esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optical Properties
### Lateral Carrier Diffusion in QWDs
At room temperature, the carrier concentration in the continuum states is enhanced, and the PL intensity of QD structure drops by about two times when the mesa diameter decreases down to 0.2 \(\upmu\)m. In the case of mesas with QWD, the photoluminescence intensity (both at 77 and 290 K) exhibits faster deterioration with decreasing the mesa diameter as compared to QDs. This is explained by the fact that QWDs provides weaker carrier localization. The smallest mesa size showing detectable PL was 1 \(\upmu\)m (at room temperature) or 0.3 \(\upmu\)m (at liquid nitrogen). In the case of mesas with QW, photoluminescence was observed only in 6-10-\(\upmu\)m in diameter mesas, whereas in the mesas with smaller sizes the luminescence was not detected. We conclude that carrier diffusion lengths in QWD structures are much shorter than in QW structures, which is in agreement with the suppressed carrier diffusion in disordered QWs [52]. This makes QWDs very advantageous for their use as an active area in compact nanophotonic devices (this will be discussed in more detail in Section 3.5).
Up to now, the most effective approach for battery monitoring was to apply advanced estimation algorithms based on equivalent circuit models. A new cascaded framework for lithium-ion battery state and parameter estimation, which separated the battery equivalent circuit model parameters and battery capacity estimation, was proposed based on theoretical analysis in [9]. Apart from providing a simultaneous estimation of battery open-circuit voltage, more rapid and less fluctuating battery capacity estimation were the main advantages of this new proposed monitoring structure. In [10], a completely different approach to model Lithium-Ion batteries was presented that did not require any prior knowledge of these batteries or theoretical analysis. It was based on training recurrent neural networks, composed of two short-term memory layers and one dense layer with processed data of measurable physical features--current, voltage, and temperature. The proposed battery model could be used in real-time applications, as the model inputs are physically measurable parameters, allowing for the possibility of simple and accurate implementation in battery management systems. Last but not least, other works that presented results that came to answer some thought-provoking questions related to hybrid electric vehicles and their energy management systems. On the one hand, the effects of the driving torque and capacity (energy density) of the lithium-ion battery on the fuel economy of a parallel hybrid vehicle were studied in [11]. The following conclusions regarding the state of charge characteristics and current balance of the lithium-ion battery were drawn: when the battery capacity is reduced by 20%, by using a variety of strategies (e.g., downsizing, engine operation strategy, state of charge increase) to compensate for the state of charge drop, it will be possible to operate hybrid electric vehicles at lower battery capacities; for efficient battery use, state of charge must be charged to near 50%. On the other hand, a systematic literature review of the more recent works that developed energy management systems for hybrid electric vehicles was presented in [12]. The review was carried out subject to the following idea: although the development of novel energy management systems that seek the optimum performance of hybrid electric vehicles is booming, in the real world, hybrid electric vehicles continue to rely on well-known rule-based strategies. The contribution of this work was to present a quantitative comparison of the works selected. It was concluded that the improvement in the analyzed energy management systems ranges, roughly, between 5% and 10% with regard to commercial rule-based energy management systems and, in comparison to the optimum, the analyzed energy management systems are nearer to the optimum than commercial rule-based energy management systems. This research received no external funding. The author declares no conflict of interest.
# Privacy Policy Analysis of Banks and Mobile Money Services in the Middle East
## 2 Background
### Mobile Money Services  
Mobile money or mobile-phone-based financial service is a service that allows users to spend, store, or receive money using an account stored on their cell phones. Mobile money services differ from phone-based access to mobile banking (e.g., Bank of America mobile app) and mobile payment services (e.g., Google Pay, PayPal) in terms of targeted customers, services providers, and technology for supporting these services [9].  
Mobile money offers services to unbanked users as well as customers who have a bank account for some apps (e.g., STC Pay as shown in Figure 3), whereas both mobile payment and mobile banking target only their existing customers. In addition, mobile money is often provided by telecommunication providers or local financial regulations, whereas both mobile payment and mobile banking are run by financial regulators. Mobile money services are supported by mobile Internet, SMS, or Universal Supplementary Services Data as part of phone's configuration, whereas both mobile payment and mobile banking are activated only through mobile Internet.  
According to the GSMA [15], two billion unbanked people in Middle East and North Africa do not have access to financial services. However, the gradual increase in usage of mobile money is providing payment services to low-income populations in the developing countries such as Middle East and North Africa since banks do not provide services to customers with low balances. The Kenyan mobile money app, M-Pesa, was deployed by SafariCom and was introduced for this purpose in March 2007 [16]. The recent statistics [17] show that more than 20% customers in the Middle East and North Africa perform transactions through smartphone apps that run the country's mobile phone services. For instance, the Arab States of the Gulf (e.g., Qatar and Saudi Arabia) [18; 19] have many immigrant workers who make international remittances through mobile money apps in an easy and secure way.  
Figure 2: A screenshot of STC Pay privacy policy.
# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 3 Proposed Method
### Feature Selection
```
1:\(x\) is the standardized and mean encoded datasets; \(y\) is the target variable on datasets
2:Output:\(\mathscr{s}f\) is the selected features' sets
```
3:[Evaluation Function]: Internal function to evaluate the performance of a given set of features for each model
4:define model dictionary \(M\) (\(M\) = (LGBM, XGBoost, and CatBoost))
5:function evaluate_features (\(\mathscr{s}\), \(x\), \(y\)):
6:initialize best score \(b\) as 0.0
7:for each model in \(M\)do
8: score \(\leftarrow\) mean cross-validation score for model using feature sets \(\mathscr{s}\), \(x\) and \(y\)
9:if score > \(b\)s
10:\(b\)s \(\leftarrow\) score
11:endfor
12:return\(b\)s
13:endfunction
14:[Forward Selection]: Iterate until no more features can be selected from full set of features
15:initialize forward-selected feature set \(\mathscr{f}\)sets, and best global score \(b\)gs as 0.0
16:set remaining feature set \(\mathscr{f}\)ists as full set of feature lists
17:while\(\mathscr{f}\)ists is not empty:
18:initialize bestscore as 0.0
19:for each feature \(\mathscr{f}\) in \(\mathscr{f}\)ists do:
20: current feature set \(\mathscr{f}\)ist \(\leftarrow\)\(\mathscr{f}\)set \(\mathscr{f}\)
21:\(score\)\(\leftarrow\) call function\(evaluate\_features\) (\(\mathscr{c}\)list, \(x\), \(y\))
22:if score > bestscore:```
19:_bestscore \(\leftarrow\)score, and \(\mathit{bf}\leftarrow\) feature \(f\)
20:endfor
21:if bestscore > bgs:
22:\(\mathit{bgs}\leftarrow\)bestscore, and \(\mathit{fsets}\leftarrow\)/\(\mathit{fsets}\) + feature \(f\)
23:remove \(\mathit{bf}\) from flights
24:else
25:break
26:endwhile
27:Ierate until no more features can be removed from full set of features
28:set backward selected feature set \(\mathit{bsets}\) as full set of features
29  
important features. Clinical variables related to patient demographics and vital signs, such as _AGE_ and _HRI_, were more important than those associated with underlying diseases. In Group 4, 16 of the 26 variables were selected as important features. Clinical variables related to blood test results, especially _WBC_ and _LYMPHO_, showed high feature importance and significantly affected the severity prediction. Finally, in Group 5, we selected 30 of the 37 clinical variables as important features. The most critical clinical variables were related to blood test results, including _WBC_, _PLT_, _LYMPHO_, _HCT_, and _HGB_. Concurrently, patient demographics and vital signs, such as _AGE_ and _TEMPI_, remained highly relevant. However, certain clinical variables, such as _PREG_, _PREGW_, _VN_, _CLD_, and _RDAD_, were not selected for Groups 1-5. Consequently, they were identified as noncontributors to the severity prediction capabilities of the model.
## 3 The OpenFlow Protocol
### OpenFlow Specifications
#### 3.2.3 OpenFlow 1.2  
OpenFlow 1.2 [26] was released in December, 2011. It comes with extended protocol support, in particular for IPv6. OpenFlow 1.2 can match IPv6 source and destination addresses, protocol number, flow label, traffic class and various ICMPv6 fields. Vendors have new possibilities to extend OpenFlow by themselves to support additional matching capabilities. A type-length-value (TLV) structure, which is called OpenFlow Extensible Match (OXM), allows one to define new match entries in an extensible way.  
With OpenFlow 1.2, a switch may simultaneously be connected to more than a single controller, _i.e._, it can be configured to be administrated by a set of controllers. The switch initiates the connection, and the controllers accept the connection attempts. One controller is defined master and programs the switch. The other controllers are slaves. A slave controller can be promoted to the master role, while the master is demoted to the slave role. This allows for controller failover implementations.
# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
## 3 Localization Algorithm
### Communication between Nodes
#### 3.5.1 Single-Stage Scheme  
We describe the single-stage communication between nodes in this part. In [62], the proposed scheme utilizes the hyperbola-based approach for event localization and a normal distribution for estimation error modeling and calibration. Between the common circle-based and hyperbola-based method, there is some difference. Firstly, the two hyperbolas always have one intersection, but the circle-based method does not. In addition, for the hyperbola-based method, it is easy to find partial solutions that are used in the calibration process to further ameliorate the localization precision. However, for the circle-based method, adopting a partial differential approach or the matrix approach is needed.  
In [63], the authors propose a Wide coverage Positioning System (WPS). Firstly, the authors consider relying on four anchor nodes, and the UPS scheme cannot uniquely localize the sensor nodes in the enclosed area and the sensor nodes residing close to the anchor nodes require five anchors. In WPS, the authors define that the using four anchors as UPS (4) can attain unique localization--similarly, if five anchors as UPS (5) are used. UPS (4) and UPS (5) are used together in which four anchors are already localization. In the WPS, if a message cannot be received, the sensor node should wait for a timeout period and then start re-initiating the positioning process. Compared with the UPS, the WPS performs better in providing higher unique localization success but with higher localization delay and communication cost.
# Review of Smart-Home Security Using the Internet of Things
## 3 Smart-Home Ecosystem
### IoT in Smart Homes
These may include lamps and lighting equipment in general [57] for the control of ambient light, thermostats [58] for heating water, and air-conditioning [59] for adjustment of the indoor temperature. Smart functionality can also be found in ovens [60] to monitor and control the cooking process or refrigerators [61] to record the stored goods and help in scheduling the resident's shopping routine. Smart plugs [62] form another device category that is gaining interest. These plugs are connected to the home's electricity grid, and ordinary devices are connected to the smart plugs. Smart plugs control the electric power on this equipment, performing smart-home functionality like open or close devices (thus starting or stopping the devices' actual operation). Usually, all these devices are managed via the user's smart phone and related applications. Once set, these installations are rarely monitored or maintained by their users in terms of security. Therefore, when equipment is compromised by attackers, it remains infected for long periods, with attacks like crypto-jacking or Denial of Service (DoS) campaigns to external victims going completely unnoticed [63; 64]. Moreover, attacks on the related devices pose great threats to the safety and well-being of the residents, while they can also cause significant economic losses (i.e., excessive energy consumption and damaged equipment). The disposal of smart-home devices introduces significant security implications and challenges [65], primarily due to concerns for data privacy and the potential for unauthorized access. Many of these devices, ranging from smart thermostats to home assistants, store sensitive user data which, if not properly erased before disposal, can be retrieved by malicious actors. This vulnerability presents a considerable risk, particularly with devices that lack a clear, user-friendly process for fully erasing stored data. Additionally, improperly disposed devices can still connect back to a user's network (e.g., if not correctly deregistered), allowing potential ongoing access to live data and network resources. These challenges highlight the need for robust data management practices, clear guidelines on device decommissioning, and awareness among users about the security risks associated with disposing of their smart-home equipment. The same conditions also hold in the case of the user selling the device. The home resident is the main actuator who uses and, usually, installs the overall IoT ecosystem [66]. However, an arbitrary user would not possess the knowledge and expertise to set this system; thus, the awareness level of users must be enhanced. Although the home resident is the main actuator, other people might access the home network, like guests who receive Wi-Fi access to connect to the Internet or technicians who must install or repair some device. Moreover, in some cases like in hired apartments or hotels, even the residents of this smart private space can change, as well as the personnel that work within.
## 3 Results and Discussion
### Model Limitations and Outlook for Future Studies
One can see from Figure 5 that the \(C_{sh}/B_{sh}\) ratios depend nearly linearly on temperature, maybe except for the points corresponding to \(T=350\) K. Moreover, the slopes of the linear dependences are scaled with the LED emission wavelengths as \(\left(E_{g}^{3}E_{P}\right)^{-1}\), in accordance with the suggested model. Remarkable deviation of the experimental points from the above trends observed at \(T=350\) K may be evidence for the transition from the localization-mediated to conventional free carrier-mediated recombination processes. The latter issue, however, requires a special examination. Figure 5: Experimental \(C_{sh}/B_{sh}\) ratios for blue (445 nm) and green (530 nm) LEDs obtained from the data of [22; 52] as a function of temperature. Circles and squares correspond to blue and green LEDs, respectively. Bars indicate the errors originating from uncertainty in the \(Q\)-factor evaluation. Lines drawn for eyes indicate the experimental trends. The transition from localization-mediated to free-hole mediated recombination is also expected to occur at high current densities due to filling the localized states with non-equilibrium holes. This transition should be accompanied by changes of all RCs to those that can be calculated by conventional theory accounting for the \(\mathbf{k}\)-selection rule. Prediction of the transition point requires knowing the DOS of localized holes, which is a task for future studies. Experimentally, such a transition has not been identified to date. The semi-empirical model discussed in this paper addresses the influence of carrier localization on radiative and Auger recombination. On the other hand, strong hole localization may also affect SRH recombination, including its dependence on crystal orientation. The latter factor has been studied both experimentally and theoretically in [24] for the case of free carrier-mediated SRH recombination. In order to account for the impact of hole localization on the SRH recombination coefficient, it is necessary to consider either hole tunneling from its localized state to a deep point defect or hopping transfer of holes from the localized state to the defect. The influence of the hole localization on the SRH non-radiative recombination is expected, however, at lower InGaN compositions providing emission wavelengths shorter than 450 nm, where the experimental SRH recombination coefficients tend to increase (see Figure 1).
# On the Evaluation of the NB-IoT Random Access Procedure in Monitoring Infrastructures
## 2 State of the Art
### Current Evaluations of the Random Access Procedure
Consequently, this paper addresses this gap. In particular, the contribution of this paper is three-fold. First, starting from the platform presented in [25], it puts forward an open-source and 3 GPP-compliant implementation of the NB-IoT random access procedure, which handles different coverage classes,global and local transmission attempts counters, backoff times, and transitions from a class to another, as well as other key simulation. Second, it formulates an analytical model describing both collision and success probabilities, given the number of users willing to perform the random access procedure. Third, it deeply cross-validates the analytical model and the simulation tool, by considering realistic NB-IoT deployments enabling periodic reporting in monitoring infrastructures, with different resource configurations. As outlined in Figure 6, the starting point is the implementation of a 3 GPP-compliant NB-IoT random access procedure within an open-source simulation tool [25]. In summary, it is able to provide the number of users accessing the NPRACH and the performance of the procedure itself, expressed in terms of collision probability and success probability, by taking into account a large number of input parameters. Then, the analytical model receives in input the number of users accessing the NPRACH, obtained through computer simulations for estimating both the collision and the success probabilities of users in an RAO. Note that the symbols adopted in this document are summarized in Table 2.  
Figure 6: Big picture of the main research contributions.
## 3 Method
### Calculation of Spatial Information Content in Area Maps  
The calculation model of spatial information proposed in this paper takes the nodes (area objects) in the spatial adjacency graph as the measurement unit. By integrating the four characteristics of nodes and edges mentioned above, it couples geometric information with spatial distribution information, forming a comprehensive measure of spatial information. These four characteristic values are non-probabilistic parameters that are directly correlated with spatial information. Specifically, the larger the geometric information content of an area object, the higher the spatial information; the higher the node degree, the higher the connectivity information of the node; a greater adjacency strength indicates a closer  
Figure 7: Schematic diagram of spatial distribution of artificial objects ((**a**) regular arrangement of similar area objects results in less spatial distribution information; (**b**) irregular arrangement of dissimilar area objects results in greater spatial distribution information).  
adjacency between the area object and its current adjacent object, resulting in a higher weight assigned to the adjacency relationship; and a larger spatial distance indicates a greater distance between two objects, which requires separate cognition and is difficult to form an integrated cognition, thus leading to a higher cognitive weight. Therefore, these non-probabilistic parameters do not conform to the measurement principles of the Shannon entropy model [27]. Based on this, this paper selects the Eigenvalue entropy model proposed by [PERSON] et al. and combines it with the above parameters to construct a calculation model of spatial information [36]. The calculation formula is as follows:  
\[S_{ij}=\log_{2}\left[(Geo_{i}\times Edge_{ij}+Geo_{j}\times Edge_{ii})Dis_{ ij}+1\right], \tag{7}\]  
\[I=\sum_{i=1}^{N}\left[\sum_{j=1}^{M-m}S_{ij}+S_{m}(1+\log_{2}(m))\right], \tag{8}\]  
The spatial information of area maps is the sum of the spatial information of all area objects within the map frame. The method proposed in this paper achieves the coupling of geometric information and spatial distribution information by using spatial distribution features as influencing factors acting on geometric information. Since the diversity of nodes has already considered geometric information, the method proposed in this paper is a kind of spatial information that includes geometric information.
# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 3 Experimental Setup
### Implementation Details
#### 3.1.2 Model Configuration  
SA-EEND encoder is composed of four transformer blocks, each containing four attention heads, producing 256-dimensional embeddings. The EDA module maintains a pool of 15 potential attractors during training, though typically only a subset achieve the existence probability threshold required for activation. The diarization layer employs a standard sigmoid activation function with a detection threshold of 0.5.