esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Literature Review
Despite showing an increasing demand forecast, the model did not encompass future policies on the effect of greenhouse gas emissions on the country [12]. [PERSON] et al. provided an overview of Ghana's generation system and its reliance on hydropower and thermal generation. The author proposed finding alternative energy sources to reduce emissions. The Leap tool and Global Warming Potential (GWP) factors for environmental assessment confirmed theneed for more non-conventional renewable technologies. The results also showed that the current energy generation system would only meet 20% of the demand in 2040. The LEAP optimization algorithm emphasizes a supply-demand balance. Therefore, policy assumptions on capacity expansion and reserve margins were used in this study [13]. The progressive approach of optimal renewable energy to sustain economic growth by 2050 is a priority for Ethiopia's economy. [PERSON] et al. explored the business-as-usual economic scenario and policy reforms to find the least investment costs associated with the ideal generation mix. The model selected is the Open-source Energy Modeling System (OSeMOSYS) over a model horizon from 2020 to 2050. The study concluded that the country has sufficient resources yet needs new investments for 2021-2030 to have met the electricity demand from 2031 to 2050 [14]. The diversification of the energy mix in Zambia can address the variability concerns of a hydro-dependent power system. [PERSON] et al. explored capacity expansion to meet the projected demand in 2063. The selected model is the Open-source Energy Modelling System (OSeMOSYS). In addition, a FlexTool is utilized to dispatch additional capacity in a specified time. The results indicated more exploration of solar and wind resources and the growth of demand requiring timely investments and the completion of future projects for the energy demand-supply balance to be reached tin 2063 [15]. Zambia has no known petroleum deposits; therefore, it is a country that is highly impacted by the dynamic conditions of importing oil for energy use. The correlation between the cost of oil and the amount imported is a huge disparity. The cost of imports continues to increase regardless of the state of the economy. In addition, the unfavorable climate risks aversions associated with fossil fuel use. Most of the energy modeling tools show details of the end-use sectors and a representation of their electricity consumption. This has led to increased modeling tools to show the costs associated with the inclusion of technologies and their spatial scale in the market to meet relevant policy interventions. Some of the recent studies in Zambia regarding energy system modeling are presented in Table 1. The similarities with all the mentioned models are factoring in the nexus of energy demand, supply-side capacity expansion, and limitations of carbon emissions by integrating clean energy technologies, while ensuring energy supply security.
# Novel Internet of Things Platform for In-Building Power Quality Submetering
## 3 Power Quality Functionality Implementation
### Rms  
The measurement of voltage and current RMS values is carried out in the ATMega328P, by reading the ADE7758 IC internal register at a constant sampling frequency, and following the flowchart shown in Figure 6. According to the manufacturer's recommendations, and to ensure stability in the measurement procedure, the RMS measurement is synchronized with the positive voltage zero-crossing detected by a platform interrupt request (zero crossing in the voltage channel of Phase A -ZXA flag- is set in Reset Status Register -RSTATUS-), following IEC 61000-4-30. Therefore, when a zero-crossing takes place, the current and voltage RMS measurement corresponds to the previous line cycle.  
Figure 6: RMS reading process implemented in the MCU.  
As previously stated, the MCU is communicated with the IC with SPI bus communication. The communication starts when the zero-crossing flag is set in the RSTATUS register. Figure 7 contains in channel 4 a reading of the V\({}_{\text{RMS}}\) (0x0D) register, and the request seen in Master Output Slave Input (MOSI) line. The Master Input Slave Output (MISO) line corresponds to the V\({}_{\text{RMS}}\) reading, in this case, 228.4 V\({}_{\text{RMS}}\). The Serial Clock Input for the Synchronous Serial Interface (SCLK) is set to 4 kHz.
## 7 Results and Discussion
### Comparative Analysis of Client Selection Strategies  
We evaluated the effectiveness of different client selection strategies using a pool of 100 heterogeneous clients (33 smartphones, 33 edge devices, and 34 IoT). All strategies were tested on the same four datasets (MNIST, EMNIST, Fashion-MNIST, and CIFAR-10) over 300 training rounds. The performance was compared across three dimensions: model accuracy, battery consumption, and privacy degradation.  
The following strategies were examined:  
* Random selection: clients are selected uniformly at random;
* Accuracy-only selection: chooses clients based on their past contribution to model accuracy;
* Battery-only selection: prioritizes clients with the highest remaining battery power;
* Privacy-only selection: selects clients based on minimizing privacy risks;
* RBPS.
# Object Retrieval in Microscopic Images of Rocks Using the Query by Sketch Method
###### Abstract  
This paper presents the retrieval method of geological images or their fragments using Query by Sketch method. The sketch can be created manually, for instance using a graphics editor, and may show the shape of objects or their distribution within an image. This query is then used to search the image database for objects showing the greatest similarity. As an example of the proposed method, the detection of porosity in microscopic images of carbonate rock and sandstone was presented. An approach was described which is founded on the designation of parameters of selected properties of the query image and images in databases, as well as on the conformity analysis of these parameters. Two methods were proposed: the first one searches for the most similar object in the image database with respect to the set criteria. The second method performs a search based on a sketch of images which are similar in terms of object distribution (i.e., porosity). The presented research results confirm that database search using the query by sketch method forms an interesting and modern approach and may constitute one of the functionalities of IT systems intended for use in geology and mining industry.  
rock analysis; reservoir rocks features; query by sketch; image analysis; computer graphics Article
## Materials and Methods
### Wireless Communication Network
#### 2.5.1 XBee RF Module  
Two XB24 CZ7 WIT-004 XBee RF modules are used for the communication between the SU and the RTU, one as coordinator and the other as a router. The XBee RF modules use the IEEE 802.15.4 networking protocol and operate within the ISM 2.4 GHz frequency band. Besides, the modules communicate with each other by using a full mesh protocol with a data rate of 250 Kbps and a maximum number of elements of 65,536.  
The XBee RF modules communicate with each other by sending and receiving wireless messages. However, they send and receive data through a serial interface to the connected device by using the API (Application Programming Interface) operating mode. The router XBee RF module communicates with an MCU through a serial UART interface, whereas the SU communicates with the coordinator XBee RF module by using a serial module. The API operating mode requires that communication with the module be done through a structured interface, that is to say, that the data is transmitted via API frames [20]. The API frame structure consists of a start delimiter byte, two length bytes, the frame data, and a checksum byte for error detection. The API operating mode allows the RF modules to communicate with each other by using a point-to-point model (one sender and one receiver) or a broadcast model (one sender and multiple receivers), only by modifying the frame type data in the frame of data.  
The data frame from the RTU to the SU includes the heliostat ID, the RTU OS, the Bluetooth connection status, the angles of the sun position, the current angles of the heliostat, and the date and the time of the RTC. For communication from the SU to the RTU, the frame modifies the OS of the RTU, corrects the RTC data, or restarts the MCU.
# Detection of Quasi-Static Trapped Human Being Using Mono-Static UWB Life-Detection Radar
## 3 Experimental Results  
### Experimental Results for Stationary Operating Mode  
In the experiment shown in Figure 6, three human test subjects with non-periodic but non-weak respiration stood at 3, 5.5 and 10 m behind a wall, of which the thickness and the measured average dielectric constant are 24 cm and 4.93, respectively. The non-periodic but non-weak echo responses (marked by three red arrows) can be seen in the pre-processing slow-time range matrix depicted in Figure 7a. Due to the non-periodic, the vital sign features obtained by FFT (vital sign features (VSFs) marked by three red arrows in Figure 7b) are blurry, which is not suitable for the subsequent VSFs decision. However, as for the non-weak echo responses in amplitude, the proposed detection method based on the OS-CFAR method for stationary operating mode works well. As shown in the left part of Figure 8, the range locations of three human test subjects are detected and updated as slow time goes on. The conclusive range locations are determined by the online clustering algorithm (see the right part of Figure 8).
# Simulated Altitude Training and Sport Performance: Protocols and Physiological Effects
## 3 Discussion  
### Effects of Hypoxia Exposure Training on Aerobic Fitness Performance in HIIT  
After HIIT, physiological adaptations and changes in the relevant hematological variables, including erythropoietin (EPO), hypoxia-inducible factor 1 (HIF-1), levels of nitric oxide, vascular endothelial growth factor (VEGF), and transforming growth factor beta (TGF-\(\beta\)), were observed, and the inflammatory response-related cytokine (TNF-\(\alpha\)) levels were significantly increased [7].  
Exposure to hypoxia elicits an additive erythropoietin response, increasing erythrocyte volume and enhancing oxygen transport efficiency [42]. Studies have shown that hypoxiaexposure stimulates the production of erythropoietin (EPO), with longer durations of exposure leading to greater EPO increases [43]. Furthermore, a systematic reduction in hypoxia concentration during training induces physiological and structural alterations in skeletal muscle that promote oxidative processes, ultimately leading to improved athletic performance [13; 14].  
Hypoxia training in a controlled environment has been found to significantly improve cardiorespiratory fitness indicators, such as ventilation efficiency (VE), heart rate (HR), oxygen consumption (VO), maximal oxygen consumption (VO\({}_{2}\)max), blood oxygen saturation (SpO\({}_{2}\)), blood lactate levels, HR variability, hemodynamic function, and stroke volume [2; 37; 38]. Additionally, hemodynamic variables including EPO, HIF1, nitric oxide, transforming growth factor (TGF-\(\beta\)), and the inflammatory response-related cytokine TNF-\(\alpha\) have been shown to increase in response to hypoxia training [2; 37; 38].  
The benefits of hypoxia exposure training extend beyond cardiovascular improvements. They also encompass enhanced muscle perfusion, delayed fatigue during intense sprints, improved utilization of fast-twitch muscle fibers, and reduced reliance on anaerobic energy pathways [19]. However, prolonged exposure to a hypoxic environment may lead to a decrease in mitochondrial content in muscle fibers, a shift towards carbohydrate reliance for fuel during oxidative metabolism, and a decrease in intracellular lipid substrate storage [19]. HIF1, a critical regulator of gene expression related to the hypoxic response, has been found to control various genes encoding glucose transporters, glycolytic enzymes, and vascular endothelial growth factor (VEGF) [44].  
While hypoxia training has not shown promising effects on performance and physiology, further research is needed to comprehensively explore the specific effects of various hypoxia training protocols. We will address this topic in the section dedicated to training duration and athlete proficiency.
## 4 Analysis and Discussion
### Communication Layer  
In the second phase, known as the data processing phase, there are three distinct stages. Specifically, two of these stages operate within this layer, while the remaining stage is situated in the middle layer. Each stage has an algorithm; these algorithms are unique and provide solutions to an attack. We divided this phase into three stages:  
Stage 1: preregister stage. We proposed an algorithm that checks all packets from IoT devices. If any spoofing station scans/reads the packet, the target station drops the packet (it is read only once). In addition, the algorithm collects all details of a network (IP, MAC, Flage bit, and latency time) as the input. A temporary database is created to store the details  
(IP, MAC, response times for a packet) as the output. The main outcome of this algorithm is creating a temporary database with these parameters, as illustrated in Figure 10.  
Stage 2: In this stage, we wrote unique algorithms that regard temporary databases and CRPs as the input. They allow IoT devices to access the network/server to be trusted devices through CRPs. They must check whether the devices are legitimate using ID/IP devices, MAC addresses, and latency time. This phase must be used to increase security and protection. Moreover, two parameters, C and R, are used to create a trusted database on the server/cloud. This provides a solution to cloning attacks where C and R are collected and used to build mathematical methods. We can detect attacks from non-regular behavior stations, record response time, and scan packets a second time. In this case, we suggest a new thrust reverser method for a PUF, sending dummy CRPs to an attacker. At the end of this stage, we can register devices (D) in a network and build a trusted database. Figure 11 illustrates the process of registration with a focus on the registration process and the associated parameters  
Figure 11: Process of registration.  
Figure 10: Process of pre-register.
# On Channel Estimation in LTE-Based Downlink Narrowband Internet of Things Systems
## 7 Simulation Results
### Bit Error Rate (BER)  
In Figure 11a,b, we can observe the related BER comparison of the proposed technique with LS, traditional LMMSE, and its other counterpart versions with traditional and proposed interpolation schemes for TU 1 Hz and EPA 1 Hz channels, respectively. As usual, the LS estimator offers the worst, and the LMMSE offers the best BER performance. When the low-rank SVD is adopted, a minimal performance degradation of 0.80 dB is observed at \(10^{-2}\) than LMMSE in Figure 11a for the TU channel. Furthermore, in the case of banded SVD LMMSE, the performance is degraded more by about 3.3 dB because of using a low correlated channel matrix for the edge subcarriers. By using the overlap band technique with the traditional interpolation method, the optimization of the performance occurred at about 1.3 dB SNR at \(10^{-2}\). With the application of the proposed interpolation scheme, the performance is improved by about 1.2 dB more, which is almost the same as the SVD LMMSE. This is the special feature of the proposed method, which can make a good balance among performance, complexity, and power consumption which is important for NB-IoT systems. Similar performance is observed for the EPA 1 Hz channel in Figure 11b also. The received SNR of the BER at \(10^{-2}\) is recorded in Table 5 for both TU and EPA channels for better understanding and comparison. From Table 5, we can see that an additional power of 1.2 dB and 0.9 dB can be saved with the application of proposed interpolation in the proposed overlap banded SVD LMMSE method while exploiting TU 1 Hz and EPA 1 Hz channels, respectively. In summary, the negligible performance loss of the proposed overlap banded SVD LMMSE with the proposed polyphase decomposed FIR filter interpolation method from the optimum LMMSE is only 0.8 dB and 0.9 dB for TU 1 Hz and EPA 1 Hz channels, respectively, which is acceptable in the case of the NB-IoT network. This simulation is obtained by considering twenty (S = 20) subvectors for the case of overlap banded SVD LMMSE. Figure 12a,b show the BER performance of the proposed overlap banded LMMSE channel estimation technique with traditional and proposed interpolation methods for different subvector values (i.e., S = 10, 20, 30, and 40) to determine the optimum operating region of the channel estimator for TU 1 Hz and EPA 1 Hz channels, respectively. It is observed that the BER performance increases up to a level for a certain number of increased subvector values and the overlap banded SVD LMMSE method always performs better with the proposed interpolation method compared to the traditional interpolation technique.  
Figure 11: Bit error rate (BER) comparison of the proposed technique for conventional and proposed interpolation methods with traditional LMMSE and its other counterpart versions: (**a**) TU 1 Hz; (**b**) EPA 1 Hz.
## 3 Materials and Methods
### Optical Remote Sensing: Sentinel-2  
The detailed analysis with high-resolution data has been approached using Sentinel-2 images. This satellite constellation is composed by two platforms that are active since 2015 and 2017 in the framework of the European Copernicus EO Programme [38]. The Copernicus Open Access Hub ([[https://sciub.copernicus.eu/](https://sciub.copernicus.eu/)]([https://sciub.copernicus.eu/](https://sciub.copernicus.eu/))) provides L1C data that must be converted from top-of-atmosphere to bottom-of-atmosphere reflectance. The analysis has been developed selecting cloud-free images and processing those data with the Sen2 Cor toolbox [39]. The atmospheric correction process required some input data that we retrieved from the AERONET network [PERSON] et al. [40]. Considering the available values of the 500 nm aerosol optical depth (<0.1) at Thule and Ittoqortoormitit (since there are no available station in the study area), we used as input data a visibility of 40 km. The total columnar ozone content was selected referencing to [PERSON] et al. [41] and we used as input values 400 and 300 DU, respectively for April 2017 and July 2017, The aerosol type was selected to be maritime and the atmosphere type as mid latitude winter, following the indications of Konig M and N [42]. The preparation of a L2A final product, with 10 m resolution and surface reflectance, has provided information about the spectral optical behaviour (12 bands with wavelengths ranging from 440 nm to 2200 nm) of the surface at selected dates characterized by a low cloud coverage. The Sentinel-2 bands are distributed as follows: B1 at 443 nm, B2 at 490 nm, B3 at 560 nm, B4 at 1665 nm, B5 at 705 nm, B6 at 740 nm, B7 at 783 nm, B8 at 842 nm, B9 at 945 nm, B11 at 1614 nm, B12 at 2202 nm. We defined for the next analysis two normalized difference indexes useful for enhancing the different contribution of visible, near-infrared and short-wave infrared wavelength domains.  
\[Index1 =(B3-B8)/(B3+B8)\] \[Index2 =(B8-B11)/(B8+B11)\]
# Design of PAM-8 VLC Transceiver System Employing Neural Network-Based FFE and Post-Equalization
## 6 Conclusions  
A PAM-8 VLC transceiver system composed of digital basebands, AFEs, an LED model, a PD model, and channel models was proposed and implemented. The limitations in VLC systems caused by the intrinsic characteristics of LEDs and channels were systemically studied and could lead to bandwidth limitation, such as ISI, and path loss. Based on the analysis of ISI and signal loss, a VLC transceiver system that combined an NN-based FFE, passive equalizers, and an RBF-NN-enabled post-equalizer to extend the limited bandwidth was proposed. The whole VLC system was implemented in a co-simulation platform consisting of MATLAB and Cadence. MATLAB was utilized to build these digital basebands, the DAC, and the ADC. The designs of the AFEs with the LED and PD device models were implemented in Cadence. In addition, both free-space and underwater channel models were established and utilized for the simulation to prove that the proposed system could work with different channel models. The data transmission performances of using the free-space model and the underwater channel model were also analyzed and compared. To verify the feasibility and evaluate the performance of the VLC system, simulations on the data rates of signal transmission versus the BER with the RBF-NN or a DNN as post-equalization schemes were conducted, and the training time was also studied. The simulation results indicated that the RBF-NN-enabled VLC transceiver system could achieve a data rate of 3.6 Gbps while taking less than 10 min of training time.  
Conceptualization, C.P.Y. and B.X.; methodology, B.X.; validation, B.X. and T.M.; writing--original draft preparation, B.X.; writing--review and editing, C.P.Y. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Foshan-HKUST Project under the Government of Foshan Municipal City (grant number FSUST20-SHCIRI05C); the Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone (grant number HZQB-KCZYB-2020083); the Hong Kong Research Grants Council under the General Research Fund (GRF) project (grant number 16215620); and the HKUST-Qualcomm Joint Innovation and Research Laboratory.  
Not applicable.  
The authors declare no conflict of interest.
## 2 MAC Design Issues in CRBANs
### Energy Efficiency  
There are several attributes to be considered for the design of an effective, reliable and energy-efficient MAC protocol. The main goal is to achieve energy efficiency. In CRBANs, all sensors and actuators are operated by a small battery. For long-term operation, it is necessary to minimize energy dissipation. To achieve this goal, energy-aware communication protocols need to be designed. In general, energy efficiency can be achieved by controlling and minimizing waste. Research [11; 12] has shown that the main source of energy wastage is data collisions, overhearing, packet overhead, traffic fluctuation and idle listening.
# Geophysical Study of a Large Landslide Affecting the Urban Area of Albuuuelas (S Spain)
## 4 Data Acquisition and Analysis
### f-k Experiment  
Seismic noise array measurements have been analyzed through the _f-k_ technique, providing an estimation of the Rayleigh wave-phase velocity dispersion curve.  
In the present study, 2D arrays were deployed at seven different sites along the landslide. The equipment used consisted of twelve 4.5 Hz vertical geophones connected to a multichannel seismic recorder (SmartSeis ST), which allows for the retrieval of dispersion curves above approximately 2 Hz [60; 61]. The geophones were deployed following a circular or rectangular layout with a maximum aperture of 16-20 m, depending on the available space. Considering that the wavelengths estimated by the _f-k_ method can be several times greater than the maximum aperture [55; 62], these configurations would be sufficient for soil classification purposes [63]. The recording duration was 30-45 min with a sampling frequency of 500 Hz, although it was subsequently resampled at 100 Hz.  
The data analysis was performed using Geopsy software (www.geopsy.com). The raw signals were previously processed in the same way as indicated for the H/V analysis (Section 4.2).