esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Antidiabetic Effects of Dietary Phenolic Compounds: Preclinical and Clinical Evidence
Thesemolecules induce benefits on glucose homeostasis through diverse mechanisms including the modulation of glucose metabolism in muscle and liver by dietary phenolic compounds that reach blood circulation, and the regulation of immune and endocrine pathways in the gut that also have an impact on glucose homeostasis. Table 4 details the _in vivo_ or _in vitro_ approaches conducted to identify the molecular pathways involved in the glucose-related benefits of phenolic compounds. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Clinical Evidence** & & & \\ \hline
**Compound** & **Clinical Trial** & **Administration, Concentration, and Duration of the Treatment** & **Evidence of Antidiabetic** & **Refs** \\ \hline Phenolic compounds from red grape pomace & NCT02865278 & One oral dose after overnight fasting & Reduced postprandial insulin levels and improved insulin sensitivity & [128] \\ \hline Flavonoids and phenolic acids & NCT01154478 & Dietary intervention & Improved oral glucose 2.903 mg/day & [129] tolerance and post-glucose insulin secretion and insulin sensitivity & [129] \\ \hline Grape phenolic compounds & NCT01478841 & Dietary intervention & Increased hepatic insulin insulin levels and improved insulin sensitivity & [130] \\ \hline Flavonoids and phenolic acids & NCT01154478 & Dietary intervention & Improved oral glucose 2.903 mg/day & [129] tolerance and post-glucose insulin secretion and insulin sensitivity & [131] \\ \hline Rseveratrol & HCT201710108129N11 & Oral: capsules 1 p/day 2 months & Reduced fasting glycemia, HbA1c, and oxidative stress & [130] \\ \hline
**Preclinical Evidence** & & & \\ \hline
**Compound** & **Animal Model** & **Administraion, Concentration, and Duration of the Treatment** & **Evidence of Antidiabetic Effects** & **Refs** \\ \hline Resveratrol & NOD mice: a non-obese model for T1D (NOD (H-2g7), NODD2.5 and NOD/SCID) & Daily oral gavage 250 mg/kg & Preventive effect: 30\% of the mice without T1D 23 weeks & [120] \\ \hline db/db mice: T2D and obesity model (C57 BL/KsJ-db/ DB mice) & Oral Supplementation diet with 0.005\% (\(w\)/\(w\)) resveratrol 6 weeks & Protective effect: Improved glycemic control and dyslipidemia & [125] \\ \hline Streptozotocin-induced diabetic mice (CS7 BL/6) & Oral: drinking water 2 weeks from the onset of diabetes & Protective effect: Improved glycemic control and dyslipidemia & [126] \\ \hline Goto-Kakizaki rat: Non-obese diabetic rats (Wistar substrain) & Daily oral gavage 20 mg/kg 10 weeks & Protective effect: Improved glucose tolerance and structure of pancreatic islets & [126] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Clinical and preclinical evidence of the benefits of phenolic compounds in glucose homeostasis.
# Lower Limb Muscle Activation in Young Adults Walking in Water and on Land
## 4 Discussion
Further research is needed to explore the optimal combination of walking speed, water resistance, and water depth to maximize TA activation and toe lift during the swing phase while also considering movement constraints. This is particularly relevant when extrapolating our study findings to populations with foot drops. For example, although our study introduced jet resistance only at 3.5 mph, future investigations should examine its implementation at slower speeds and with a sufficient immersion depth [28], which may better suit individuals with limited mobility or gait impairments. Given the observed influence of walking speed and jet resistance on the effect sizes, clinical populations such as those with foot drops may walk at a slower pace. Increasing the jet resistance may be an important consideration when extrapolating the findings of the current study to individuals with foot drops. It is also reasonable to assume that individuals with foot drop experience more drag opposing dorsiflexion during the swing phase, as foot drop results in a greater projected area. This increase in drag could enhance TA activation during the swing phase, especially if individuals with foot drop are unable to walk at a 2.5 mph pace in water due to their limitations. It is also important for future research to consider the water immersion depth, with chest-deep immersion recommended, as increased water depth has been observed to enhance the effect of fluid resistance on muscle activation during gait performed in water [28]. Several study limitations need to be acknowledged. Firstly, a convenient sample of healthy, recreationally active adults was chosen, limiting the generalizability of the findings to clinical populations. Future research should include individuals with conditions commonly associated with foot drop, such as cerebral palsy, multiple sclerosis, or spinal cord injuries, to assess the consistency of cross-sectional findings and the potential effectiveness of aquatic treadmill walking in reducing foot drop. Additionally, while increased TA activation during the swing phase in water was observed, this study did not establish a causal link between increased TA activation and biomechanical or neural adaptations in dorsiflexor function. Future studies should investigate the effects of aquatic treadmill interventions on TA excitability, voluntary dorsiflexor torque, and toe lift/clearance while walking, as demonstrated in previous research [14; 15]. Lastly, it is important to consider the limitations of sEMG when interpreting the findings. Internal noise and movement artifacts stemming from factors like fat, skin, and muscle can directly influence sEMG recordings, potentially impacting the quality of the signal collection.
## 2 Materials and Methods
### Data
#### 2.2.4 MODIS Products  
The most widely used GPP product is MOD17A2H, which uses updated Biome Property look-up tables and an updated version of the daily GMAO meteorological data as input data. Since the time resolution is 8 days, there are a total of 92 scene MODIS GPP products used within two years. The purpose of using MOD17A2H is to compare of the model prediction accuracy.  
The MOD12Q1 land cover product with a 500 m spatial resolution, which is one of the MODIS products, was used in the MODIS GPP algorithm. Boston University's UMD classification scheme within the dataset was also employed for offering biome-specific information for GPP estimation models [6]. Moreover, a 1.5 km \(\times\) 1.5 km area centered on every flux tower site was extracted to represent the flux footprint, the mean values of the 5 \(\times\) 5 pixels and the mean values of the 3 \(\times\) 3 pixels were the final GPP values for Sentinel-3 OLCI products and MODIS GPP products, respectively [5; 50].
# Comparative Analysis of BERT and GPT for Classifying Crisis News with Sudan Conflict as an Example
## 4 Methodologies
### Methods
#### 4.1.2 Gpt  
The GPT-based classification framework was designed to evaluate the performance of seven open-source LLMs: Gemma2-9b, Gemma2-27b, Llama3.3-70b, Llama3.2-3b, Llama3.1-70b, Llama3.1-7b, and Mistral-7b. The goal was to identify the most effective model for both SL-MLG and MLC in classifying conflict-related news articles.  
To analyze their performance under varying learning conditions, three distinct prompting methodologies were employed:  
(1) Zero-Shot Prompting--The model was only provided with the input article \(x_{i}\), with no additional contextual information. This setting evaluates the model's raw generalization ability based on its pre-trained knowledge:  
\[\widetilde{y}_{i}=\textit{arg max}\;p_{\phi}(y\mid P(x_{i})) \tag{7}\]  
(2) RAG Without Definitions--The model received a prompt \(P(x_{i})\) composed of the article \(x_{i}\) and a flat list of category names \(C\). This format provides some task grounding while still relying on the model's internal representations:  
\[\widetilde{y}_{i}=\textit{arg max}\;p_{\phi}(y\mid P(x_{i},\;C)) \tag{8}\]  
(3) RAG with In-Context Learning (ICL): The prompt was expanded to include the article \(x_{i}\), category names \(C\), and detailed expert-annotated definitions for each category \(D\). This setup offers the highest level of task-specific guidance:  
\[\widetilde{y}_{i}=\textit{arg max}\;p_{\phi}(y\mid P(x_{i},\;C,\;D)) \tag{9}\]  
To support the RAG and RAG ICL approaches, Facebook AI Similarity Search (FAISS) was employed to build an efficient dense vector index of the expert-defined category definitions and curated context examples [44]. During inference, the top-k most relevant vectors were retrieved and appended to each prompt, enabling the LLM to incorporate precise in-domain context without relying on external documents or a general web-scale corpus.  
All GPT model experiments were conducted on a high-performance workstation equipped with an Intel(R) Xeon(R) w3-2423 CPU, 128 GB of RAM, and dual NVIDIA RTX A6000 GPUs (each with 48 GB of VRAM). This setup enabled efficient execution of large-scale models such as Gemma2-27B and Llama3.1-70B, particularly for RAG pipelines involving long-context prompts.
## 5 Results and Discussion
### Stacking Ensembles  
The stacking ensemble prediction performance is compared with those of the tree-based ensembles in terms of F1-score, AUC and Brier in Figure 3. Considering the three evaluation measures, we found that the stacking ensemble's prediction performance was high and consistent across almost all of the datasets. Moreover, the XGB tree-based ensemble showed a competitive prediction performance by having AUC scores equal to or greater than the stacking ensemble in almost 50% of the datasets. On the other hand, Ada was consistently one of worst performing ensembles on all datasets. Two other tree-based ensembles, ET and HGB, were the second worst ensembles; both achieved low AUC and F1 scores. The most significantly different performance was observed for the RF ensembles: low F1 scores on almost all datasets while having high AUC and Brier scores for 50% and 66% of the datasets, correspondingly.  
The Scott-Knott clusters with the AUC values in each dataset are shown in Figure 4. From the figure, we observe that the stacking ensemble was ranked as the best performing for 80% of the datasets, and the second best for the remaining datasets. Moreover, RF and  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Classifier** & **ET** & **RF** & **Ada** & **GB** & **HGB** & **XGB** & **CAT** \\ \hline Prop 5 & 6 & 9 & 16 & 409 & 308 & 896 & 4199 \\ SWT & 10 & 13 & 17 & 116 & 161 & 45 & 1321 \\ Xalan 2.5 & 10 & 14 & 16 & 106 & 131 & 45 & 1239 \\ Xalan 2.6 & 10 & 12 & 16 & 110 & 140 & 42 & 1313 \\ \hline AVG time (ms) & 8.86 & 11.90 & 17.29 & 234.19 & 254.05 & 267.90 & 2618.57 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
Figure 3: The stacking ensemble versus fine-tuned tree-based ensembles.  
XGB were among the first clusters for 40% and 30% of the datasets, respectively. GB and CAT ensembles were among the best performing ensemble models for 23% of the datasets.  
We further analyzed the rankings of ensemble models over the 21 datasets by performing a double Scott-Knott test. The procedure is shown in Figure 5. First, for each dataset, we  
Figure 4: AUC rankings of the studied ensembles models.  
ranked the ensemble models based on the AUC values using Scott-Knott algorithm. Then, we constructed a new dataset where each ensemble model had 21 ranking values (one from each dataset) obtained from the previous step. The newly constructed data were fed into the Scott-Knott algorithm to rank the ensemble models for all datasets. Figure 6 presents the ranking of the studied ensemble models over the 21 datasets. Stacking ensemble stayed the first-ranked ensemble. Next came RF and XGB as the second-best-performing ensembles.  
**RQ2 Answer.** The stacking generalization ensemble's prediction performance was significantly better, or at least competitive, compared to tree-based ensembles in defect prediction. Among all tree-based ensembles, RF and XGB ensembles showed significantly superior prediction performance.
## 4 Discussion
### Monitoring Lake Tana with the Sentinel-2 Satellites
(2020) employed Sentinel-2 with Level-1C (TOA) [37]. [PERSON] et al. (2020) used the PlanetScope imagery with higher spatial resolution (3-5 m) to identify water hyacinth by supervised classification; however, the atmospheric correction was not defined [26]. Our results, in comparison with other studies (Table S1), used BOA reflectance products from Sen2 Cor, allowing us to accurately and consistently monitor water hyacinth cover and biomass along the year and improve the relationship with water quality parameters (i.e., Chl-a and TSM) estimated with C2 RCC. In our work, water hyacinth was identified and mapped by computing NDVI values (range of 0.6-0.95) derived from the Sentinel-2 Level-2 images (Figures 6 and 7). [PERSON] et al. (2020) observed both lower NDVI levels and a range associated with water hyacinth (range of 0.27-0.47) [37], much lower than those observed in this work, probably due to the application of Sentinel-2 Level-1C (TOA) images without atmospheric correction. The increased sensitivity offered by the approach used here is crucial to distinguish the changes in both cover and biomass over different atmospheric conditions. To date, this is the first work in which the NDVI was calculated from L2A BOA Sen2 Cor to study the water hyacinth cover in the lake. Due to absence of other macrophytes floating on the water, the expansion of water hyacinth can be studied directly using NDVI from multispectral satellite images without the need to apply machine-learning algorithms, which are complex black-box models. Although remote sensing tools are accessible and cost-effective that permit the monitoring of large water bodies, data should be processed adequately to obtain appropriate and consistent results and allow efficient management.
## 6 Simulation Results and Discussion
### Green Impact and CO\({}_{2}\) Emission  
The CO\({}_{2}\) emission depends on the power supply in the base station. The important factor in the green impact and CO\({}_{2}\) emission is the conversion factor (\(c_{f}\)). The value of CO\({}_{2}\) emission conversion factor is different for the different energy sources [39]. The total CO\({}_{2}\) emission can be calculated as follows:  
\[\text{CO}_{2}\text{ Emission (kg)}=\text{Power (kWh)}\times c_{f} \tag{20}\]  
Based on the statistics in [40], the power consumption of a full load HeNB is assumed to be 165.42 kWh/year. The author in [41], assumed a constant CO\({}_{2}\) emission conversion factor of 0.5 kg CO\({}_{2}\)-e/kWh. Using the \(c_{f}\), we can estimate that a full load HeNB under No Power control approach  
Figure 5: Average throughput under different power control approaches.  
Figure 6: FBS Power Consumption Vs number of active subscribers.  
emits 82.71 kg of CO\({}_{2}\) per year. Table 3, shows the estimated CO\({}_{2}\) saving per year for one femtocell deployed in the dense urban area. From the Table 3, we can see that the proposed active power control technique offer better performance compared to its counterpart by saving 11.17 kg of CO\({}_{2}\) emission per year.  
The aim of the proposed APC technique is not only reducing ICI to MUE in close proximity of the femtocell and maintaining necessary QoS of FUE, but also reduces the unnecessary power losses. Moreover, the APC techniques activate only when the femtocells receive an IM from the victim MUE. The IM originates on the basis of IDF when the interference from the femtocell to victim MUE is higher than that of threshold interference. Before tuning its downlink transmission, HeNB seeks for the RSRP feedback report from the femto user. On the basis of IM and feedback report, HeNB actively tunes it downlink transmission power, whereas in proactive technique, the HeNB does not take care of the existence of nearby victim MUE and it tunes its downlink transmission power based on RSRP (Reference Signal Received Power), therefore, the proposed APC reduces unnecessary power consumption. Although femtocells are low-powered base stations, these power savings are crucial for the green deployment of femtocells, especially for dense deployment of femtocells that would result in increasing the total network consumption as millions of femtocells are expected to be deployed in the next few years.
## 3 Description of Information Technologies for Order Picking and Formation of an MCDM Model
### Order-Picking Technologies
#### 3.2.2 Pick-to-Light Technology  
This technology is the best in systems where a high speed of order picking is required, and those are most often in the retail, pharmaceutical, cosmetic, and electronic industries. Since there is no reading, writing, or searching for items with this technology, it is ideal for systems with large amounts of data and items. Order picking time is reduced by up to 50%, and accuracy can be increased by more than 80%. These systems use small lights mounted on carriers on racks and are directed straight toward order pickers. When an order is received from the information system, the lights associated with the items on the list turn on. In addition to the lights, the display also shows the number of pieces of a particular item for the order. The order picker takes the required number of items and presses the button when the light turns off and approaches the next item where the light turns on. [32]  
Pick-to-light systems are easy to be trained for and used and are ideal for large volumes of items. Figure 3 shows order picking by light detection.  
In a cramped environment with a high speed of movement of items, a pick-to-light system enables successful task implementation because all lights associated with items for a specific order are turned on at the same time. In this way, the order picker can determine the best order for taking individual items, which leads to a higher speed of order picking.  
Figure 2: Wireless order picking using barcode technology [31].  
The pick-to-light system requires the order picker to respond to light signals that identify the location and number of items to be taken from a shelf. When taking the required number of an item is completed, the order picker presses a button, turning it off and confirming that the process has been finished. Compared to others, this technology enables the highest order-picking speed. However, the system is not flexible, as it requires a fixed installation of equipment that cannot be moved so easily. Costs increase with each new item.
# A Cross-Layer Media Access Control Protocol for WBANs
## 2 Related Works
### Power Adjustment Scheme  
In order to improve the network throughput, a proper AP selection scheme is proposed in [25], considering the condition of the wireless channel. Meanwhile, transmission power is adjusted according to the wireless channel condition in the uplink and downlink to save energy. A Transmission Power Control (TPC) scheme is proposed in [26] to improve the network performances during on-body communications. The proposed solution combined the neural network and the fuzzy inference system to model the wireless channel, which considered different body postures. A power adjustment scheme is used when designing the ERPC-MAC protocol in [27]. Without relying on additional devices, ERPC-MAC employs relaying nodes to provide relay service for nodes that consume energy fast. To further reduce the energy consumption ratios of sensors in WBANs, a power adjustment scheme is proposed in [28], which considers an adaptive transmission power correction scheme in a one-hop star network. Because human motion is one of the dominant factors that affect the channel characteristics in WBAN, a motion classification scheme is proposed in [29], the power control scheme considers both the body motion and the temporal to enable efficient communication among nodes.  
Table 1 presents a summary of the related works. In related works, it is common to design power adjustment and relay cooperation separately. The power adjustment process often overlooks the quality of service requirements for heterogeneous data, and there is a lack of specific designs for the retransmission of heterogeneous data in case of data loss. The existing approaches also lack an integrated design that combines power adjustment, data loss retransmission, and cooperative transmission mechanisms when transmitting heterogeneous data. This makes it difficult to achieve synergistic optimization of network QoS and energy efficiency.  
Our work comprehensively considers the optimization design of the MAC protocol, cooperative relay, and power control scheme. The power control scheme aims to prolong the lifetime of sensors, the cooperative relay aims to further prolong the lifetime of the network, and the MAC protocol is responsible for enabling efficient cooperation among sensors and improving the QoS of the network.  
\begin{table}
\begin{tabular}{l l
## 3 Results and Discussion  
Table 1 shows the results of the physiochemical tests of the strawberry powders obtained by the freeze-dried (FD), convection drying (CD) and spray drying (SD) methods. The tested powders were characterised by their high dry matter content (d.m.) (ranging from 962.5 to 978.2 g kg\({}^{-1}\)) and correspondingly low water activity (a\({}_{\rm w}\)) (ranging from 0.19 to 0.28). The low water activity is important for ensuring the microbiological stability of the powders during storage, provided that they are properly barrier packaged. An a\({}_{\rm w}\) value of less than 0.6 is considered acceptable for raw products since microbiological activity, including that of osmophilic yeast, is reduced [29]. The low water content of dried fruit is important not only for microbiological consideration, but also because of the difficulty of grinding it into powder when the water content is higher (viscous texture). Similarly, as in the present work, low levels of a\({}_{\rm w}\) were found in chokeberry and blackcurrant powders prepared by different methods [33; 34]. The method by which the powders were obtained significantly influenced (\(p<0.05\)) their physical properties, particularly their WSI and WHC. The SD powders showed the lowest significant (\(p<0.05\)) water binding capacity, with the highest (\(p<0.05\)) water solubility index of 885.4 g kg\({}^{-1}\), indicating that these powders can be used for the products with the instant type of matrix where particle solubility is required, for example in beverages and jellies. Given the lower solubility of FD and CD powders, they will be particularly useful in the preparation of products where particle sensitivity is indicated, for example in smoothies, cocktails and yoghurt. The higher solubility (\(p<0.05\)) (WSI) of FD powder as compared to CD powder could result from the difference in texture of these powders. In the investigations of [PERSON] et al. (2019) [33; 34] conducted on chokeberry and blackcurrant powders using the same methods as in this study, similar results were obtained with respect to WSI and WHC, taking into account the drying method used. Table 2 shows the content of bioactive components, such as vitamin C and polyphenols, and the antioxidant activity of powders obtained from organic strawberries using the FD, CD and SD methods--the latter involving spray drying of juice on a carrier, maltodextrin. The results obtained were characterised by significantly different contents of the determined components, depending on the method of obtaining the powder. Considering the content of vitamin C, it can be concluded that both the strawberry fruit and the powders obtained from them are rich sources of this vitamin. The determined content of vitamin C in organic strawberry fruit was 0.68 mg g\({}^{-1}\) (8.07 mg g\({}^{-1}\) d.m.). This value is similar to the literature data [1,44].