esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
An electro-physical interpretation for the degradation of the Fill Factor in p\({}^{+}\)/n silicon heterojunction solar cells (SHI) due to incomplete hole collection at the thermionic emission barrier at the amorphous/crystalline silicon (a-Si:H/c-Si) hetero-interface is proposed supported by results of AFORS-HET device simulations. Under illumination, reflected holes at the thermionic barrier pile up at the hetero-interface which strengthens the dipole with the negative dopant ions in the doped a-Si:H(p\({}^{+}\)) layer and enhances the electric field passing through the a-Si:H layer. Such an enhanced electric field sweeps back the free holes spilling over in the intrinsic a-Si:H(i) layer from the a-Si:H(p\({}^{+}\)) layer considerably depleting the double a-Si:H layer and enhancing its resistance and the overall cell series resistance. Therefore, the degradation due to incomplete hole collection at the thermionic emission barrier under illumination can be assimilated to the effect of a series resistance does not affect the cell open circuit voltage but degrades only its fill factor. The resistance enhancement is found to be bias-dependent and to increase with decreasing the doping level in a-Si:H(p\({}^{+}\)). Predictions of the proposed model for different hole reflection probability at the barrier and for different thicknesses of the intrinsic a-Si:H(i) layer agree perfectly with the results of simulations.  
Keywords:resistance of silicon heterojunction solar cells (SHI); amorphous/crystalline silicon (a-Si:H/c-Si) heterojunction; fill factor degradation in SHJ cells; thermionic emission transport; valence band discontinuity +
Footnote †: journal: Applied Physics Letters B
# A Complete and High-Resolution Estimate of Sardinia's Rootrop Photovoltaic Potential
## 3 Methodology
### Computation of Rooftop Slope and Aspect  
By applying the algorithms to the DSM [42], offered by the tool gdaldem of the GDAL library [43], we obtained the 1 m resolution raster map estimates of the tilt and orientation angles of the roof surface, denoted respectively with \(\beta\) and \(\gamma\). These values were necessary for the calculation of the incident irradiance on photovoltaic panels, whose annual total varies significantly depending on the orientation with respect to the south and on the inclination of the rooftops on which they are installed. Figure 6 shows the (a) computed aspect and (b) slope values for the rooftop of the buildings within an area of the Municipality of Cagliari.  
Figure 5: Flowchart representing the workflow followed to obtain the photovoltaic potential of Sardinia. Input data are shown in the yellow boxes, derived data are framed in pink and light-blue boxes, data processing steps were placed in green rounded rectangles, and the workflow results are outlined by red frames.  
The DSM covers a total building rooftop surface of about 113 km\({}^{2}\), corresponding to 113 million pixels. Not all surfaces are suitable for the installation of panels due to the peculiarities of the buildings, discontinuities, irregularities, and steep inclinations of the rooftop surfaces. After filtering the unsuitable portions of building rooftops, we obtained a total useful surface area of approximately 80 km\({}^{2}\).  
Figure 7 shows the cumulative sum of the rooftop surfaces: about 60% of building rooftops were characterized by a slope less than 20\({}^{\circ}\). Around 25% of rooftops had a slope greater than 45\({}^{\circ}\) and were thus excluded from the dataset. A further 5% of the rooftops were removed because they were part of contiguous surfaces that were too small to allow for the installation of photovoltaic modules. The aspect of the building rooftops is instead described by a continuous uniform distribution (not shown).  
Figure 6: (**a**) Maps of aspect and (**b**) slope values computed for the building rooftops within the Municipality of Cagliari.  
Figure 7: Cumulative sum of rooftop surface, sorted by increasing tilt angle.
## 4 Result
### Path Loss Exponent  
The analyzed path loss exponents for an omnidirectional and a bidirectional antenna are presented in Figure 6. The path loss exponent determines the decay of the signal power over distance. When the path loss exponent is 2, the propagation channel behaves as free space. When the value is below 2, the channel is likely an indoor environment with strong LoS, high directivity, or where the environment acts as a waveguide. Typical outdoor terrestrial channels have path loss exponents of 2.5-4.  
The average path loss exponents are between 2.60 and 2.95 for our rural-like crossings (#1, #2, and #5) and 3.05 and 4.11 for our suburban-like crossings (#3 and #4), with an exponential distribution. For the bidirectional antenna, we observe the average path loss exponent of 2.17-2.34 for the rural and 2.49-3.67 for the suburban crossings with an exponential distribution.  
For all the crossings, the average path loss exponents with the bidirectional antenna are lower than with the omnidirectional antenna. This result confirms that using a bidirectional antenna can concentrate the power in the same direction as a train will move and provide more coverage range than using an omnidirectional antenna.  
Figure 9: Doppler spreads for different crossings with (**a**) omnidirectional, (**b**) bidirectional antennas.
## 1 Introduction
In addition, it is crucial to establish mutual authentication as an essential security measure that must be ensured. Considering the prevailing difficulties and the presented criteria for the Internet of Things, our primary aim has been to establish a protocol for deploying the security processes and necessary authentication mechanisms. Today, we are faced with significant advances in technology, and new technologies are emerging every day that can serve as a fundamental component in IoT systems and applications. This new structure with the technologies used in it imposes more threats, and the understanding of the security risks, the importance of security, and the potential solutions has significant gaps. This is because all communication is based on an open channel and data are being exchanged within it. These conditions provide the ability to access and collect data to network adversaries and should provide safe conditions for all network nodes by implementing appropriate security policies. Also, network attackers will undoubtedly discover the Internet of Things as a smart and talented new space to enhance their unethical activities. According to them, the security requirements for the IoT include privacy, reliability, access, authentication, etc. [13]. Figure 1 shows the different components that are connected in an Internet of Things network. In this article, we examine the existing vulnerabilities and security requirements of the Internet of Things. Due to the extensive variety of devices, components, and users that make up the IoT network, the issues of exchanged information security, stored data, and privacy of the components are all serious challenges. With the endangerment of any of the mentioned cases, network attackers who have hostile intentions (such as delaying information packets and network services or even creating conditions for accessing unauthorized data) can cause significant destructive effects on the network. Also, another significant concern in the IoT network is the presence of limited computational capability nodes in this network, which forces the network to use a symmetric cryptography system to implement the confidentiality policies of the security infrastructure. The disclosure of the symmetric keys jeopardizes not only the existence of the connection but also compromises all network connections, resulting in the loss of information confidentiality. To address these security weaknesses, we propose a key-agreement and multiple authentication protocol to enhance the IoT node safety and protection policies. In the proposed plan to address the stability issue of the symmetric key, a mechanism has been implemented for communication entities to generate their unique symmetric key in each session. Additionally, due to the weakness and high vulnerability of nodes that have little computing power, we tried to implement the protocol process operations in a way that not only provides security mechanisms but also requires minimal processing power. The protocol contributed as follows.
## 5 Simulation Experiments and Results Analysis
### Design of the Simulation Experiment  
This subsection provides a detailed introduction to the experiment design from three aspects: experimental environment, experimental topology, and parameter design.  
Python was chosen as the simulation language for the experiment. The experimental topology adopts a layered and domain-partitioned mechanism described earlier. The ratio of the number of network nodes in each layer and the delay between domains are determined based on an actual survey of Computing Network deployments. According to the survey [65] results, the number of large, medium, and edge nodes can be set to 3, 9, and 45, respectively. The edge domain delay was kept within 1 ms, the medium domain within 5 ms, and the large domain within 20 ms. In this experiment, there was 1 large domain, 3 medium domains, and 9 edge domains. Additionally, it was assumed that dedicated links were used to connect users to access network nodes and computing clusters to gateways, with negligible transmission delays between them. The data plane can achieve nanosecond-level matching and forwarding processing, so the overhead delay caused by it can also be ignored.  
The main parameters include user request information and cluster resource information. Currently, there is no publicly available dataset for user request QoS metrics. Therefore, we investigated the requirements of Computing Network application scenarios. Based on several survey results [66], we categorized the requests in this experiment into three types according to delay: low, medium-low, and high, with delay limits of 10 ms, 50 ms, and 100 ms, respectively, accounting for 20%, 60%, and 20% of the total requests. Furthermore, we set the success rate requirement for low-delay requests at over 95%, for medium-low delay requests at over 90%, and for high-delay requests at over 80%, with increments of 5% as a level. To accelerate the simulation execution speed, we assumed that the system can provide 20 types of services, each categorized into different quality levels based on processing delay. The cluster resource information was derived from the Alibaba resource dataset [67], which includes resource configurations, resource utilization rates, resource demands of deployed applications, and runtime resource utilization for several servers. This data have been statistically processed to form the resource dataset for this experiment. The information related to services comes from the QWS2 [68] public dataset, which is the first dataset to measure the quality of service (QoS) of real web services. QWS2 is the second version of this dataset and includes 2057 services along with their QoS data. The processing data serves as the service processing delay within the computing cluster.  
Table 5 is the experimental parameter design of this paper:
## 2 Materials and Methods
### Gene Expression Analysis
#### 2.6.3 Real-Time qPCR  
Real-time qPCR was conducted for four target genes related to oxidative stress defense: genes coding for superoxide dismutase (SOD), catalase (CAT), photosystem II protein D1 (psbA) and heat shock protein 70 (HSP70) (Table 1). All qPCR assays were carried out on a StepOnePlus(tm) PCR cycler (Applied Biosystems, Foster City, CA, USA).  
Amplification reactions were performed in a 20 uL reaction mix containing 10 uL of SYBR Premix Ex Taq II (2\(\times\)) (Takara, Kyoto, Japan), 1 uM of each primer (Bio Basic Canada Inc., Markham, ON, Canada) and 2 uL of each appropriate cDNA sample. The used cycling conditions were 30 s at 95 \({}^{\circ}\)C, followed by 40 cycles of 5 s at 95 \({}^{\circ}\)C and 30 s at 60 \({}^{\circ}\)C. At the end of the qPCR cycles, the amplification specificity of each primer pair was verified through a fusion step performed by heating from 60 \({}^{\circ}\)C to 95 \({}^{\circ}\)C. The gene  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gene** & **Description** & **Primer Sequence (F = Forward; R = Reverse)** & **References** \\
**Abbreviation** & **Description** & **Primer Sequence (F = Forward; R = Reverse)** & **References** \\ \hline \multirow{2}{*}{_SOD_} & \multirow{2}{*}{Superoxide dismutase} & F: 5\({}^{\prime}\) CGCATGACTCTTGCCTTCG 3\({}^{\prime}\) & [16] \\  & & R: 5\({}^{\prime}\) TCCTGGTTGGCTTGGTTTC 3\({}^{\prime}\) & \\ \hline \multirow{2}{*}{_CAT_} & \multirow{2}{*}{Catalase} & F: 5\({}^{\prime}\) TCCTTGTTACGTTTTCTG 3\({}^{\prime}\) & [16] \\  & & R: 5\({}^{\prime}\) CAAAGTTCCCCTCTGTG 3\({}^{\prime}\) & \\ \hline \multirow{2}{*}{_psbA_} & \multirow{2}{*}{Photosystem II protein D1} & F: 5\({}^{\prime}\) ACTTTCTTACTGCTTGGC 3\({}^{\prime}\) & \\  & & R: 5\({}^{\prime}\) GACCTTGTAGTACTACTACTG 3\({}^{\prime}\) & \\ \hline \multirow{2}{*}{_HSP70_} & \multirow{2}{*}{Heat shock protein 70} & F: 5\({}^{\prime}\) TGCAGCCTGGTTGTGTGTGTGT 3\({}^{\prime}\) & [48] \\  & & R: 5\({}^{\prime}\) AGGGTGGTGTTGCGGGGTGAT 3\({}^{\prime}\) & \\ \hline \multirow{2}{*}{_\(\beta\)-TUB_} & \multirow{2}{*}{Beta tubulin} & F: 5\({}^{\prime}\) ATCTGTTCCGACCCTGAA 3\({}^{\prime}\) & \\  & & R: 5\({}^{\prime}\) AGCCGACCATGAAAGAGTGC 3\({}^{\prime}\) & \\ \hline \multirow{2}{*}{_18S_} & \multirow{2}{*}{18S RNA} & F: 5\({}^{\prime}\) TTGGGTAGTCGGGTGTGT 3\({}^{\prime}\) & \\  & & R: 5\({}^{\prime}\) CGCTGCGTTTCTCGT 3\({}^{\prime}\) & [49] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specific primers used for qRT-PCR.  
coding for \(\beta\)-tubulin was used as a reference gene. Relative mRNA expression values were determined with the \(2^{-\Delta\Delta\text{Ct}}\) method [47].
# Path Loss Model for 3.5 GHz and 5.6 GHz Bands in Cascaded Tunnel Environments
## 3 Channel Measurement and RT Parameters Calibration in a Subway Tunnel  
### Measurement Environment and Configuration  
As shown in Figure 5, the measurement campaign was conducted in the straight tunnel of Shanghai metro Line 7 (tunnel from Shanghai University station to Qihua Road station). The considered frequencies are at the 3.5 GHz and 5.6 GHz bands. The cross-section of the tunnel is arched, with a radius of 2.78 m and a bottom of 3.4 m, as shown in Figure 6a.  
In the measurement, the transmitting and receiving antennas are located on the railcar in the center of the tunnel. The height of both antennas is 2.65 m, and the positions of the transmitting and receiving antennas are shown in Figure 6b. When the transceiver distance  
Figure 5: The measurement environment.  
Figure 6: Transmitting and receiving antennas setting **(a)** The cross-section of the measurement tunnel; **(b)** the positions of \(T_{x}\) and \(R_{x}\).  
is from 20 m to 350 m, the sampling interval is 10 m, and the interval from 350 m to 650 m is 50 m. There is a total of 40 measuring points.  
The configuration of the measurement system is shown in Figure 7. It mainly comprises an Agilent E8257D signal source and Ceyear 4024G spectrum analyzer. Both transmitting (T\({}_{\mathrm{x}}\)) and receiving (R\({}_{\mathrm{x}}\)) antennas are ultra-wideband omnidirectional antennas, and the rubidium clock is used to ensure the clock synchronization. The input power is set as 10 dBm. As the railcar moves, the receiver gradually moves away from the transmitter, and the spectrum analyzer reads and records the received power of the signal at each test position. The antenna is high enough to ignore the influence of the testers.
# Energy-Efficient Opportunistic Routing Algorithm for Post-Disaster Mine Internet of Things Networks
## 3 System Model and Problem Description
### Energy Model  
Usually, the nodes in the Mine Internet of Things (MIoT) are sensor devices whose energy is limited and cannot be replenished after a disaster. In this section, we adopt the first-order radio energy consumption model to calculate the energy dissipation of the surviving network nodes [36]. In the MIoT, the energy cost of data transmission mainly considers the energy dissipation of communication modules of nodes. The energy consumption of transmitting a packet of size \(b\) from \(n_{i}\) to \(n_{j}\) is expressed as  
\[E_{\text{tx}}(i,j,b)=\left\{\begin{array}{ll}(b\cdot E_{\text{elec}})+(b\cdot \zeta_{\text{fs}}\cdot d_{i,j}^{2}),&d_{i,j}<d_{\text{thr}}\\ (b\cdot E_{\text{elec}})+(b\cdot\zeta_{\text{amp}}\cdot d_{i,j}^{2}),&d_{i,j} \geq d_{\text{thr}}\end{array}\right. \tag{1}\]  
Similarly, the energy consumption of receiving a \(b\)(bits) packet by \(n_{j}\) is formulated as  
\[E_{\text{rx}}(j,b)=b\cdot E_{\text{elec}} \tag{2}\]  
where \(E_{\text{elec}}\) represents the circuit energy consumption consumed by the sensor to send or receive each bit of data transmission, \(\varepsilon_{\text{fs}}\) and \(\varepsilon_{\text{amp}}\) are the expended amplification energy in free space and multipath fading space, respectively, while \(d_{\text{thr}}=\sqrt{\zeta_{\text{fs}}/\zeta_{\text{amp}}}\) is the threshold distance in meters, and \(d_{i,j}\) denotes the Euclidean distance from sender \(n_{i}\) to receiver \(n_{j}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Meaning** \\ \hline \(N\) & The network. \(n_{i}\in N\) is a sensor node. \\ \(N_{i}\) & The neighbor set of node \(n_{i}\). \\ \(m_{i}\) & The size of \(N_{i}\), \(m_{i}=|N_{i}|\). \\ \(n_{s}\) & The source node. \\ \(n_{\text{sink}}\) & The sink node. \\ \(e_{\text{ini}}\) & The initial energy of node. \\ \(d_{i,j}\) & The transmission distance from the node \(n_{i}\) to its neighbor \(n_{j}\). \\ \(\theta_{i,j}\) & The direction angle between the node \(n_{i}\) and \(n_{j}\) towards the sink node. \\ \(\varepsilon_{i,j}\) & The residual energy of neighbor \(n_{j}\) of \(n_{i}\). \\ \(e\) & Euler’s constant. Approx. 2.71828. \\ \(D\) & The network density. \\ \(F_{i}\) & The forwarding zone for the node \(n_{i}\) \\ \(w_{i}\) & The width of forwarding zone for the node \(n_{i}\) \\ \(A\) & The area of the target field. \\ \(r\) & The communication range of node. \\ \(Z_{i}\) & The candidate forwarding zone of \(n_{i}\). \\ \(C(i)\) & The candidate forwarding set of \(n_{i}\). \\ \(\alpha_{\theta},\alpha_{d},\alpha_{e}\) & The control parameters of routing metrics. \\ \(\overline{Q}_{i,j}\) & The routing quality of neighbor \(n_{j}\) of \(n_{i}\). \\ \hline \hline \end{tabular}
\end{table}
Table 2: The notations used in this paper.
## 2 Materials and Methods
### Genetic Algorithm  
A GA is an evolutionary algorithm inspired by natural selection and the biological processes of reproducing the fittest individuals. Initially, the GA generates a population of random candidate solutions, referred to as individuals or chromosomes. Each chromosome consists of a set of genes that represent the problem's variables. The evolutionary process is governed by an iterative mechanism in which a fitness function evaluates how well a solution addresses the problem. The higher the fitness, the greater the probability of the solution being selected for the next generation [27].  
GAs employ three main operators to drive the evolutionary process:  
Selection: This process determines which chromosomes will contribute to the next generation. The most common methods include Roulette Wheel, where the probability of selection is proportional to an individual's fitness; Tournament, in which a random group of individuals is selected, and the fittest advances; and Rank Selection, which orders individuals by their fitness and assigns selection probabilities proportional to their rank.  
Crossover: This operator combines pairs of chromosomes to produce new offspring. Crossovers can occur at one or two random points or follow a fixed probability to determine the crossover location.  
Mutation: This introduces variability by randomly altering one or more genes in a chromosome. Mutation prevents the population from becoming trapped in local optima and enhances exploration of the search space [28].  
Figure 2 illustrates the cycle of a GA, represented as a circular flowchart. It begins with an initial population of chromosomes and progresses through key stages such as fitness  
Figure 1: Configurations of an RC. (**a**) Circular: facilitates quick access between stations and optimizes space usage in compact areas. (**b**) Linear: enables a logical and sequential flow of materials or products, ideal for assembly line processes. (**c**) U-shaped: allows the workflow to return to the starting point, reducing downtime and minimizing material movement.  
evaluation, selection of the fittest individuals, crossover to generate new chromosomes, and mutation to introduce variability. These stages are repeated iteratively until a stopping criterion is met, such as a predefined number of generations or no change in the fitness value, highlighting the continuous evolutionary process toward optimal solutions.
# MinHash-Based Fuzzy Keyword Search of Encrypted Data across Multiple Cloud Servers
## The Proposed Fuzzy Keyword Search Scheme Based on MinHash
### Preliminaries
#### 3.1.3 Definition of Parameters  
Following are a list of parameters that will be used throughout our discussion.  
F = (f1, f2,..., fk): the set of plaintext documents; U = (u1, u2,..., um): the group of authorized users; C = (c1, c2,..., ck): the set of ciphertext documents corresponding to F; W = (w1, w2,..., wn): the set of keywords extracted from F; FIDt = (FID1, FID2,..., FIDt): the set of identifiers for a document; Si: the fingerprint of keywords wi; Score: the score of the correlation degree of keywords; IDwi: the set of addresses for a document that contains the keyword wi; Tw: the trapdoor of the keyword w; \(\Delta\) = {\(\alpha_{i}\)}: a predefined set of symbols, where \(\mid\Delta\mid\) = 2n and each symbol can be expressed by n bits; Gw: the index tree that contains all the fuzzy keywords; h(hk, \(\bullet\)): one-way hash function using key hk; (sk, \(\bullet\)): the symmetric encryption algorithm using key sk; OPE(ek, \(\bullet\)): an order-preserving encryption function using key ek; D(w) = {id(D_{i}) \(\mid\) w \(\in\) Di,1 \(\leq\) i \(\leq\) n}: the collection of IDs of all the documents containing the keyword w.
## 2 Materials and Methods
### Sesame Feedstock and Produced Biochar Characterization
#### 2.3.4 Infrared Spectroscopy (FTIR)  
The Fourier transform infrared (FTIR) spectral method was used to identify the functional groups of the feedstock and different biochar types. The spectral range investigated was from 400-4000 cm\({}^{-1}\) using (Shimadzu FT/IR-5300, Tokyo, Japan).