esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Building Arabic Word Embeddings
### Model Training  
To train our Arabic models, we employed two algorithms: Wor2 Vec and FastText. For the Wor2 Vec algorithm, we utilized the Gensim library, while the FastText library was used for the FastText models. [PERSON] et al. [3] emphasized that the most critical hyperparameters for word embedding models are architecture, dimension size, and context window size. Consequently, we constructed various models based on different combinations of these hyperparameters.  
The architecture hyperparameter pertains to the utilized training algorithm, whether it is a skip-gram or CBOW. Additionally, the models input the words as numerical values, known as vectors. The dimension size refers to the vector size for each word, determining the number of features that represent the word. It can range from 100 to 1000. Unfortunately, there is no research available that provides the optimal hyperparameter values for Arabic. To avoid overfitting or underfitting problems for our models, we chose 200, 300, and 400 as the vector sizes.  
Lastly, for a morphologically rich language such as Arabic, the context window size plays a significant role in training the models. Essentially, the context window size refers to the number of words surrounding a specific word (target word). It determines the attention range for the model regarding the specific word. This means that the surrounding words are part of the word prediction. Given Arabic's rich syntax and semantic properties, we selected 5, 7, and 10 as window sizes.  
By taking into account the three different corpora and the previously chosen hyperparameter values, the total number of possible combinations is 108. Consequently, we trained 108 distinct models. Among these models, those trained with the Wikipedia corpus required the longest time, with each model taking 8 h to complete. In contrast, the models trained with the Books corpus took 25 min per model, and the Watan corpus models took approximately 10 min per model.
## 3 Results
### Development and Validation of the \(K_{\rm d}\)(490) Algorithm  
The simultaneous sampling sites for \(K_{\rm d}\)(490) were divided into two groups. Two thirds of the sampling sites (N = 26) were used to establish an algorithm. To determine the optimal band or combination of bands for deriving \(K_{\rm d}\)(490), a correlation analysis was performed on the in situ measured \(K_{\rm d}\)(490) and different OLCI-derived \(R_{\rm rs}\) bands or combinations of OLCI-derived \(R_{\rm rs}\) bands. The dual band ratio (681 nm/560 nm and 754 nm/560 nm) algorithm is found to have a high accuracy in estimating \(K_{\rm d}\)(490) (Figure 5; R\({}^{2}\) = 0.81, RMSE = 0.99 m\({}^{-1}\), RMSE\({}_{\rm rel}\) = 25.36%, MNB = 2.81% and NRMS = 25.73%). The remote sensing-based algorithm for deriving \(K_{\rm d}\)(490) from the OLCI-derived \(R_{\rm rs}\) is as follows:  
\[K_{\rm d} \tag{490}\] \[=11.89*\frac{R_{\rm rs}(681)}{R_{\rm rs}(560)}+6.81*\frac{R_{\rm rs}(75 4)}{R_{\rm rs}(560)}-6.17 \tag{3}\]  
The remaining one third of the data (N = 14) was used to validate the algorithm (Figure 5). The validation accuracy of the algorithm is also high (R\({}^{2}\) = 0.83, RMSE = 1.06 m\({}^{-1}\), RMSE\({}_{\rm rel}\) = 30.81%, MNB = \(-3.89\%\) and NRMS = 31.63%), and the intersection points between the measured and estimated values of \(K_{\rm d}\)(490) are evenly distributed on both sides of the 1:1 line. This suggests that the algorithm has a high estimation accuracy for both high and low ranges and exhibits high consistency. Therefore, the dual band ratio algorithm generally has a high accuracy and applicability and can be used to derive \(K_{\rm d}\)(490) for the waters of Lake Taihu from remote sensing data.  
Figure 4: (a) Comparison of the \(R_{\rm rs}\) measured in situ and the OLCI-derived \(R_{\rm rs}\) after the 6S atmospheric correction for 40 matchups obtained in November 2016, March 2017, and April 2017; (b) comparison of the \(R_{\rm rs}\) spectrum measured in situ and the OLCI-derived \(R_{\rm rs}\) spectrum after the 6S atmospheric correction for different \(K_{\rm d}\)(490) values. The lines represent the \(R_{\rm rs}\) spectra from the in situ measurements, and the marks represent the OLCI-derived \(R_{\rm rs}\) spectra after the 6S atmospheric corrections.
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 2 LoRa Overview
### LoRa Protocol Stack  
LoRa, which stands for \"Long Range\", is a long-range wireless communications system, promoted by the LoRa Alliance. This system aims at being usable in long-lived battery-powered devices, where the energy consumption is of paramount importance. LoRa can commonly refer to two distinct layers: (i) a physical layer using the Chirp Spread Spectrum (CSS) [17] radio modulation technique; and (ii) a MAC layer protocol (LoRaWAN), although the LoRa communications system also implies a specific access network architecture.  
The LoRa physical layer, developed by Semtech, allows for long-range, low-power and low-throughput communications. It operates on the 433-, 868- or 915-MHz ISM bands, depending on the region in which it is deployed. The payload of each transmission can range from 2-255 octets, and the data rate can reach up to 50 Kbps when channel aggregation is employed. The modulation technique is a proprietary technology from Semtech.  
LoRaWAN provides a medium access control mechanism, enabling many end-devices to communicate with a gateway using the LoRa modulation. While the LoRa modulation is proprietary, the LoRaWAN is an open standard being developed by the LoRa Alliance.
## 4 Proposed Hybrid FL-Based Framework  
This section suggests a federated learning architecture that addresses privacy concerns and effectively predicts cardiac disease in a healthcare system that is sensitive to privacy concerns. Table 1 provides a description of the symbols used in our suggested framework, where \(X_{io}\) represents the initial vector for client sites using the M-ABC algorithm; \(X_{ri}\) is the randomly chosen local solution; the candidate solutions of employed, onlooker, and scout bees are represented as \(C_{ent}\), \(C_{ent}\), and \(C_{ent}\), respectively; Fit[n] is the fitness function; \(B\) is the size of the local batch at HSP clients; \(\omega_{o}\) is the initial model disseminated by the HSP global orchestrator; similarity function is represented as \(c\) (\(w_{jl}\), \(\theta_{i}\)); and the decision function based on RB-SVM for the heart disease dataset \(d\) is represented by \(\mathrm{D}_{F}\) (\(d\)).  
In the FL-based HSP environment, we have a small number of HSP client devices and the data for independent model training on each device are not sufficient; an FedMA approach is more suitable. Our idea is to train multiple models independently on every HSP client and then average their predictions to produce a final prediction. This can lead to a better performance by reducing overfitting and increasing the diversity of the models. We propose an FL-based framework for privacy-aware prediction of the heart disease. Our proposed framework is constituted for the HSP client- and server-end. For the HSP client, the hybrid model of the RB-SVM with M-ABC for optimal classification and feature selection is proposed. For the HSP server, the FedMA is proposed to overcome the issues of HSP's central model updation and communication efficiency.  
\begin{table}
\begin{tabular}{c c} \hline
**Symbol** & **Brief Description** \\ \hline \(X_{io}\) & Initial vector for MABC at client sites \\ \hline \(X_{ri}\) & Local solution chosen randomly \\ \hline \(C_{ent}\) & Employed bee’s candidate solution \\ \hline \(C_{ent}\) & Candidate solution from onlooker bee \\ \hline \(C_{ent}\) & Candidate solution obtained by scout bee \\ \hline Fit[n] & Fitness function \\ \hline N & Number of HSP clients \\ \hline \(B\) & Local minibatch at every HSP client \\ \hline \(\omega_{o}\) & Initial model by HSP global orchestrator \\ \hline \(c\) (\(w_{jl}\), \(\theta_{i}\)) & Similarity function \\ \hline \(\theta_{i}\) & Gaussian mean \\ \hline \(w_{jl}\) & Weight of \(l\)th neuron on dataset \(j\) in MABC \\ \hline \(E\) & Local epochs \\ \hline \(\eta\) & Learning rate \\ \hline \(\omega_{N}\) & Model of \(N\)th HSP client \\ \hline \(d\) & Input dataset to RB-SVM \\ \hline \(K_{\mathrm{F}}\) & Kernel function \\ \hline \(D_{F}\) (\(d\)) & Decision function on dataset \(d\) in RB-SVM \\ \hline \(m_{r}\) & Margin function \\ \hline \(RB_{\mathrm{F}}\) & Radial basis function \\ \hline \end{tabular}
\end{table}
Table 1: Brief description of the symbols utilized in our proposed framework.
# A Data-Driven State Estimation Based on Sample Migration for Low-Observable Distribution Networks
## 4 Case Study  
Two actual 26-node distribution networks in a certain region are selected as test systems in this paper. According to the method proposed above, all 26-node distribution networks in the region are clustered into four typical topology sets. One topology type is selected, with the distribution networks featuring complete measurements serving as the source domain and the distribution networks with insufficient measurements as the target domain. Based on sample migration technology, the abundant samples in high-observable distribution networks are migrated to low-observable distribution networks.  
Multi-time section power flow calculations are performed based on load curves which come from actual operational data of the region networks, and Gaussian white noise is added to node voltage amplitudes and node injection powers to simulate real-time measurements. The node voltage magnitudes generated by power flow calculations are used as the true values, while the WLS is applied for multi-time section state estimation to generate estimated node voltage magnitudes, which are used as labels for model training. This paper selects 4000 cross-sectional measurements for training the source domain model, with 3500 cross-sections used for model training and 500 cross-sections used for validating. For the low-observable distribution networks in the target domain, select the few section measurements of the low-observable distribution networks in the historical database, and based on the distribution networks sample migration technique, make the state estimation model of the source domain applicable to the distribution networks of the target domain.  
This algorithm proposed above is implemented in Python, using the Keras module used to establish the state estimation model, specifically a convolutional neural network (CNN). After extensive testing, the CNN model is composed of three convolutional and two dense layers, with 32 samples processed per iteration and 60 training epochs. The simulations are conducted on a personal computer with an Intel(R) Core (TM) i5-8300H CPU @ 2.30 GHz and 8.0 GB of memory.
# Exploring Decentralized Warehouse Management Using Large Language Models: A Proof of Concept
###### Abstract
In the context of warehouse management, LLMs can ingest real-time data streams, encompassing current warehouse utilization, incoming demand patterns, and dynamic pricing structures, and generate optimized storage decisions (to accept, reject, or forward offers) on the fly, thus eliminating the need for complex middleware or custom optimization solvers [16]. Moreover, by leveraging a stateless prompting strategy, each decision is framed as a self-contained query that includes all relevant background, the current state, and minimal history. This prompt-only approach sidesteps the substantial data collection, computational expense, and maintenance overhead associated with fine-tuning or training domain-specific models from scratch [14]. In practice, such a deployment yields sub-second inference latencies and controlled per-transaction costs while still achieving a decision quality that adapts to uncertainty and volatility, addressing many of the data-processing and standardization hurdles previously identified in AI adoption within supply chains [17]. Previous research on decentralized warehouse management has mainly focused on the use of multi-agent systems, game theory, and decision-theoretic models to improve resource allocation and logistical efficiency [4; 5; 10; 18; 19; 20; 21]. These studies demonstrate the potential of agent-based systems to simulate cooperation and competition between autonomous units while highlighting the challenges of optimal decision-making under uncertainty, such as the secretary problem [11]. On-demand warehousing platforms such as Flexe and Ware2 Go have been investigated for their ability to convert fixed costs into variable costs and support SMEs in dynamic markets [9]. Recent advances in AI have introduced LLMs as potential decision engines in decentralized environments. Studies have shown that they are able to process structured and unstructured data and draw strategic conclusions without the need for extensive domain-specific training [14; 15; 16]. However, the previous literature has largely treated these elements--optimization, collaboration, and AI--separately. The present work bridges this gap by integrating LLMs into a decentralized game-theoretic warehouse system, building on previous findings while demonstrating superior performance through simulation-based validation. Our contribution is thus located at the intersection of collaborative warehousing, AI-driven decision-making, and decentralized systems, providing a novel and practical architecture for collaborative production contexts.
# Adaptive Multi-Grained Buffer Management for Database Systems
## 4 Performance Evaluation
### Settings  
We conduct all experiments on a PC powered with a processor of Intel Core i5-6200U 2.30 GHz, 8 GB DDR4 DRAM, and an SSD (Samsung 860 EVO) of 500 GB. The operating system is Ubuntu 8.4. We use the direct I/O mode in Linux to eliminate the influence of the file-system cache. We use a dataset of 1 GB and a buffer pool of 128 MB (10% of the data set) by default for all experiments. Additionally, we use the tuple size of 128 B and the page size of 4 KB by default.  
Traditional buffer managements are usually evaluated using page-grained requests, such as _<page id, request type_>. In this paper, we need to use tuple-grained requests consisting of a tuple id and a request type. As there is no open-source workload satisfying our experiments' need, we generate tailor-made traces to verify the performance of AMG-Buffer.  
In the following experiments, we evaluate the effectiveness of AMG-Buffer by comparing it with various buffer management schemes, including LRU [1], LIRS [2], CFLRU [4], CFDC [5], and MG-Buffer [7]. For emulating various real circumstances, we change workloads with different read/write ratios and hybrid ratios. At last, we evaluate AMG-Buffer under dynamic workloads and a large data set. Note that for SSD-based databases, the overhead of disk I/Os costs is still the main bottleneck. Thus, we focus on I/O costs and ignore the overhead of migration and merging operations.
###### Abstract  
_Listeria monocytogenes_ is a significant concern in cheese production. It has been assumed by some that wood surfaces pose a greater risk for pathogen contamination during cheese production. The goal of this study was to evaluate the effect of the microbiota obtained from wooden boards used in cheese ripening on the growth of _Listeria monocytogenes_. Bacterial communities from the surface of wooden boards obtained from cheese-ripening facilities were inoculated into tryptic soy broth (TSB) and incubated at 11 \({}^{\circ}\)C for 48 h. These communities (10\({}^{8}\) CFU/mL stationary phase cells) were co-incubated with 10\({}^{4}\)-10\({}^{5}\) CFU/mL _L monocytogenes_ 2203 at 11 \({}^{\circ}\)C for up to eight days. At various times, samples were removed, diluted in sterile saline and plated on modified Oxford agar. Bacterial communities from each of the five boards from three different facilities significantly inhibited growth of _L. monocytogenes_ in vitro, compared to growth of _L. monocytogenes_ 2203 alone. Using 16S rRNA analysis, we identified sequences belonging to the genera _Cambobacterium_, _Leuconostoc_ and _Staphylococcus_ as the most abundant in the communities grown in TSB. _Leuconostoc mesenteroides_ and _Staphylococcus_ isolated from the most inhibitory community significantly inhibited growth of _L. monocytogenes_ in TSB at 11 \({}^{\circ}\)C, compared to growth of _L. monocytogenes_ 2203 alone. These findings suggest that some members of the complex microbial communities on wooden boards in cheese aging facilities might inhibit the growth of _L. monocytogenes_.  
wood; cheese; bacteria; inhibition; safety; _Listeria monocytogenes_
# Unraveling the Impact of Class Imbalance on Deep-Learning Models for Medical Image Classification
## 4 Discussion
Regarding the application of statistical analysis and class balance, the research incorporates statistical analysis to guide image selection, in contrast to the more generalized methodologies of previous studies. This analysis enables informed selection, enhancing the representativeness of the dataset. The formation of balanced and imbalanced sets directly addresses the impact of class balance on the effectiveness of the classification model, an aspect not always explicitly dealt with in the compared studies. The review of studies highlights significant deficiencies in considering the differential impact of classes and specific patterns during the feature extraction phases, underscoring the lack of detailed analysis on the influence of classes and common image signs. This omission points to a critical need for more detailed classificatory evaluations to ensure precise and balanced interpretations in the classification of pulmonary diseases. The importance of a holistic approach that prioritizes the optimization of architectures and the calibration of the training period for improved generalization becomes evident. Furthermore, the need for adaptive and meticulous preprocessing of medical images to address challenges such as dimensional variability and class imbalance is emphasized. The current research underlines the relevance of customizing preprocessing techniques and conducting a model performance analysis that includes sensitivity, specificity, and class precision. This directs towards the development of more robust and equitable classification models, urging future research to establish clear guidelines for hyperparameter tuning and neural network architectures, therefore facilitating significant advances in the application of deep-learning technologies for medical diagnosis.
## 3 Results and Discussion
### Hydrolytic Characteristics  
#### 3.2.1 Degree of Hydrolysis  
Significant interaction effects on the degree of hydrolysis of porcine organ hydrolyzes were found (Figure 1b). The porcine heart exhibited a remarkably higher degree of hydrolysis than the other organs (\(p<0.05\)), which could be associated with the increased extractable protein in this organ. Numerous studies have noted that enzymatic hydrolysis could improve the solubility of food proteins as a result of exposure to free amino acids and carboxyl groups from peptide bonds [21]. On the contrary, our results indicate that the high extractable protein content could also increase the degree of hydrolysis. [PERSON] and [PERSON] [22] noted that a combination of advanced technologies, such as ultrasonication and high hydrostatic pressure, which facilitate conformational changes in food protein, could enhance the efficiency of enzymatic hydrolysis and lead to significant increases in the degree of hydrolysis. Another noticeable trend highlighting the more efficient enzymatic hydrolysis of porcine organs was observed for Alcalase compared to trypsin (\(p<0.05\)). Similarly, [PERSON] et al. [23] suggested that Alcalase is more suitable for hydrolyzing porcine liver than trypsin, based on the efficiency of its hydrolysis. Furthermore, our results indicate that Alcalase could have greater hydrolysis efficiency than trypsin in porcine liver, as well as in other porcine organs.
## 4 Simulations and Results Analysis
### Sinusoidal Steering Maneuver  
To evaluate the tracking performance of the 4 WS control system with the designed controller, a steering test on the sinusoidal wave maneuver was performed. The sinusoidal input of the front wheel angle is shown in Figure 20. The 4 WS vehicle initially ran at 120 km/h, and the road friction coefficient was set as 0.6 (wet road). The simulation results are shown in Figure 21.  
As can be seen in Figure 21, the 4 WS vehicle with the H2/H\(\infty\) controller had a smaller sideslip angle than those of the H\(\infty\) controller and the proportional controller, which shows that the H2/H\(\infty\) controller gave the 4 WS vehicle the best path tracking capability. We can see from Figure 21 that the 4 WS vehicle equipped with the H2/H\(\infty\) controller was fully capable of tracking the ideal yaw rate, while those of the H\(\infty\) controller and the proportional controller had yaw rate tracking errors. It was concluded that the 4 WS system with the H2/H\(\infty\) controller outperformed that with the Ho controller and the proportional controller in terms of path tracking and yaw rate tracking capabilities.  
Figure 19: The frequency response of the maximum singular value of perturbation block \(\Delta\) transfer function matrix.  
Figure 18: The step responses of the front wheel angle under interferences: (**a**) sideslip angle response; (**b**) yaw rate response.
## 4 Numerical Simulation
### Results of Simulation  
We use the genetic algorithm to run the numerical simulations, and the key rate is optimized over the free parameters. Here, we set \(\epsilon_{RS}=\overline{\epsilon}=\epsilon_{EC}=\epsilon_{PA}=10^{-10}\) and utilize experimental parameters listed in Table 2. Figure 3 shows how the key rate varies with transmission distance between [PERSON] and [PERSON] when their channels have the same transmittance, where the total pulses are set as \(N=10^{12},N=10^{10},N=10^{8}\), respectively. We can find that our protocol shows great robustness against finite-key effects. In Figure 4, we plot the results of our asymmetric protocol when the total pulses are set \(N=10^{12}\), where the difference in length between [PERSON]'s channel and [PERSON]'s is fixed at 10 km, 50 km, and 100 km, respectively.  
Figure 3: The performance of our protocol under symmetric channels. Under the experimental parameters listed in Table 2, we simulate results in the case that \(l_{a}=l_{b}\), where \(N=10^{12}\), \(N=10^{10}\), and \(N=10^{8}\).  
When the original TF-DPSQSS [29] protocol is applied to the asymmetric channels, a high system error rate will arise since different channel transmittances will lead to the poor performance of interference at the beam splitter. Figure 5 presents numerical results of our asymmetric protocols and the original TF-DPSQSS [29] protocol with the difference in length between two channels fixed at 10 km and 14 km. We can see clearly from Figure 5 that our asymmetric protocol improves the secret key rate by two orders of magnitude when \(l_{b}-l_{a}=14\) km. Moreover, no keys are obtained with difference fixed at 50,100 km in the original protocol, whereas Figure 4 shows that our protocol still provides a high key rate when the difference is large. It means that in the asymmetric channels, the performance of the asymmetric protocol is much better than that of the original protocol, especially when channels are extremely asymmetric.  
Figure 4: The performance of our protocol under asymmetric channels. Under the experimental parameters listed in Table 2, we simulate results in the case that \(N=10^{12}\), where \(l_{b}-l_{a}=10,50,100\) km.  
Figure 5: Quantum secret sharing key rate vs transmission distance between [PERSON] and [PERSON]. Under the experimental parameters listed in Table 2, we compare the simulation results of the original TF-DPSQSS [29] protocol and our asymmetric protocol with \(N=10^{12}\), where the difference in length between [PERSON]’s and [PERSON]’s channels is fixed at 10 km, 14 km.