vocabulary of every dataset in the lower-case and removed the punctuations from the sentence. The total vocabulary size of the two datasets is approximately 42 k.
* Experimental Setup: We have proposed an attention-based bidirectional-LSTM model [53] for video captioning. The model can predict both the past and future words and combine them to generate a meaningful sentence. Both forward and in reverse data can be handled to catch relevant data. The attention-based two-layered LSTM [53] has 512 hidden nodes in each layer. The visual substance is separated from the various proposed CNN models [42, 47, 48] and the primary layer, along with an attention mechanism to feed the decoder. Moreover, the decoder with sequential LSTM [42] and the attention layer also consists of 512 hidden nodes in each layer. The regularization dropout layer is used in our proposed model. We set the dropout 0.5 at both the encoder and decoder levels. We used the ADAM optimizer to generate the loss function. We defined the starting learning rate \(10^{-5}\) to neglect the gradient burst. In addition, we included a beam search algorithm with size 5 to produce the textual description. Then, we trained our model with a batch size of 64.  
In the training stage, to manage sentences with fluctuating spans, we included the <start> and <end> tags to begin and end the sentence. Inspired by [15], we trained our model over 500 epochs and monitor the valuation metrics. If the valuation metrics did not progress in the validation set, we stopped the training in 25 epochs. Lastly, we used the Tensorflow [55] and Keras [56] libraries to implement our model.
# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
## 3 Materials and Methods
### Combined Forecasts  
In different prediction scenarios, prediction using only a single model often suffers from high sensitivity to data changes and poor generalization ability applied to different scenarios. In contrast, a combined model can be composed of multiple single models combined with decomposition and denoising techniques, and the prediction results of the combined model can be obtained by assembling multiple base models. This not only improves the overall prediction accuracy but also reduces the limitations caused by using only a single model for prediction [56]. In this study, weight optimization is used to incorporate the prediction results of each base model to form a combined prediction by assigning appropriate weights to each base model in the combined model (see Definition 1 for details). The combined prediction model can effectively incorporate the unique advantages of each model, thus improving the accuracy and reliability of prediction.  
**Definition 1**.: _For an individual prediction model of type \(m\), by giving the corresponding weights \(\mathbf{W}\) = \(\{w_{1}\), \(w_{2}\), \(w_{3}\),...\(\}\), the combination prediction can be expressed as \(\hat{y}_{combined}\ =\ \hat{y}_{1}\ *\ w_{1}+\ \hat{y}_{2}\ *\ w_{2}\) +...+ \(\hat{y}_{m}\ *\ w_{m}\)._  
An important issue that needs to be addressed in forecasting combinations is the combination strategy. To further evaluate the performance of the PSO-GA weight assignment method in various prediction tasks and find the most suitable combination model for scallion price prediction, we apply it to three combinatorial strategies with different combinatorial capabilities: homomorphic, heteromorphic, and semi-heteromorphic. We classified the results based on the similarities and differences between different models. That is, if different base models use the same learning algorithm, then the combination is called a homogeneous structure, if different learning algorithms are used, then it is a heterogeneous structure, and the combination model that contains both homogeneous and heterogeneous structures is a semi-heterogeneous structure. By complementing the advantages of different types of models, the predictive performance of the model can be improved, overfitting can be reduced, and the generalization ability of the model can be improved.
## 2 Materials and Methods  
### The Study Design and Participants  
This cross-sectional study was conducted at Beijing Tiantan Hospital, Capital Medical University. The study protocol was approved by the hospital's Ethics Committee, and written informed consent was obtained from all participants.  
Patients with IDDs (MS, NMOSD, and other IDDs) were recruited from the Neurology Department between January 2022 and December 2024, diagnosed according to the current international criteria. Physical disability was quantified using the Expanded Disability Status Scale (EDSS), rating neurological impairment from 0 (normal) to 10 (death) based on eight functional systems in the CNS [28]. To minimize confounding by severe motor/visual deficits, we only included patients with EDSS scores \(<6.5\) and visual-function EDSS scores \(<3\).  
Exclusion criteria included the following:  
1. The use of psychotropic medications or other drugs that could affect cognitive function;
2. The presence of severe psychiatric disorders as assessed by clinical experts;
3. A history of traumatic brain injury or stroke;
4. Other neurological disorders that could impact cognitive performance;
5. An inability to complete the cognitive assessment protocol.  
All participants underwent a one-time assessment session lasting approximately 60 min. At enrollment, we recorded detailed medication history including drug names, start/end dates, and the self-rated relief levels for both disease-modifying therapies and symptomatic treatments. To reduce acute pharmacological confounding, only patients on stable regimens (no dose changes or relapses \(\geq\)4 weeks before testing) were included.  
Cognitive impairment was defined using the MoCA (Beijing version), with scores \(<26\) indicating impairment according to its original validation [7] and subsequent application in neurological populations. Alternative thresholds have been proposed, including lower cutoffs (23/24) for better specificity [29] and stratified thresholds based on age and education [30]. However, our sample is relatively young (mean: \(37.8\pm 10.5\) years) and highly educated (72% \(\geq 16\) years; see Results Section 3.1), with limited demographic variability. Based on these sample characteristics and to maintain consistency with most cognitive studies on IDDs [31] and avoid demographic confounding, we retained the standard \(<26\) cutoff. Participants were accordingly classified into cognitively impaired (CI, MoCA \(<26\)) and cognitively normal (CN, MoCA \(\geq 26\)) groups.  
Demographic information was collected, including age, gender, and years in education, for all participants.
# Thermal Impact of 5G Antenna Systems in Sandwich Walls
###### Abstract  
The 5 th generation (5G) cellular networks offer high speeds, low latency, and greater capacity, but they face greater penetration loss through buildings than 4G due to their higher frequency bands. To reduce this loss in energy-efficient buildings, a passive antenna system was developed and integrated into sandwich walls. However, the thermal effects of this system, which includes highly thermally conductive metals, require further study. In this research, three-dimensional heat transfer simulations were performed using COMSOL Multiphysics to determine the thermal transmittances (U-values) of 5G antenna walls. The results revealed that, using stainless steel as the connector material (current design), the U-value rose from 0.1496 (for the wall without antenna) to 0.156 W/m\({}^{2}\)K, leading to an additional heating loss per year of only 0.545 KWh/m\({}^{2}\) in Helsinki. In contrast, with the previous design that used copper as the connector material, the U-value increased dramatically to 0.3 W/m\({}^{2}\)K, exceeding the National Building Code of Finland's limit of 0.17 W/m\({}^{2}\)K and causing 12.8 KWh/m\({}^{2}\) additional heat loss (23.5 times more than the current design). The current design significantly reduces thermal bridging effects. Additionally, three analytical methods were used to calculate antenna wall U-values: parallel paths, isothermal planes, and ISO 6946 combined. The isothermal planes method was found to be more accurate and reliable. The study also found that a wall unit cell with a single developed 5G antenna and a wall consisting of nine such cells arranged in a 3 \(\times\) 3 grid pattern had the same U-values. Furthermore, areas affected by thermal bridging were typically smaller than the dimensions of a wall unit cell (150 mm \(\times\) 150 mm).  
5G passive antenna system; 5G antenna walls; sandwich wall; numerical modeling; parallel path method; isothermal planes method; ISO 6946 combined method; thermal transmittance 2023
# Learning Optimal Time Series Combination and Pre-Processing by Smart Joins
## 3 Proposed Solution: Smart Join
### Application Example  
The current subsection introduces an illustrative example of the application of the proposed method to a dataset from a simple piecewise function. Suppose that the piecewise function is sampled irregularly in order to save memory applying two criteria:  
* The system checks every minute if the value of the data point has changed enough according to a pre-established criterion (in this particular case, a difference with the prior data point higher than 0.5) to save that data point.
* Every minute the system also checks the difference in time with the last saved data point and if this difference is greater than or equal to four minutes it saves the last available data point.  
The original piecewise function and the saved data using these criteria are shown in Figure 3.  
Suppose that the desired time reference system corresponds to \(t_{d}=\{1,3,5,\ldots,33\}\). Results after the application of different joining methods are shown in Figure 4. Error values used in the optimization of the Smart Join methodology are shown in Table 6.  
Because the input for the algorithm is the received data, when default weights in the error function are used (\(w_{i}=1/7\ \forall i\)), the minimal error is obtained by the nearest join (see Figure 4b). However, if knowledge about the irregular sampling approach used by the system is introduced by penalizing the anticipation of data points (for example with \(w_{5}=w_{6}=2/9\) and \(w_{1}=w_{2}=w_{3}=w_{4}=w_{7}=1/9\)), the optimal join method is backward join. Figure 4d shows that the data points obtained by the backward join as a result of taking into account this extended description of the data sampling mechanism are the ones that are the closest to the real piecewise function.  
Figure 3: Application example problem.  
Having established the significance of the measure of quality of a joining method, in the remainder of this contribution we leverage mathematical optimization techniques on training data to automatically determine which of the joining methods is most adequate for a given time series.
###### Abstract  
Optical modulators are key components to realize photonic circuits, and Mach-Zehnder modulators (MZM) are often used for high speed short reach interconnects. In order to maximize the tolerable path loss of a transmission link at a given bitrate, the MZM needs to be optimized. However, the optimization can be complex since the overall link performance depends on various parameters, and, for the MZM in particular, implies several trade-offs between efficiency, losses, and bandwidth. In this work, we propose a general and rigorous method to optimize silicon MZM. We first describe the optical link, and the numerical method used for this study. Then we present the results associated to the active region for 1310 nm applications. An analytical model is generated, and allows us to quickly optimize the p-n junction depending of the targeted performances for the MZM. Taking into account the required optical link parameters, the maximum tolerable path losses for different length of MZM is determined. By applying this method, simulations show that the optimum MZM length for 25 Gbps applications is 4 mm with an efficiency of 1.87 V-cm, 0.52 dB/mm of losses. A tolerable path loss of more than 25 dB is obtained.  
silicon photonics; Mach-Zehnder Modulators; optical communications; short reach Interconnects +
Footnote †: journal: Applied Physics Letters
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 2 Literature Review
### Mobile Ad Hoc Network (MANET)  
The development of mobile ad hoc networks can be traced back to projects run by the American Defence Advanced Research Projects Agency (DARPA) in the 1970s, when packet radio systems (PRNET) are developed to carry Advanced Research Projects Agency Network (ARPANET) between fixed and mobile nodes ([PERSON] and [PERSON], 1987, [23]). A key focus in MANET has been the routing problem, and a large number of routing protocols have been proposed: destination-sequenced distance-vector routing (DSDV, [PERSON] and [PERSON], 1994, [24]); dynamic source routing ([PERSON], [PERSON] and [PERSON], 1996, [10]); ad hoc on-demand distance vector routing (AODV, [PERSON] and [PERSON], [25]) and its variations: ad hoc on-demand multipath distance vector (AOMDV, [PERSON] et al., 2017, [26]) and NACK-based AODV (N-AODV, [PERSON] et al., 2014b, [27]; [PERSON] et al., 2015b, [28]) to provide quicker access to a network topology compared to typical AODVs. Most of these protocols only exist in design or simulated forms used for initial evaluation. Some MANETs are modelled using formal models (e.g., Petri Nets ([PERSON] and [PERSON], 2010, [29]), abstract state machines (ASMs, [PERSON], et al., 2014a, [30]), tools with formal descriptions to evaluate MANET protocols (e.g., Datamonitor in [PERSON] and [PERSON], 2013, [31]), and use of a formal passive testing approach to detect flaws in MANET protocols (ibid; [PERSON] et al., 2009, [32]). ASMs provide a means to facilitate high-level analysis of a system ([PERSON] and [PERSON], 2003, [33]), including properties of the system under study ([PERSON], nd [34]), while [PERSON] and colleagues (2015a) [35] have provided predicate abstractions to verify formal models of abstract state machines). The Internet Engineering Task Force (IETF) mobile ad hoc networks working group has a small number of protocols documented via requests for comments (RPCs) (13 as of September 2013) (IETF, nd, [36]). Currently, there is no commercial nor publicly available MANET implementation. A literature search reveals frequently asked questions (FAQs) posted to the IET MANET mailing list requesting concrete examples of actual MANET deployment. Some of the vague responses to these requests are \"the military are using them\", etc. ([PERSON], 2007, [37]; [PERSON], 2008, [38]; [PERSON], 2011, [39]). [PERSON] and colleagues (2014) [40] state that \"The practical deployment of vehicular networks is still a pending issue\". The reasons for this are beyond the scope of this research, but MANET research still remains in the experimental stage due to the technological challenges discussed by [PERSON] and colleagues (nd) [41]. [PERSON] and [PERSON] (2007) [42] stress that though pure, general-purpose MANET is yet to exist in the real world, technologies based on the multi-hop ad hoc networking paradigm (e.g., VANET, Mesh, and Opportunistic networks) have been successfully deployed and are penetrating the mass market.
# NaviSoC: High-Accuracy Low-Power GNSS SoC with an Integrated Application Processor
So far, most of the designs either offer only configurability between different bands of GNSS without the possibility of simultaneous multi-frequency reception of all constellations by a single chip [13; 15; 16; 19] or they do not receive all bands from the most popular GNSS systems and focus only on L1/E1 and L5/E5a reception [14; 17]. This paper presents the design of the CCNV1-A1 chip developed under the NaviSoC project [20], the first ever dual-frequency, multi-band, multi-constellation navigation receiver integrated with a multi-core application microprocessor as one SoC (System-on-a-Chip) capable of receiving, simultaneously, all GNSS signals located in L1/E1 and L5/E5 bands. The scope of the paper covers hardware description and results. The main focus is on the noise, gain and bandwidth of the designed system, as these are the main parameters that can influence the overall system performance. The overall navigation system is not fully covered. The receiver parameters, such as sensitivity, strongly depend on the signal type received and demodulation algorithms. The algorithms are a part of navigational software which is currently under development. For that reason, some system level parameters are not presented. The paper is organized as follows. Section 2 provides an overview of the SoC, application processor unit and frequency plan with radio architecture. The circuit level design is described in Section 3. A comparison with state-of-the-art parameters is reported in Section 4. The paper ends with a conclusion.
# A Systematic Review of the Role of BIM in Building Sustainability Assessment Methods
## 1 Introduction
[25] have used several different BIM software to assess eight credits from six categories belonging to the BREEAM UK Refurbishment and Fit-out 2014 scheme. [PERSON] and [PERSON] [18] have gathered the necessary data for evaluating 26 out of 56 criteria from BEAM Plus (with Autodesk Revit, developed by Autodesk, Inc, San Rafael, CA, USA). They achieved a faster assessment with fewer resources when compared with the traditional method. For the Australian Green Star Building certification, [PERSON] and [PERSON] [26] have assessed 66% of its credits. They argue that the capabilities of BIM were not used to their full potential. It is essential to develop BIM execution and coordination plans, which addresses the requirements of green building certification. [PERSON] et al. [27] have suggested a conceptual framework, where 75% of the criteria from the New Zealand Green Star certification can be supported by BIM, highlighting the importance of creating proper guidelines for BIM integration. By carrying out a Delphi analysis using 24 experts, [PERSON] et al. [28] have identified that 31 out of 78 items from the Singapore Green Mark Non-Residential Buildings 2015 can be assessed with BIM and building performance analysis tools. Concerning the SBTool, [PERSON] et al. [5] have presented a 3-stage framework for a BIM-based Application Programming Interface (API) to automate the assessment of 24 of the 25 criteria from the Portuguese residential version of this BSA scheme. They are currently developing a practical assessment of the identified criteria. As it was possible to understand, different approaches and software are being used to integrate BIM in BSA methods. Additionally, several BSA methods and versions are also being applied, making it challenging to create a common understanding of the actual level of BIM integration. Facing the existing knowledge gaps, the present research aims at establishing a common understanding regarding which BSA criteria (from the most recognised schemes) were already practically assessed with a BIM-based procedure. To archive this goal, a systematic review will be carried out. Relevant publications from the past 10 years will be identified, to analyse the topic trend in terms of interest, applied software, preferred journals and addressed BSA method/version. Furthermore, it is also intended to investigate the applicability and attractiveness of a BIM-based process to assess SBTool, facing the existing procedures available for LEED and BREEAM. The outcomes of this research will provide a basis and guidance for future researchers on the topic of the actual and prospective state of BIM integration in BSA methods. As BSA methods usually have similar criteria (applied procedures/software can be adapted to other sustainability schemes) [22], insights will also be gathered for process replicability.
## 5 Results
### Comparison to Proposed Model Computing Time  
To prove the optionality of the proposed model, the comparison result of computation times of the proposed model with the computational time of the vehicular network based on PoW is mentioned. The results can be seen in Figures 13 and 14.  
Figure 13 compares generation block time in Sharding algorithm with POW as the most well-known consensus algorithm. As shown in this figure, by increasing number of vehicles, the required time to mine a new block in the proposed system, in contrast with POW, increases near linearly.  
Figure 14 shows the calculation time for vehicles. It is clear from the diagram that the vehicle calculation time is less in our proposed model. As a result, it shows the success of this method compared to previous methods.  
Figure 12: Accuracy and precision of the proposed system for different event rates and number of generated blocks. (**a**) Model accuracy; (**b**) Model precision.
###### Abstract  
This study aimed to determine the antibacterial activity of the essential oil of fingerroot (_Boesenbergia pandurata_) (EOF) as a natural preservative in ground meat and is effect on the formation of heterocyclic amines (HAs) in pan-fried meatballs. EOF was applied either by adding it to ground pork or marinating pork in it before grinding. In addition, the antibacterial activity of EOF was tested. Aerobic mesophilic total viable count (TVC), lactic acid bacteria (LAB), and Enterobacteriaceaeae bacteria were monitored. The results show that EOF exhibited strong antibacterial activity when added at concentrations of 1.0 and 2.5 wt%. Antimicrobial activity against TVC, LAB, and especially Enterobacteriaceae bacteria was observed at all EOF concentrations (0.25, 0.5, 1.0, and 2.5 wt%). A 2.5% concentration of EOF applied by marinating trimmings can extend the shelf-life of ground pork to 18 days, while 2.5% EOF applied via addition can extend the shelf-life to 15 days, compared with 3 days for the control sample. After frying the meatballs, the inhibitory effect on the formation of heterocyclic amines was only significant for MelQx with the highest addition of EOF (2.5 wt%). Significant increases in the concentrations of all other HAs were determined by adding EOF (2.5 wt%).  
meat; fingerroot; essential oil; antibacterial activity; _Boesenbergia pandurate_; heterocyclic amines; cooking; meatball; pork