esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
By stabilizing the frequency of an electronic oscillator on a specific atomic transition line, the atomic clocks could provide excellent frequency stability performance [1; 2; 3; 4; 5]. Cesium clocks have advantages in long-term frequency stability, volume and power consumption, and are widely used in synchronization, timekeeping, information infrastructures, and fundamental physics research [6; 7; 8].  
The frequency synthesizer is a crucial component of cesium clocks, bridging the frequency of electronic oscillator and atomic transition frequency. The stability of a clock is particularly related to the phase noise level and the settling time of the synthesized microwave signal [9; 10; 11; 12; 13]. However, we found that the vast majority of existing work in this field mainly focuses on the low phase noise of frequency synthesizers [14; 15; 16; 17; 18; 19], and there are few reports on the settling time. Therefore, in this paper, the settling time of the frequency synthesizers will be investigated in detail, which is also important for precisely identifying the central transition lines and maximizing atomic transitions [12; 13].  
We here present a microwave frequency synthesizer, which employs frequency multiplication and dual-phase-locked loop circuits. In this design, a direct 9.192 631 770 GHz frequency signal is generated through the dielectric resonant oscillator (DRO) in the phase-locked Loop (PLL). In contrast to previous synthesizers [12; 13; 14; 15; 16; 17], the dual-phase-locked loop circuits are equipped with two oven-controlled crystal oscillators (OCXOs): one serves as the 10 MHz reference source in the first PLL, and the other one operates at 200 MHz and is used in the second PLL. The 200 MHz OCXO serves the purpose of decreasing the phase noise of the intermediate frequency offset frequency band signal. By using the presented chain scheme, we demonstrate the realization of alternative low-noise cesium clock frequency synthesis. We show that the phase-settling times can be adjusted from 644.5 us to 1.5 ms by continuously changing the loop filter gain of the PLL. This is well explained by theory and verified by both simulations and experiments. Our work will be useful for the design of compact, high-performance frequency synthesis for different atomic clock applications.
# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 5 Proposed Solution
### NSSF DAF Restrictions  
Although the NSSF DAF framework solves the slice selection problem, it does not address issues related to MM. Thus, the mobile device must also use a management protocol that allows for handover between slices, keeping the continuity of the service  
Figure 5: NSSF DAF Integration overview.  
active, especially in instances sensitive to latency and packet loss, such as traditional real-time applications.  
In [80] the main challenges involving MM are presented, as well as potential solutions. Among the main issues, we can mention handover signaling, network slicing, integration with other frameworks, and frequent handovers, among others. Potential solutions include the use of deep learning, the creation of mechanisms on demand MM, Software-Defined Networking (SDN)- NFV integration with legacy methods (e.g., IEEE 802.21, PMIPv6, LTE MM) and the use of edge computing solutions.  
These demands are outside the scope of this work, but the framework proposed here is in line with these demands, and can be easily extended to cover these issues. This is mainly possible due to its deployment through the edge computing model, SDN and NFV, in addition to slice selection strategies, Radio Access Technologies (RAT), the network metrics acquisition approach and slice evaluation with traffic flow during operation.
## 5 Hybrid Protocols
### Binary Tree Slotted Aloha
#### 5.2.1 Dynamic Binary Tree Slotted Aloha  
Dynamic Binary Tree Slotted Aloha (dynamic BTSA) involves a dynamic frame adjustment and the basic BTSA algorithm [65]. The advantage of this protocol is that the reader can adjust its frame size by judging only the first slot type in the identification process.  
Figure 23 shows the dynamic BTSA algorithm, where the initial frame length is \(L=2^{Q}\), and the initial \(Q_{0}=4.0\). The procedure is very similar to the Q protocol. Firstly, the reader transmits a QueryAdjust command with frame length to all of the tags in the interrogation area. Subsequently, each tag randomly chooses a random number between 0 and \(L\)\(-\)1. Tags whose Counter value is 0 transmit their ID. Then the reader transmits a new request with a new \(L\) and will be capable of receiving responses in the first slot of the following frame. If the first slot is idle, the reader decreases the value of \(Q\) by 1 (\(Q\) = \(Q\)\(-\)1) and the reader creates a new frame based on the updated \(Q\). If the reader successfully identifies a tag in the first slot, \(Q\) will not be changed and the reader will move to the BTSA algorithm [65].  
In BTSA, the reader transmits the Query command in a frame. The reader has a slot counter (SC) that is set to 0 at the beginning of the frame, and is increased by 1 at the end of each slot. When the  
Figure 23: Flow chart of Dynamic BTSA.  
Figure 22: An example of Binary Tree Slotted Aloha (BTSA).  
frame length is equal to SC, the frame finishes. When the reader receives an ID in a slot, it knows the type of slot and will inform other tags by transmitting its feedback. If the reader detects a collision in a slot, it will resolve the collided tags by BinTree splitting [66]. In order for the reader to know when the binary tree has finished, it uses the variable \(B\) The initial value of \(B\) is set to 2. In the case of a collision, \(B\) is increased by 1, and if there is no collision, \(B\) is decreased by 1. Only if \(B=0\) does the reader know that the binary tree has finished.  
Dynamic BTSA reduces the number of collisions and improves identification efficiency [65].
## 5 Conclusions  
The secrecy analysis of FD short-packet transmission in a wiretap channel for both passive and active eavesdroppers has been implemented, subject to the constraints of the MSE, BER, security gap \(S_{g}\). This paper highlights that the presence of a jamming signal has a major effect on the reliability and security factors in PLS. To deal with this effect, a joint iterative SI channel estimation, propagation channel estimation, and decoding algorithm in FD transmissions via feedback were applied at the legitimate receiver, including blind feedback or semi-blind feedback schemes in the case of passive and active eavesdroppers, respectively. The numerical results presented show that the proposed algorithms, such as the blind feedback scheme in the passive case and the semi-blind feedback scheme in the active case, outperform the conventional algorithms without feedback, where the security gap \(S_{g}\) is significantly reduced. Moreover, it can be noticed that the blind feedback scheme in the case of a passive eavesdropper and the semi-blind feedback scheme in the case of an active eavesdropper are less sensitive to the increase in self-jamming power. Moreover, the SNR of the legitimate receiver is reduced when applying the proposed schemes to decode the intended message, which means that the system not only ensures the security factor well, but it also significantly improves the power consumption by reducing the transmitting power. It is also noted that the proposed blind and semi-blind algorithms have better performances in terms of processing time and computational complexity, which are shown in [27; 28]. Therefore, the presence of joint iterative estimation and decoding with blind and semi-blind algorithms at the legitimate receiver is highly recommended to enhance the security of FD wiretap transmission, especially in short-packet transmission-specific to IoT applications and green communications.
# The Relationship between Radon and Geology: Sources, Transport and Indoor Accumulation
## 2 The Origin of Radon
### Rocks and Soil Migration Factors
#### 2.2.4 Other Factors
Due to temperature variations between soil air and atmospheric air, the investigation observed air movements between regions of the ice-marginal deposit at different elevations. Notably, immediate changes in air flow direction were seen when ambient air temperatures approached the average yearly air temperature. The second most significant factor influencing soil radon concentrations was found to be air pressure. Precipitation, wind speed and direction, or soil moisture, however, had no discernible influence. The research also investigated how indoor and soil radon levels varied seasonally in a glacid fluvial deposit 40 km southwest of Kinsarvik. The seasonal fluctuation patterns in the two places were correlated, which raises the possibility that the findings may also be applicable to other regions with highly permeable building ground and variations in terrain elevation. * Topography: The topography of the terrain also influences the transport of radon [69]. The presence of slopes, hills, and depressions can affect air circulation and soil pressure, which in turn affects the mobility of radon [70]. In areas with rugged terrain, the drainage of groundwater and air circulation may be greater, resulting in lower accumulation of radon [57]. On the other hand, depressions and low-lying areas can act as traps for radon, leading to higher concentrations of the gas. [PERSON] et al. (2014) highlighted the influence of atmospheric conditions and topography on radon concentration [69]. Specifically, the radon measurements at Jungfraujoch were intermittently affected by boundary-layer air brought to the station by processes including thermally driven (anabatic) mountain winds. These observations suggest the potential for topographical features and atmospheric dynamics to significantly impact radon distribution in the environment. This highlights the importance of considering the interplay of topography and atmospheric processes when assessing radon concentrations in various geographic locations.
## 2 The Principle of Uniform Accuracy Lifetime Design
### Definition of Uniformity of the Accuracy Lifetimes  
According to the uniform accuracy lifetime principle, accuracy lifetimes should be absolutely equal to each other. However, this is impossible for an actual measurement system due to some uncontrollable influencing factors. A certain variation in lifetimes should be allowed in a specific design process. In order to establish a practical uniform design model, the uniformity \(\rho\), which characterizes the variation in lifetimes, should be given. Referring to the definition of the uniformity of illuminance [29], the uniformity \(\rho\) of the measurement system can be defined as follows:  
\[\rho=\frac{\min\{T_{1},T_{2},\ldots,T_{n},T_{s}\}}{\overline{T}},\overline{T} =\frac{\sum\limits_{i=1}^{n}T_{i}+T_{s}}{n+1}. \tag{3}\]  
Obviously, when the minimum lifetime \(\min\{T_{1},T_{2},\ldots,T_{n},T_{s}\}\) of the whole system is equal to the mean lifetime \(\overline{T}\), then \(\rho=1\), and the whole system is absolutely uniform; when \(\min\{T_{1},T_{2},\ldots,T_{n},T_{s}\}\) is equal to 0 and far from \(\overline{T}\), then \(\rho=0\), and the uniformity of the system is the worst. The smaller \(\rho\), the worse the accuracy lifetime uniformity, and the system should be improved. On the contrary, the bigger \(\rho\), the better the accuracy lifetime uniformity. If \(\rho\) is bigger than the expected value, the measurement system can be regarded as basically uniform.  
For an actual measurement system, the lifetimes of the system and its components are generally different from each other. After obtaining each lifetime, the uniformity of the whole system \(\rho\) can be calculated. If \(\rho\) is smaller than the required uniformity index \(\varepsilon\) of the measurement system, the accuracy loss laws of some components should be redesigned. Here, \(\varepsilon\) can be determined according to the service life, the cost of budget for the system etc.
## 4 Proposed Framework
### Architecture Overview
#### 4.1.3 Central Unit Layer  
The central unit serves as the coordination hub for the framework, managing ontology updates, global model aggregation, and system synchronization.  
Composition  
The central unit is a high-performance server or a cloud-based infrastructure capable of managing global computations and communications.  
Responsibilities  
* **Ontology management**: Maintains the ontology \(O\), ensures uniform data representation across edge servers, and distributes updates as needed.
* **Model aggregation**: Aggregates updates from edge servers to refine the global model:  
\[w_{0}^{t+1}=\frac{1}{m}\sum_{j=1}^{m}w_{j}^{t}, \tag{11}\]  
where \(t\) denotes the current training round.
* **Global coordination**: Synchronizes federated learning rounds and ensures edge servers receive the updated model \(w_{0}^{t+1}\) for further training.
## 6 Heat-Insulating Layer Design Considerations
### Heat-Insulating Materials (with Mechanical Ventilation Velocity of 5 m/s)  
Three different heat-insulating materials that are frequently used in anti-freezing designs and constructions of cold region tunnels are employed in the present analysis to investigate the influence of different materials on design parameters of heat-insulating layers. The thermal physical parameters for the FLOLIC material can be found in Table 1, while the thermal physical parameters for the other two materials (the polyurethane material and the stem method aluminosilicate sheet) are listed in Table 2. As shown in Tables 1 and 2, the FLOLIC material shows the best performance with regards to heat insulation (0.024 \(w/m\cdot^{\circ}\)C), while the heat-insulating performance of the stem method aluminosilicate sheet is the worst (0.038 \(w/m\cdot^{\circ}\)C) out of the three materials. The design thickness and length for the three materials are calculated under the same mechanical ventilation condition with a velocity of 5 m/s, and using the same computation procedure in Section 6.1. The numerical results for the three materials with different thickness and different length are shown in Tables 3 and 4, respectively.  
As the FLOLIC material has the best heat-insulating performance, the design thickness is proposed as 4 cm, which is the thinnest out of the three materials. Furthermore, the design length in the entrance/exit section and is chosen as 700 m/600 m, according to the calculation method provided in Section 6.1. For the polyurethane material with slightly worse heat-insulating performance, the design thickness is proposed as 6 cm (thicker than the FLOLIC material). As a thicker insulating layer could lead inadequate heat exchange as well as potentially preventing rapid heating of airflows inside tunnels, the design length for polyurethane material is 800 m/700 m for the entrance/exit section. Although the heat-insulating performance of the stem method aluminosilicate sheet is the worst out of the three materials, a thickness of 6 cm is able to satisfy the requirements of heat preservation for the entrance and exit sections (see Table 3). As the thermal conductivity of the stem method aluminosilicate sheet is relatively high, there is no need to increase the design length, which remains the same with the design length of the FLOLIC material (700 m/600 m for the entrance/exit section). By comparing the design parameters between the polyurethane material and the stem method aluminosilicate sheet, it can be found that the material with better heat-insulating performance requires a longer design length when the design thickness of the two materials is the same. Therefore, with the satisfaction of the anti-freezing requirement, applying a heat-insulating layer with relatively poor insulating performance could reduce time costs and engineering consumptions for a tunnel construction.
###### Abstract  
Sleep represent one of the main reservoirs of diarrheagenic _Escherichia coli_; this microorganism is an etiological agent of food-borne diseases; therefore, this work aimed to identify and characterize the principal pathotypes of diarrheagenic _E. coli_ (DEC) obtained through rectal swabs and carcass samples from sheep slaughterned in an abattoir at the central region of Mexico. The isolates were subjected to bacteriological identification, serotyping phylogenetic classification; detection for virulence factors, and antimicrobial sensibility. A total of 90 _E. coli_ isolates were obtained. It was observed through 49 _E. coli_ isolates (54%), 8 of them from carcass, and 43 from feces was DEC. DEC serotypes with health public relevance were found: O76:H19 (n = 5), O146:H21 (n = 3), O91:H10 (n = 1), O6:NN (n = 1), and O8:NM (n = 1). Regarding the presence of Shiga toxin-producing _E. coli_ (STEC), 43/90 (47.7%) isolates have the _stx1_ w/o _stx2_ genes, and therefore were assigned as STEC non-O157; only one isolate expressed _stx1_ and _ae_ genes and was classified as t-STEC (typical STEC). Additionally, 3/90 (3.3%) harbored only the _ae_ gene and were classified as enteropathogenic _E. coli_ (EPEC), the _stp_ gene was found in 2/90 isolates (2.2%) and were classified as enterotoxigenic _E. coli_ (ETEC); 1/90 (1.1%) isolates harboring the _ipuH_ were classified as enterovasive _E. coli_ EIEC. Regarding _stx1_ genes subtypes, _stx1_ only was found in 60.5% (26/43), followed by _stx1a-stx1c_ 20.9% (9/43) and _stx1a-stx1d_ 2.3% (1/43). The presence of both, _stx1_ and _stx2_ genes was found in 7/43 isolates (16.3%) from rectal swabs; the combination _stx1c-stx2_ was detected in 3/43 isolates (6.9%), while 4 (9.4%) isolates showed different patterns (_stx1a-stx1c-stx2g_; _stx1c-stx2b-stx2g_; _stx1c-stx2b_ and _stx1a-stx1c-stx2b-stx2g_). STEC isolates showed the major diversity of phylogenetic groups, although phylogeny B1 was predominant in 90.6% (39/43) while there was only one isolate (2.3%) in each remaining phylogeny (A, B2, C, and F). All EPEC, ETEC, and EIEC isolates were clustered in phylogeny B1. We observed that 27.9% (12/43) of STEC isolates carried at least one antibiotic resistance: nine isolates expressed the _tte1_ gene, one isolate the _sul2_ gene, one isolate the _sul1_ and one isolate the _sul1_-_tte1_ genes. These results highlight the importance of diarrheagenic _E. coli_ as a potential risk for public health during the slaughtering process.  
diarrheagenic _Escherichia coli_; serotyping; pathotypes; phylogroup; sheep; antibiotic resistance; slaughterhouse
# Towards an Indoor Gunshot Detection and Notification System Using Deep Learning
## 2 Related Work
The system implements an array of four omnidirectional microphones, connected to a commercial data acquisition (DAQ) recording system. MATLAB is then used to analyze and classify the signal. The authors report a 99% accuracy in classifying M16 gunshots from background noise. In the work of ref. [15], convolutional neural network (CNN) models such as VGG16, InceptionV3, and ResNet18 are trained with transfer learning for gunshot detection. Mel Frequency Cepstral Coefficient (MFCC) features are generated from the audio signals and then fed into the CNN models. An accuracy over 99% is reported for the ResNet18 model for their dataset. The hardware implantation results and notification systems are not presented in the paper. The work in ref. [16] uses a custom CNN model for gunshot classification. Spectrograms are generated from audio signals and fed to the CNN model. The proposed model reports an accuracy of over 99% for their custom dataset. The model is implemented in a low-cost hardware system consisting of a USB microphone, Raspberry Pi board, and a short message service (SMS) modem. When a gunshot is detected, the system sends an SMS alert message to a fixed list of phone numbers. However, the system does not include custom user and device configuration using a smartphone app and plotting the location on a map. A comparison with published related works is shown in Table 1. Here, we see that a CNN-based deep learning model is trained with the largest dataset in the proposed work, and it has high accuracy and precision. It should be noted that achieving high accuracy on a large dataset indicates that the model is generalized well without overfitting. The proposed system is implemented in a Jetson Nano-based embedded system which contains GPU and TensorRT engine for fast inferencing, whereas the work in [16] uses Raspberry Pi that neither has GPU nor TesnorRT support, resulting in slower inferencing. As the proposed device is connected to Wi-Fi, it can obtain the date and time and record the gunshot sounds with timestamps for post-crime analysis. In this work, a complete gunshot detection system is implemented consisting of a gunshot detector device; a central server having an SQL database, plotting on a map, data searching, and push notification sending capabilities; and two smartphone apps--for the user and the emergency responders. Users and devices can be configured using smartphone apps considering possible many-to-many relationships. As soon as the gunshot event happens, the smartphone apps receive real-time push notifications with location information plotted in Google Maps.
# Design and Implementation of a RFID Reader/Router in RFID-WSN Hybrid System
## 3 Hardware Design of RFID Reader/Router  
### Hardware Composition of RFID Reader/Router  
The block diagram of the RFID reader/router is shown In Figure 3. CC2530 works as the microprocessor and the 2.4 GHz transceiver. It gets environmental information, and transmits information to the coordinator in the WSN network. It also executes the commands sent by the coordinator. MFRC522 operates at 13.56 GHz as a RFID reader. It performs read and write operations. At the respective operating frequencies, CC2530 and MFRC 522 do not interfere with each other.