esa-sceva/satcom-chunk-collection huggingface dataset:
# Mn(II) Complexes of Enlarged Scorpiand-Type Azamacrocycles as Mimetics of MnSOD Enzyme
## 3 Results
### SOD Mimetic Activity  
The SOD mimetic activities of the Mn\({}^{2+}\) complexes of L1-L3 were tested in vitro in a bacterial model consisting of two strains of _E. coli_: a wild-type strain and an SOD-deficient mutant. The SOD-deficient strain can only grow at about 20% of the rate of the wild-type strain when cultured in a restricted media in the presence of oxygen at pH 7.4. However, an SOD-mimetic compound able to enter the bacterial cytoplasm could compensate for the SOD deficiency and increase the growth rate [32]. All three compounds showed a protective effect in the SOD-deficient strain, increasing its growth with a maximal recovery rate of about 75% for L1/Mn\({}^{2+}\), 65% for L2/Mn\({}^{2+}\) and 45% for L3/Mn\({}^{2+}\) at 20 uM (Figure 5). Out of the three compounds, only L3/Mn\({}^{2+}\) slightly reduced, by about 20%, the wild-type growth rate. In this sense, also L3/Mn\({}^{2+}\) in the SOD-deficient strain showed less growth at  
Figure 4: Cyclic voltamperogram of the L1/Mn\({}^{2+}\) system. Sweep speed = 10 mV/s.  
the highest concentrations (10 and 20 \(\upmu\)M) in comparison with lower concentrations (1 to 5 \(\upmu\)M). Based on these results, L1 and L2 complexes seem the best candidates for future studies regarding SOD-mimetic activity.  
Control experiments with the complexes were performed against the wild-type strain to test the toxicity at the highest concentration. As can be seen in Figure 5, only L3/Mn\({}^{2+}\) lowers the rate of growth to a significant extent (about \(-20\%\)). Control experiments with the free ligand and the mutant strain showed no significant increase of the growth rate at the highest concentrations. The possible effect of the free Mn\({}^{2+}\) was considered negligible at those concentrations based on previous experiments.
## 6 Stability Analysis of Surrounding Rock Based on Refined Modeling
### Analysis of Stress Evolution  
#### 6.4.1 Stress Analysis of KZ1 Profile  
As shown in Figure 20, under the combined mining conditions of the two sublevels, an unloading zone is formed, and the compressive stress in the fill bodies is relatively low, indicating that the stability of the fill bodies in the first and second slopes of the 220 m Sublevel is relatively good. The pillars, inter-pillar areas, and isolation pillars are primarily compression zones, with the isolation pillars bearing greater compressive stress. When no isolation pillars are set, the 340 m Sublevel isolation pillar becomes the main compression zone, dividing the unloading zones above and below 340 m. When the isolation pillars are set, and as their thickness increases, the average compressive stress of the isolation pillars between Phase I and Phase II gradually decreases, and stability gradually improves. However, stress concentration zones are likely to form at the junctions of the isolation pillars and the ore body faults, causing plastic deformation and reducing the stability of the isolation pillars. It is necessary to strengthen stress and deformation monitoring around stopes, pillars, and faults.  
The minimum and maximum principal stress ranges for the KZ1 profile were summarized, and it was found that with the increase in the thickness of the isolation pillars, the overall range of the minimum and maximum principal stresses decreases, and the maximum values of compressive and tensile stresses also decrease. Additionally, from Figure 20, it can be observed that the region of tensile stress distribution is reduced, and the area of high-stress distribution also shrinks. From the perspective of stress evolution, the stability of the mine rock mass has significantly improved. The summarized results are shown in Table 6.  
Figure 20: Stress evolution analysis of KZ1 profile. (**a**) Minimum principal stress with 0 m isolation pillar; (**b**) maximum principal stress with 0 m isolation pillar; (**c**) minimum principal stress with 10 m isolation pillar; (**d**) maximum principal stress with 10 m isolation pillar; (**e**) minimum principal stress with 20 m isolation pillar; (**f**) maximum principal stress with 20 m isolation pillar.
# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 3 RQ 1 and RQ 2--Current Challenges and Recommendations to Enhance BIoT Integration
### BC-Based Technical Considerations
#### 3.3.8 Learned Lessons  
According to our study, various elements can cause technical challenges, including communication and bandwidth, cryptographic mechanisms and algorithms, heterogeneity of IoT devices, and smart contract architecture. Despite several solutions provided to address these challenges, mining remains a major issue in the IoT due to its limitations. A smart contract code is typically not upgradeable; furthermore, more BC nodes will increase exponentially in the near future limiting the bandwidth, and BC is built on the assumption of steady connections, which is not feasible for IoT, which frequently has poor network connections.
## Materials and Methods
### Input Datasets
#### Evapotranspiration and Interception  
The ETla in the WaPOR portal is based on a modified version of the ETLook model (ETLook-WaPOR) [40]. The ETLook model uses Penman-Monteith (PM) to estimate ETa adapted to remote sensing input data [36]. The PM approach uses the combined approaches of the energy balance equation and the aerodynamic equation and is described in the FAO-56 drainage paper [41]. The ETla defines soil evaporation and transpiration separately using Equations (1) and (2). The interception is a function of the vegetation cover, leaf area index (LAI), and precipitation. The ETI-WaPOR is then calculated as the sum of evaporation, transpiration, and interception.  
\[\lambda E=\ \frac{\Delta\Big{(}\ R_{n,soil}-\ G\ \Big{)}+\ \frac{\rho_{air}\ C_{p}(e_{s}-e_{s})}{\tau_{Air, soil}}}{\Delta\ +\ \gamma\ \Big{(}1+\frac{\tau_{Air, soil}}{\tau_{Air, soil}}\Big{)}} \tag{1}\]  
\[\lambda T=\ \frac{\Delta\Big{(}\ R_{n,canopy}\Big{)}+\frac{\rho_{air}\ C_{p}(e_{s}-e_{s} )}{\tau_{Air, canopy}}}{\Delta\ +\ \gamma\ \Big{(}1+\frac{\tau_{Air, canopy}}{\tau_{Air, canopy}}\Big{)}} \tag{2}\]  
where \(E\) and \(T\) are the soil evaporation and plant transpiration rates in (kg.m\({}^{-2}\).s\({}^{-1}\)), respectively, and \(\lambda\) is the latent heat of vaporization (MJ/kg). Rn of the soil (R_n,soil_) and canopy (R_n,canopy_) is the net radiation (MJ/m\({}^{2}\)/day) and \(G\) is the ground heat flux (MJ/m\({}^{2}\)/day). \(\rho_{air}\) is the density of air (kg/m\({}^{3}\)), C\({}_{p}\) is the specific heat of air (MJ/kg\({}^{\circ}\)C), (e\({}_{s}-e_{a}\)) is the vapour pressure deficit--VPD (kPa), \(r_{a}\) is the aerodynamic resistance (s/m), \(r_{s}\) is the soil resistance (s/m), or canopy resistance when using the PM-model to estimate evaporation or transpiration respectively. \(\Delta=\) d(e\({}_{sat}\))/dT (kPa/\({}^{\circ}\)C) is the slope of the curve relating saturated water vapour pressure to the air temperature, and \(\gamma\) is the psychometric constant (kPa/\({}^{\circ}\)C). This approach partitions the WaPOR ETla to evaporation and transpiration using the modified versions of PM, which differentiate the net available radiation and resistance formulas based on the vegetation cover according to the ETLook model [40]. A major difference between ETLook-WaPOR and ETLook is the source of remote sensing data for the soil moisture. In the original ETLook, soil moisture is derived from passive microwave, and in the WaPOR approach, soil moisture is derived from LST.  
Interception is the process where leaves intercept rainfall and is evaporated before it reaches the soil surface. Intercepted rainfall evaporates directly from the leaves and requires energy that is not available for transpiration. Interception (mm/day) is a function of the vegetation cover, LAI, and precipitation.  
\[\lambda I=0.2\ LAI\Bigg{(}1-\frac{1}{1+\frac{\ F_{c}P}{0.2\ LAI}}\Bigg{)} \tag{3}\]  
\(Fc\) (-) is the vegetation cover calculated from NDVI, and \(LAI\) (-) is the leaf area index converted from \(Fc\). \(P\) is precipitation (mm/day).
## 2 Materials and Methods
### Ensemble of Deep Convolutional Neural Networks
#### 2.4.1 Preprocessing VLE  
In order to guide the network towards a better convergence, and similar to single-frame analysis (Section 2.3.1), we performed some specific cleaning by removing non-informative areas from the original VLE images. This removal involved two main sources of less important information: (1) the pixel background information, and (2) the balloon pixel information. Our aim was to maximize the useful information that the network can learn and exclude any additional learning that degrades the classification task. For this reason each image was cropped to occlude the balloon. For each VLE image, the balloon region (Figure 1, red curve) was removed following the steps below:  
1. For each image the average intensity curve was computed along the vertical dimension, thus allowing us to obtain the profile of changing intensity (Figure 1, cyan curve).
2. The first derivative of the average intensity curve was computed to obtain a quantitative measurement of slope differences (Figure 1, yellow curve).
3. Given the first derivative, the balloon end location was defined as the local maximum value found after the minimum point of the first derivative (Figure 1, green star).  
Figure 1: (Best viewed in color) Example of preprocessing applied to each volumetric laser endomicroscopy (VLE) frame. At the left side of the image the balloon line (red line) is located by calculating the average intensity of the whole image (cyan line) and then using the first derivative (yellow line) to extract the end point of the balloon pixel (green star). Scale bars: 0.5 mm.
# Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm
## 4 Proposed Method  
In the ANC, the noise will be attenuated by generating another noise known as anti-noise; having an equal amplitude and opposite in phase with the original noise. The work developed in this paper is the novel ANC to reduce the noise. This noise control is done through the MFsLMS algorithm. The stability of this system has been improved through the HPSOFF algorithm. For the proposed mechanism, a multichannel ANC system is considered. Generally, in a single channel system, a reference microphone, of one in number, is used with a loudspeaker and an error microphone. In contrast, multichannel ANC system uses 'L' number of reference microphones, 'N' number of error microphones and 'M' number of loudspeakers.  
Figure 1 shows the schematic of the multichannel ANC system that has been designed by using the MFsLMS algorithm. Here, the modification of FsLMS algorithm is given through the HPSOFF algorithm by changing the functional expansion.
# A Cloud Microservices Architecture for Data Integrity Verifiability Based on Blockchain
## 9 Discussion and Conclusions
### Discussion  
Table 8 shows, with a \(\checkmark\), the main characteristics supported in the works [35; 36] and our proposal model. We coincide in both works in prevailing characteristics of data integrity, blockchain technology, auditing and traceability services, and implementing the microservice architecture. However, reference [35] focuses exclusively to supply chain systems resulting in very bounded implementations, so, in this work, we decide to include a more generalized solution for cloud systems. In contrast with [35], our proposal starts with the security suggestion of [36] concerning the auditing mechanism from when a Root user is created in a decentralized environment.  
On the other hand, we propose a decentralized architecture that extends both works [35; 36], presenting a general solution for cloud computing services; and considering  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Cloud** & \multicolumn{2}{c}{**Latency from a Cloud Supply Chain**} & \multicolumn{2}{c}{**HTTP**} & \multicolumn{2}{c}{**Processing Time**} & \multirow{2}{*}{**Description**} \\
**Schain** & **Start** & **End** & **Time (ms)** & & & & **Start** & **End** & **Time (ms)** & \\ \hline \hline Farmer & 1645057568522 & 1645057575176 & 6654 & POST & 1645057575177 & 164505757016 & 1839 & Creating a Farmer \\ \hline \hline Farmer & 1645057572394 & 1645057575191 & 2797 & GET & 1645057575192 & 1645057576297 & 1105 & Consulting a log \\ \hline \hline Carrier & 1645057569135 & 1645057575901 & 6766 & PUT & 1645057575902 & 1645057577691 & 1789 & Updating information \\ \hline Carrier & 1645057574510 & 1645057576801 & 2291 & GET & 1645057576802 & 1645057577797 & 995 & Consulting a log \\ \hline Packers & 1645057570272 & 1645057576902 & 6630 & DELETE & 1645057576903 & 164505758528 & 1625 & Deleting information \\ \hline Packers & 1645057574996 & 1645057577904 & 2908 & GET & 1645057577905 & 1645057578795 & 890 & Consulting a log \\ \hline Merchant & 1645057570477 & 1645057577011 & 6534 & POST & 1645057577012 & 1645057578832 & 1820 & Creating a Merchant \\ \hline Merchant & 1645057575175 & 1645057577365 & 2190 & GET & 1645057577366 & 1645057578474 & 1108 & Consulting a log \\ \hline Consumer & 1645057576367 & 1645057578762 & 2395 & GET & 1645057578763 & 164505759562 & 799 & Consulting a log \\ \hline \hline \end{tabular}
\end{table}
Table 7: Transaction latency from a Cloud Supply Chain and processing time in the Event API-Gateway.  
the creation of several types of users and roles; respectively, making scalable and modular the audit mechanism proposed. Thus, we can integrate more services through smart contracts (as shown in Figure 4) and traceability with the processing time and latency shown in Section 8.3. A critical remark is that we can provide information by consulting the log stored in the blockchain provided by the smart contracts about _how_ an event was generated, _who_ generated it, _what_ included the event, _where_ was it generated, and _when_ was the event generated.
# An Interoperable Component-Based Architecture for Data-Driven IoT System
## 4 Use Case and Implementation
### Implementation
Users can select or update the algorithms without the need to manually reassign the data to the newly-selected model or to change the data input format, as the data-driven models react to the same set of input data. Furthermore, under the same data-driven model, the assignment of the different algorithm can be done. For instance, user can assign P&O algorithm for PV_1 and InCond algorithm for PV_2, and vice versa. After going through the MPPT calculation, the feedback signals are transmitted back to the end devices to be applied on the solar PV's DC-DC converter to update the controller's action. The program then continues its iteration to read the next data set to decide the upcoming feedback action. * External data input handler: The external data input handler is in charge of data curling from any website on the Internet through API. For our use case, we import external weather data from the _OpenWeatherMap_ as a reference. The _OpenWeatherMap_ is an online service that provides weather data, such as daily weather forecast and historical data. The data provided are available in JSON format and accessible through free API. However, only certain data (current weather data and five-day/3-h forecast) are available for free. For experiment purpose, we only collected these data, stored them in _InfluxDB_ on a daily basis, and visualized the data on _Grafana_.
# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 2 Background
### Handover Decision in Smart Cities  
Smart cities require very dependable and flexible network connectivity because their infrastructure must support autonomous transport systems, IoT devices, and digital services in real time. In such environments, handover decisions must consider multiple parameters, including signal strength, network congestion, estimated movement trajectories, and even the weather. In smart cities, AI-driven centralized systems processing data streams from millions of devices can control handover procedures (Figure 5). This approach works to reduce network overload, make traffic routing more efficient, and ensure a stable connection in high-density user environments [39].  
Figure 4: Handover decision algorithms.
# Channel Access in Wireless Smart Grid Networks Operating under ETSI Frame-Based Equipment Rules
## 5 Performance Analysis
### Homogeneous Networks
However, changing the number of contending nodes without parameter optimization leads to significantly reduced performance. In the case of standard FBE, fixed-muting FBE, and random-muting FBE the main factor which determines this decrease is the periodicity of fixed frames. For non-optimized parameters, it is easy to cause overlapping of FFPs, which then leads to the synchronization issue depicted in Figure 2a and either constant collisions or, in the best case, only occasional transmissions. Floating FBE suffers from the problem of single-node domination, which is proven by unacceptable values of mean channel access delay and fairness, while also increasing channel efficiency. It turns out that DB FBE, greedy-enhanced FBE, and BITR FBE achieve similar results as in the optimized case, but with a visible decrease in channel efficiency and an increase in mean channel access delay. However, they maintain a high value of fairness. The worst performance among the backoff-based variants is achieved by enhanced FBE, where for 32 nodes, the value of mean channel access delay almost doubles. In summary, these results show that improper selection of FBE parameters can cause serious channel access problems. Any additional node is able to desynchronize all others and reduce overall performance. We conclude that for these variants, both parameter and _shift_ optimization are required for each new node joining the channel access contention. Such optimization could be performed by an external controller, responsible for monitoring and reconfiguring nodes operating in the same channel. Figure 12: Results for homogeneous networks under optimized parameters. Figure 13: Results for homogeneous networks under non-optimized parameters.
# Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks
## 4 Optimized-AODV Protocol Topologies and Testbed Design
### How the Optimizations Were Achieved
#### 5.1.4 Application Layer Optimization  
Because the application layer is critical for the compilation of the results, we improved the periodic updates function for printing status messages. Furthermore, we optimized the bt_mesh_cfg_srv function used in the Zephyr RTOS to modify various parameters required for our experimentation, such as enabling the relay, TTL value, GATT proxy, and so on, to improve application performance in accordance with our experimental requirements.  
Figure 6: Message passing diagram with the AODV layer.  
Subsequently, Figure 7 depicts the summary of the acquired optimizations (obtained with the help of the aforementioned hardware and software) that resulted in better and more efficient results. To begin, this research concentrated on optimizing the application layer to achieve the desired output, as well as printing the messages to the PuTTY terminal. In addition, we enabled some of the capabilities of the configuration server model function and streamlined the periodic update function to obtain the desired results. In addition, as indicated in Figure 7, changes were made to the AODV layer functions. Following that, to address the issue of message retransmission, the mesh network layer functions such as bt_mesh_net_send and bt_mesh_net_recv were enhanced. In addition, the controller configuration was altered, as shown in Figure 7, to ensure that the protocol ran smoothly as per the requirement.
# On the Use of Learnheuristics in Vehicle Routing Optimization Problems with Dynamic Inputs
## 5 Computational Experiments
### Computational Results  
The results of our experiments comparing the static and dynamic approaches are summarized in Table 1. It is structured as follows. The first three columns refer to the instance considered (name, number of nodes, and capacity of the vehicle). The next four columns describe the forecasted and the real costs for both approaches. Then, the number of routes for the solutions of each approach is revealed. Finally, the last three columns represent the gaps with respect to the DRC. The average values of each gap are shown in the last row.  
\begin{table}
\begin{tabular}{r r r r r r r r r r r} \hline \hline
**Instances** & \multicolumn{5}{c}{Cost} & \multicolumn{5}{c}{\# Routes} & \multicolumn{5}{c}{**Gap**_uv.r.t._ **DRC (\%)**} \\ \hline
**Name** & **\# Nodes** & \(vCap\) & \multicolumn{1}{c}{DFC} & \multicolumn{1}{c}{DRC} & \multicolumn{1}{c}{SRC} & \multicolumn{1}{c}{_SFC_} & \multicolumn{1}{c}{_Dynamic_} & \multicolumn{1}{c}{_Static_} & \multicolumn{1}{c}{_DFC_} & \multicolumn{1}{c}{_SRC_} & \multicolumn{1}{c}{_SFC_} \\ \hline A-n32-k5 & 35 & 100 & 864.04 & 863.05 & 871.49 & 816.73 & 5 & 5 & 0.11 & 0.98 & \(-\)5.37 \\ A-n38-k5 & 4
## 1 Introduction  
Orthogonal frequency division multiplexing (OFDM) is a leading multicarrier modulation scheme with the capability to combat multipath fading distortions and support high data rates. Due to its appealing features, OFDM has been considered as the standard multiplexing technique for many international digital television terrestrial broadcasting (DTTB) systems, such as the advanced television standards committee (ATSC), digital television/terrestrial multimedia broadcasting (DTMB), digital video broadcasting-terrestrial-second generation (DVB-T2), and integrated services digital broadcasting-terrestrial (ISDB-T) [1; 2; 3; 4]. The ISDB-T system is designed to support reliable reception and commonality for high-definition television (HDTV) services using MPEG-4 advanced video coding (MPEG-4 AVC) over terrestrial networks [5]. One special feature in the ISDB-T system is the combination of band segmented transmission (BST) and OFDM, which is known as BST-OFDM. Using different modulation and coding schemes (MCS) in one or more OFDM segments forms the basis of hierarchical transmission [4; 5; 6]. With the use of the segmented structure and hierarchical partition, different modulation modes, such as coherent and differential modulation, can be applied to different OFDM segments. More specifically, ISDB-T is based on the transmission of MPEG-4 compressed video data containers, which can be used to transmit a flexible mixture of compressed video, audio, and other data services.  
Despite its many excellent benefits, OFDM has some principal problems that need to be overcome. One of the major disadvantages of OFDM is its vulnerability for frequency misalignment, which can be introduced by the Doppler shift and oscillator instabilities [7]. To fully utilize the appealing features of OFDM, it is crucial for the receiver to reliably perform time and frequency synchronization, which usually includes estimation of symbol timing offset (STO), carrier frequency offset (CFO), and sampling frequency offset (SFO). In OFDM, the STO can result in inter-symbol interference (ISI) and a rotated phase, of which value is proportional to the subcarrier index. On the other hand, the CFO can be split into integer and fractional offsets, each influencing the system in a different way. If not correctly detected and removed, the fractional CFO (FFO) gives rise to the imperfection of the orthogonality among the subcarriers, leading to the inter-carrier interference (ICI), while the integer CFO (IFO) produces a cyclic shifting of all subcarriers in the frequency domain [8]. To address this issue, a lot of estimation methods have been studied for the estimation of the FFO and the IFO [9; 10; 11; 12; 13; 14; 15; 16; 17; 18].