esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Scheme and Sample Preparation
#### 2.5.2 Plant Cultivation Test  
Dry XG powder (with 0.00%, 0.25%, 0.5%, 0.75%, 1.00% of base soil mass) was mixed with the base soil. Then the designated amount of solution (mass of dry XG-soil mixture \(\times\) optimum moisture content) with different pH values (2, 5, 7, 9, and 12) was poured into the dry XG-soil mixture. A homogeneous wet XG-soil mixture was obtained finally through a thorough mix. The wet mixture was then placed into a cultivation box (length \(\times\) width \(\times\) height = 205 mm \(\times\) 150 mm \(\times\) 80 mm, respectively, see Figure 2a), covered with a filter paper and statically compacted with a wood panel. After compaction, the filter paper and wood panel were removed, and 160 out seeds were evenly sown on the soil surface, see Figure 2b. A thin layer of the mixture (e.g., 1 cm) was placed on top to cover the oat seeds (Figure 3). Compaction degree of 80% was adopted to take into account the requirements for both soil stability and root growth space [25].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tests** & **Pore Fluid pH Values** & **Treatment** \\ \hline Afterberg Limits & 2, 5, 7, 9, 12 & None \\ Plant cultivation & 2, 5, 7, 9, 12 & 0.00\% XG-oat, 0.25\% XG-oat, 0.50\% \\  & 2, 5, 7, 9, 12 & XG–oat, 0.75\% XG-oat, 1.00\% XG-oat \\ Direct shear & 2, 5, 7, 9, 12 & None, 0.00\% XG–oat, 0.25\% XG-oat, 0.50\% \\  & & XG–oat, 0.75\% XG-oat, 1.00\% XG-oat \\ \hline \hline \end{tabular}
\end{table}
Table 1: Soil and treatment conditions for Afterberg limits, plant cultivation, and direct shear.  
Figure 3: Soil moisture sensor in vegetated soil.  
Figure 2: (a) Culture box\({}_{z}\) (b) oat sowing.  
A soil moisture sensor was installed in the vegetated soil to measure the soil volumetric moisture content, as shown in Figure 3. The instrument uses FDR (Frequency Domain Reflected Electromagnetic Pulse) technology for measurement, which has smaller error (\(\pm\)1%), more sensitive response, and higher stability than the traditional capacitive measurement. The volumetric moisture content and the number of germinated oat seeds in the cultivation box were measured and recorded every 24 h for a period of 14 days. Watering was performed by providing designated amount of water (i.e., 70 mL) in 5 s on the 1 st, 2 nd, and 3 rd days and the 8 th, 9 th, and 10 th days after sowing to meet the moisture conditions required for the initial growth of oats and the rainfall conditions in the oat growing areas in China [25]. During the test, the indoor constant temperature was kept at 25 \({}^{\circ}\)C and the relative humidity was 75%.
### Limitations and Proposed Work  
Although deep learning networks have achieved an efficient disease diagnosis accuracy, their performance has shown some drawback which can be summarized as: (1) increasing network depth does not ensure higher diagnosis accuracy as simply stacking layers together might cause the training error to be higher [35] on top of increasing complexity and computational cost; (2) deep networks are hard to train because of the vanishing gradient problem as the gradient is back-propagated to more initial layers [35]. As a result, when training deep networks, their performance becomes saturated or even begins to degrade rapidly; (3) transfer learning has some problems related to negative learning and the automation of task mapping; (4) features extracted from deep layers lose important spatial information in the CXR images as these deep layers are either identity mappings or copies of the early layers; (5) depending on single descriptor may introduce more classification errors; and (6) most recent work has evaluated their prediction models in only healthy lung vs. either bacterial or viral infected lung CXR images.  
Therefore in this paper, our contributions can be summarized as (1) instead of using a complete deep network, we propose a light-weighted bottleneck layer feature descriptors exploiting the residual building blocks suggested in [6] and dense blocks suggested in [18] for building an improved feature descriptor which introduces no extra parameters thereby achieving low training errors and time; (2) the extracted features are reduced based on an efficient method for feature reduction, wherein 3D map of features generated for each image are shrunk into one vector using four methods; (3) adaptive score fusion with learned weights is performed using Adaboost ensemble learning that for each iteration, the decision power for prediction model weights is altered; and (4) the proposed model is examined in three scenarios which are: Normal vs. infected lungs and normal vs. bacterial vs. viral infected lungs.  
The organization of this paper is as follows: The proposed method including feature extraction and reduction, the prediction model, and the adaptive score fusion are explained in Section 2. Results and discussion are discussed in Section 3 whereas we draw our conclusion and feature work in Section 4.
## 2 Related Work
### LLM-Based In-Context Machine Translation  
The emergence of large language models has revolutionized machine translation for low-resource languages through in-context learning capabilities. [PERSON] et al. [8] first demonstrated that large language models could perform translation tasks using only a few demonstration examples in the prompt, without requiring parameter updates or fine-tuning.  
Building on this foundation, several studies have specifically investigated ICL for low-resource and endangered languages. [PERSON] et al. [23] explored few-shot learning with multilingual generative models, demonstrating effectiveness across various language pairs. [PERSON] et al. [24] provided systematic evaluation of prompting strategies for translation, establishing best practices for prompt engineering and shot selection.  
Dictionary-based approaches have shown particular promise for low-resource ICL. [PERSON] et al. [25] introduced dictionary-based phrase-level prompting, showing significant improvements when lexical information is incorporated into prompts. [PERSON] and [PERSON] [26] demonstrated effective translation of a low-resource language using GPT-3 with human-readable dictionaries, highlighting the potential of combining linguistic resources with few-shot learning.  
Recent work has specifically targeted endangered and critically low-resource languages. [PERSON] et al. [9] showed that incorporating linguistic descriptions in prompts significantly improves LLM performance on endangered languages, while [PERSON] et al. [27] explored teaching LLMs unseen languages through in-context examples. [PERSON] et al. [10] established benchmarks for learning translation from grammar books alone, providing systematic evaluation of grammatical knowledge integration in ICL.  
Grammar-based approaches have yielded mixed results. [PERSON] and [PERSON] [11] explored translation using grammar books, finding that while grammatical information can be helpful, its integration requires careful prompt engineering. [PERSON] et al. [28] conducted a comprehensive study on Mambai, demonstrating the effectiveness of retrieval-augmented prompting for extremely low-resource languages.  
However, systematic evaluation of ICL strategies specifically for Berber languages remains absent from the literature. Most existing work has focused on individual language cases without cross-linguistic analysis, and none has addressed the particular challenges posed by the morphological complexity and multilingual context characteristic of Tarifit and related Berber languages.
# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
## 7 Experimental Results
### DOA Estimation and Navigating to the BLE Beacon  
We also conducted experiments to validate the measurement method discussed in Section 6.4 at a home environment that has different layouts suitable for the experiment and is impaired by multipath radio propagation. We placed the BLE beacon in one corner of a room at a height of approximately 1 m from the ground and started the measurement process from a different room/hallway on the same floor. This section describes two experimental setups with different layouts and discusses the measurement steps required in each setup to reach the BLE beacon.
# Smart Internet of Things Power Meter for Industrial and Domestic Applications
## 4 Results  
To test the accuracy of out power meter and determine the measurement error, we implemented the setup described in Figure 4, removed the UPS and isolation transformer, and connected our device directly to the local grid. We installed the power meters and the auxiliary equipment inside a two-bay cabinet. A multiple socket outlet was mounted on the side of the cabinet to allow for the connection of different consumers. Figure 6 presents the layout of the cabinet:  
1. Industrial power meters,
2. Our device,
3. Current transformers,
4. RS485-USB two-port serial interface,
5. Raspberry Pi 3B+ computer.  
We conducted multiple tests, over a period of several days, with the aim of creating different scenarios of electrical grid loading, connecting and disconnecting various loads:  
* Incandescent bulbs (60 W, 100 W and 200 W),
* Oil radiator (heater) (2.5 kW),
* Bench grinder (250 W),
* Steam Iron (2 kW),
* Power resistor connected through a transformer,
* Faculty electronic lab workbench supply network (8 workbenches equipped with power supply, signal generator, oscilloscope, bench multimeter, desktop computer, and monitor).  
Figure 5: Protocol conversion and message flow diagram.  
During the tests, the power meters measurement data were logged in the database, plotted, and compared using Grafana. To determine the measurement error, we extracted the power meter data from the database as an average of the last 5 s values. For each data record and electrical property, we compared the value measured by our device with the values measured by each industrial meter and determined the measurement error using (6).  
\[Err_{1M}[\%]=\frac{PM_{value}-IM_{value}}{IM_{value}}\times 100\%\]  
\[Err_{3M}[\%]=\frac{PM_{value}-IM_{avg}}{IM_{avg}}\times 100\%\]  
\[IM_{avg}=\frac{JAN_{value}+PHX_{value}+SIM_{value}}{3}\]  
where  
\(PM_{value}\) represents one measurement (value) from our meter,  
\(IM_{value}\) represents one measurement (value) from an industrial meter,  
\(JAN_{value}\) represents the Janitza UMG-96 RM measured value,  
\(PHX_{value}\) represents the Phoenix Contact EEM-MA400 measured value,  
\(SIM_{value}\) represents the Siemens SENTRON PAC4200 measured value,  
\(Err_{1M}\) is the measurement error between our meter and one industrial meter,  
\(Err_{3M}\) is the measurement error between our meter and all other meters,  
\(IM_{avg}\) is the average value of the three industrial meters' measurements.
## 3 Results and Discussion
### Effect of AITC on the Smell Detection by Electronic Nose in Chicken Meat  
The results presented in Figure 4 a-c show the effect of AITC on meat and the use of E-nose to identify different meat groups. In contrast to untreated meat, the treated meat groups showed different directions for the second discriminant function when comparing the various concentrations of AITC. In addition, the E-nose appropriately differentiated between groups of meat, particularly at storage days 10 and 14. The E-nose had demonstrated overlap between the control and meat treated with AITC MIC-1, MIC-2, and MIC-4 by comparing the BAC levels and different days of storage. However, MIC-4 showed a clear tendency toward the first discriminant function on various days. In the present experiment, when the bags were opened, the meat containing MIC-1 produced a slight odor, and the odor intensity increased in the bags containing higher levels of AITC. In a study by [PERSON] et al. [31], the results were in agreement with those of the present study. They observed that meat samples treated with AITC at concentrations below 1480 ppm showed only a weak residual odor. The strong aroma of AITC is the main challenge for its application in food, and it is concentration-dependent, which has the potential to negatively affect taste perception of food [31; 35; 73]. In the present study, MIC-2 and MIC-4 also showed higher \(L^{*}\), \(b^{*}\), C\({}^{*}\), and \(h^{*}\) values, lower pH and \(a^{*}\) values, a decrease in TBARSs scores, and a decrease in numbers of _L. monocytogenes_, _S._ Typhimuriumium, and _P. lundessis_, demonstrating that the application of E-nose apparatus can be useful in classifying the chicken meat as either fresh or spoiled with a rancid flavor. In addition, it is not yet known how AITC as a natural BAC affects the organoleptic qualities (flavor and texture) of meat (whether it is raw or ready-to-eat meat) and its minimum acceptable levels in relation to oxidation of lipids and microbiological properties of meat and meat products. However, there are some limitations to the use of AITC, including its poor solubility in water, the pungent flavor and smell that is associated with isothicyanate, its instability at high temperatures, and its susceptibility to nucleophilic molecular destruction [73]. A higher concentration of AITC may, therefore, not be suitable for food preservation due to changes in flavor and odor.  
Figure 4: Effect of various AITC concentrations on odor detection by E-nose in chicken meat kept for up to 14 days at 4 \({}^{\circ}\)C. Canonical discriminant analysis score plot of (**a**): the separation based on the AITC concentration, (**b**): the separation based on storage days, and (**c**): the separation based on storage days and AITC concentration.
# The Role of Data-Driven Methodologies in Weather Index Insurance
## 1 Introduction
They found that the neural network-based solution had the optimal functional form of the insurance payouts, compared to other contracts such as linear, quadratic, and cubic piece-wise contracts; therefore, reducing basis risk. They used extensive datasets of annual corn production from Illinois from 1925 to 2018. The implementation of decision trees has proved useful in identifying important weather indices from satellite data [23] and for capturing non-linear relationships between different crop classes and low levels of precipitation, and evapotranspiration [5]. Additionally, [PERSON] et al. [24] compare the performance of neural networks and support vector machines for identifying floods and droughts from satellite data, demonstrating the superiority of both methods over a logistic regression model [24]. There are other types of studies involving machine learning algorithms in weather index insurance. These are applied to crop yield predictions. Among them are [PERSON] et al. [25] and [PERSON] et al. [26], who used neural networks to predict agricultural yields. Although Colombia has low rates of penetration of weather index insurance, successful cases show that there is a potential for this type of insurance to be extended to more productive regions [2]. In this article, we aim to implement a machine learning framework to identify the best model for capturing the intricate relationships between weather variables and a proxy for crop losses. This represents an alternative method to traditional linear modelling that is used to derive insurance contracts. This methodology is applied in the Department of Antioquia, the largest agricultural producer in Colombia [13]. We used Python and Keras to implement artificial neural networks (ANNs). Keras is a high-performance application programming interface (API) from the TensorFlow library. This paper contributes to the state of the art as the application of machine learning methods has been limited in most low-income countries. This limitation has been shown even though machine learning methods have demonstrated their potential as non-linear and flexible methods for estimating performance-index relationships [24]. Furthermore, we believe that the research in index insurance programs should be promoted in low-income economies, especially in countries with social conflicts, as is the case of Colombia.
## 6 Mobile Networks
### 5 GS Capabilities
The most important ones include Discontinuous Reception (DRX)-a terminal that is in the active state only for the data transmission; scheduler-level mechanisms, such as cross-slot scheduling (sending data in between control channel messages) or grant-free access (skipping transmission channel allocation procedures); and paging mechanisms (early paging indication and UE sub-grouping, allowing a decrease in false paging alarms), as well as mechanisms allowing for small data transmissions in the uplink while in an inactive state (in channel-access procedures) [126; 127]. **Edge computing**: Tight integration of 5 GS with edge computing platforms, such as European Telecommunications Standards Institute (ETSI) Multi-Access Edge Computing (MEC) [128], enables the offloading of efficient computations by forwarding the computationally intensive operations to the local compute units close to the RAN nodes (e.g., AI-based image and video processing) instead of transmitting the data to remote servers. In addition to considerable bandwidth savings, it enables latency reduction due to spatial proximity between UEs and edge applications.
## 4 Numerical Modeling of Connection  
### Numerical Model  
A numerical model of the plate-reinforced large CFT column to H-beam connection was constructed, as shown in Figure 4, using a finite element code, LS-DYNA [19], for evaluation of the beam, column, and connection subjected to blast loadings. All steel and concrete components were modeled using one-point quadrature eight-node solid elements. The Flanagan-Belytschko hourglass control with exact volume integration was defined with CONTROL_HOURGLASS to prevent zero energy deformation modes of elements, where the hourglass coefficient was defined to be 0.1, as suggested by [PERSON] [25].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Blast Scenario** & \(W\) **(kg)** & \(R\) **(m)** & **Target** & \(Z\) **(m/kg\({}^{1/3}\))** \\ \hline
1 & 227 & 6 & Beam & 0.98 \\
2 & 227 & 4 & Column & 0.66 \\
2 & 454 & 6 & Beam & 0.78 \\
3 & 1814 & 6 & Beam & 0.49 \\
4 & 1814 & 4 & Column & 0.33 \\
4 & 4536 & 6 & Beam & 0.36 \\
4 & 4536 & 4 & Column & 0.24 \\
5 & 11,000 & 6 & Beam & 0.25 \\
11,000 & 4 & Column & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Determination of blast loading.  
Figure 4: Numerical model of plate-reinforced large CFT column-H-section beam connection. **(a)** Beam, column and connection model; **(b)** mesh in the vicinity of connection.  
One-half of the connection was modeled, as shown in Figure 4a, to reduce the computational expense. A plane of symmetry was invoked at the center of the beam, i.e., 4 mm away from the column face. The top and bottom of the CFT column were pinned. Finer mesh with element sizes of approximately 10 to 15 mm in the vicinity of the connection was applied, as shown in Figure 4b, to reasonably predict the complex behavior around the connection. For other parts, element sizes up to approximately 70 mm were used. This mesh scheme is considered appropriate because the structural behavior of the connection model in ABAQUS was validated previously using experimental results and the numerical model in LS-DYNA was generated by importing the entire mesh itself of the ABAQUS model. The total number of elements for the numerical model was 334,084. All steel members of column, beam and reinforcing plate were connected to each other with node merging and CONTACT_TIED_SURFACE_TO_SURFACE. The composite action between steel and concrete was modeled using CONTACT_AUTOMATIC_SURFACE_TO_SURFACE.
# Locating Multiple Sources of Contagion in Complex Networks under the SIR Model
## 4 Anti-Noise
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network** & **Strategy** & **PCL** & **LPSI** & **DC** & **JC** & **UBC** \\ \hline \multirow{3}{*}{**ER**} & All-inf & 53.17 & 11.77 & 24.31 & 30.84 & 12.31 \\  & None-inf & **58.60** & 12.22 & 25.27 & 30.97 & 12.31 \\  & Rand-inf & 55.51 & 12.16 & 25.60 & 31.01 & 13.36 \\ \hline \multirow{3}{*}{**WS**} & All-inf & 47.59 & 10.24 & 18.76 & 31.12 & 12.94 \\  & None-inf & **54.28** & 10.21 & 18.16 & 31.12 & 12.55 \\  & Rand-inf & 50.11 & 10.21 & 17.92 & 30.87 & 13.09 \\ \hline \multirow{3}{*}{**BA**} & All-inf & 51.16 & 10.87 & 19.42 & 30.89 & 12.10 \\  & None-inf & **57.31** & 10.81 & 18.88 & 30.93 & 11.98 \\  & Rand-inf & 54.25 & 10.87 & 18.79 & 30.91 & 12.07 \\ \hline \multirow{3}{*}{**Karate**} & All-inf & 46.27 & 11.83 & 27.67 & 33.09 & 16.48 \\  & None-inf & **50.05** & 11.77 & 27.85 & 33.18 & 17.47 \\  & Rand-inf & 48.22 & 11.95 & 28.39 & 33.22 & 16.30 \\ \hline \multirow{3}{*}{**Lesmis**} & All-inf & 36.31 & 10.24 & 19.18 & 31.10 & 14.02 \\  & None-inf & **36.58** & 10.69 & 19.54 & 31.38 & 13.60 \\  & Rand-inf & 36.10 & 10.51 & 18.97 & 31.41 & 14.44 \\ \hline \multirow{3}{*}{**Adjnoun**} & All-inf & 53.08 & 10.69 & 22.06 & 30.81 & 12.97 \\  & None-inf & **53.56** & 10.48 & 22.57 & 30.96 & 12.19 \\  & Rand-inf & 52.24 & 10.30 & 21.52 & 30.69 & 12.13 \\ \hline \multirow{3}{*}{**Football**} & All-inf & 51.13 & 10.15 & 31.09 & 30.72 & 12.25 \\  & None-inf & **59.92** & 10.27 & 30.79 & 30.79 & 11.95 \\  & Rand-inf & 52.63 & 10.21 & 31.42 & 30.83 & 12.37 \\ \hline \multirow{3}{*}{**Jazz**} & All-inf & 38.53 & 10.24 & 19.42 & 30.34 & 11.35 \\  & None-inf & **41.62** & 10.12 & 18.13 & 30.56 & 11.50 \\  & Rand-inf & 38.59 & 10.12 & 19.03 & 30.55 & 11.35 \\ \hline \multirow{3}{*}{**USAir**} & All-inf & 37.90 & 10.12 & 13.93 & 30.38 & 11.08 \\  & None-inf & 36.04 & 10.21 & 13.45 & 30.30 & 10.90 \\ \cline{1-1}  & Rand-inf & **38.47** & 10.06 & 13.66 & 30.27 & 11.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The average precision (PCL vs. LPSI vs. DC vs. JC vs. UBC), \(\alpha=0.1:0.1:1,\beta=-1\), for different strategies of dealing with unknown infection information in synthetic networks and real networks with 20% uncertainty. The number of sources is \(s=3\) in all networks, \(<k>=4\), \(N=100\) in ER, WS and BA network, the connect probability is 0.1 in WS network. Besides, a bold number denotes the highest accuracy of sources localization in each network.
## 3 Experiments and Analysis
### Quantitative Comparison
#### 3.5.5 Other Methods (FKRW, UIUNet)  
The FKRW method and the deep learning-based UIUNet exhibit poor performance in the experiments, lagging behind other methods in all metrics across Seq.1 to Seq.6. Although UIUNet demonstrates ostensibly competitive background suppression ratios (BSF > 50) and D-statistic values (1.26 \(\times\) 10\({}^{-5}\) in Seq.4-Seq.6), this anomaly actually stems from its pathological tendency to classify all pixels as background (zero-detection), as shown in Figure 8, fundamentally revealing end-to-end networks' deficient perception capability for low-texture dim targets in complex environments.