esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Frost Depth and Frost Heave in Romania
## 3 Frost Depth Prediction
### Russian and Polish Standards/Regulations  
In Russia, according to paragraph 2.124 (2.27) of the manual for the design of the foundations of buildings and structures (Russian construction standard SNiP 2.02.01-83)  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Investigated Period**} & \multicolumn{3}{c}{**FI (\({}^{\circ}\)C \(\times\) Days )**} & \multicolumn{3}{c}{**100-, 50-, and10-Year Return FIs**} & \multicolumn{3}{c}{**d\({}_{\text{frost}}\) Depth of Frost**} \\  & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{(\({}^{\circ}\)C \(\times\) Days )} & & \multicolumn{3}{c}{**(cm)**} \\ \cline{2-10}  & **Mean** & **St. Dev** & **Max** & **FI\({}_{\text{100}}\)** & **FI\({}_{\text{50}}\)** & **FI\({}_{\text{10}}\)** & **d\({}_{\text{frost,100}}\)** & **d\({}_{\text{frost,50}}\)** & **d\({}_{\text{frost,10}}\)** \\ \hline
1900–2020 & 147 & 139 & 447 & 582 & 507 & 328 & 117 & 109 & 89 \\ \hline \hline \end{tabularand SNiP 2.01.01-84 Climatology and Geophysics, the frost depth, H (in m), is calculated very simply using Equation (6):  
\[H=k\times\sqrt{M} \tag{6}\]  
where:  
\(M\) is the sum of the absolute values of the average monthly negative temperatures for the winter (only negative values are summarized);  
\(K\) is coefficient equal to 0.23 for clays and silts, 0.28 for sandy clays and fine-grained sands, 0.30 for coarse-grained sands, and 0.34 for gravels.  
For example, according to SNIP, a frost depth of 1.4 m is set for the Moscow region for severe meteorological conditions, for a high groundwater level, in the absence of snowmelt, and in severe frost conditions, although according to studies, the frost depth in the Moscow region varies from 60 cm to 180 cm.  
Since 1955, the maps of the soil freezing depths have been given in the Polish Standards [11]. The values of frost depth, H, are calculated in centimeters according to the formula given in Soviet recommendations, as shown in Snip:  
\[H=k\times\sqrt{M+2} \tag{7}\]  
where \(M\) and \(K\) are the same as the ones from the Russian norms. In both norms, there are no references to the return periods of the values.  
The depth of frost (in m) for the Arad station calculated for the coarse-type ground by using the Russian norm is 0.34 m, and it is calculated as 0.57 m by using the Polish norm. The application of Equation (7) for Romanian territory in the worst-case scenario (k = 0.34--gravely soils) is presented in Figure 6.  
The \"Zoning of Romania's territory by freezing depth\", STAS 6054/77, specifies for the Arad station a 70-80 cm frost depth. In comparison, in Table 6, there are values of  
Figure 6: Frost depths of Romanian territory for altitudes <1000 m.  
depths of frost with different mean recurrence intervals (MRI, 100, 50, 20, and 10 years), calculated for different periods and via different methodologies.
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 2 Background and Related Work
### Time-Slotted Channel Hopping  
In TSCH networks, every node follows a time-synchronized schedule. This schedule instructs every node about exactly what to do and avoids wasting valuable energy. The TSCH schedule is divided into time slots. The duration of a time slot is typically 10 ms or 15 ms and sufficient to transmit a packet of the maximum size of 127 bytes, immediately followed by an optional acknowledgment frame indicating that the packet was successfully received. Multiple time slots are grouped into a slot frame, and the size of a slot frame defines the width of the schedule. These slot frames repeat continuously over time. TSCH also allows one to use multiple frequencies, leading to a two-dimensional matrix of cells. The number of available frequencies actually determines the height of the schedule.  
A schedule can contain four possible cell types: TX, RX, shared and off. The first two indicate that the node should send or receive, respectively. Shared cells can be used by any node, and a contention-based back-off mechanism manages the access to it. They are used to synchronize, join and boot up the network [6]. An off cell indicates that the radio of the node should be turned off. The cells in a schedule are updated dynamically by a so-called scheduling function that takes into account the necessary resources to handle the traffic load and prevents wasting resources.  
TSCH also uses channel hopping to combat multi-path fading and external interference [7]. This channel hopping depends on the Absolute Sequence Number (ASN) and the number of channels. The exact frequency on which two nodes will communicate is determined by \(frequency=F((ASN+channelOffset)\mod nFreq)\) where \(F\) is a lookup table containing the set of available channels, \(channelOffset\) is the channel offset of the time slot in the schedule and \(nFreq\) is the number of available frequencies. The slot frame size (i.e., number of slots in a single slot frame) should be a prime number in order to be sure that every frequency is used.  
Figure 1 illustrates an example of a TSCH schedule with a slot frame size of 101 cells and 16 channel offsets. It represents a combination of all schedules of each individual node. Each cell in the schedule represents a specific time slot and channel offset in which directed communication between nodes can be assigned. These assigned cells can either be dedicated to a single transmitter (e.g., from node W to node Z in the cell with a slot offset of one and a channel offset of two), or they can be shared between multiple nodes (e.g., the shared cell with a slot offset of zero and a channel offset of one). All other cells are considered off cells.  
This article focuses on TSCH in IEEE 802.15.4e, but the research is also easily transferable to other protocols using TSCH, e.g., WirelessHART and ISA100.11a [8; 9].
## 2 Theoretical Foundation
### Signal Detection Techniques
#### 2.1.3 ZF Detection  
ZF detection assumes perfect knowledge of the channel matrix and treats interference as noise to be removed. However, limitations include the requirement for precise channel matrix knowledge, which may not always be available or can be subject to estimation errors. Equation (8) depicts the ZF detection process, where s denotes the transmitted symbol vector, H represents the channel matrix, and \(H^{-1}\) is its inverse. By multiplying the received signal (\(x\)) by the channel matrix inverse, ZF aims to remove interference and recover the transmitted symbols.  
\[s=H^{-1}x \tag{8}\]  
Equations (9) and (10) represent the calculation of the receiver's linear processing matrix (\(W\)) using the ZF approach. \(H^{T}\) denotes the channel matrix's conjugate transpose, and \((H^{T}H)^{-1}\) is the inverse of the product of \(H^{T}\) and \(H\). \((H^{T})^{+}\) denotes the pseudo-inverse of \(H^{T}\).  
\[W=((H^{H}H)^{-1}H^{H} \tag{9}\]  
\[W=(H^{T})^{+}=H(H^{T}H)^{-1} \tag{10}\]
# Numerical Analysis of EOD Helmet under Blast Load Events Using Human Head Model
## 3 Results and Discussion
### HIC and GSI Parameters
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **TNT [kg]** & **HIC** & **GSI** \\ \hline \multirow{3}{*}{Without Helmet} & 0.050 & 1235.0 & 1537.5 \\  & 0.075 & 2968.0 & 3620.2 \\  & 0.100 & 5552.0 & 6675.5 \\ \hline \multirow{3}{*}{With Helmet} & 0.050 & 61.9 & 75.9 \\  & 0.075 & 170.3 & 199.2 \\ \cline{1-1}  & 0.100 & 352.7 & 403.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Gadd Severity Index (GSI) parameter Head Injury Criteria (HIC) values obtained using both methods presented. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Case** & **TNT (kg)** & **AIS \(\geq\) 1 (\%)** & **AIS \(\geq\) 2 (\%)** & **AIS \(\geq\) 3 (\%)** & **AIS \(\geq\) 4 (\%)** & **AIS \(\geq\) 5 (\%)** & **AIS 6 (\%)** \\ \hline \multirow{3}{*}{Without Helmet} & 0.050 & 100 & 96 & 73 & 32 & 7 & 0 \\  & 0.075 & 100 & 100 & 100 & 100 & 100 & 99 \\  & 0.100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline \multirow{3}{*}{With Helmet} & 0.050 & 2 & 1 & 0 & 0 & 0 & 0 \\  & 0.075 & 16 & 6 & 2 & 0 & 0 & 0 \\ \cline{1-1}  & 0.100 & 54 & 20 & 6 & 2 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Probability of brain injury at different AIS (Abbreviated Injury Scale) levels for varying blast mass, based on HIC parameter. In Figure 11, the exponential growth of HIC with TNT load was observed for both situations studied, with and without helmet. Here, for any arbitrary TNT load, the use of helmet reduces significantly the probability of injury. Furthermore, the probability of death is close to 1 even if the protector helmet is used when the explosion is caused by a mass of close to 0.150 kg TNT (for the simulated conditions, that is at a distance 0.681 m). When an HIC value of 2693 is reached, it corresponds to a probability of 95% for the occurrence of AIS 6 injuries. In the case of no helmet used, the critical load related to irreversible brain injuries is reduced almost half, around 0.075 kg. It should be noted that 0.050 kg would already be enough to cause significant injuries to the user; due to this, it has been decided not to study in this work the effect of loads greater than 0.100 kg.
###### Abstract  
The concept of network slicing (NS) has been proposed for flexible resource provisioning where a physical resource is partitioned into logically independent networks on demand. The NS resource allocation implies the definition of a feasible path in the infrastructure network with adequate resource availability. However, due to complex structural characteristics of the backhaul transport network, a number of issues arise when fast deploying the end-to-end (E2E) slices onto network infrastructures. In this paper, a pair-decision resource allocation model is firstly formulated to construct the mapping relationship between logical networks and substrate networks in a coordinated way. In order to improve extreme quality of service (QoS) and user experiment, latency-optimal virtual resource allocation problem is defined, subject to the backhaul capacity and bandwidth constraints. The problem is formulated as an integer linear programming (ILP) and solved with the branch-and-bound scheme, whose resolution yields an optimal virtual network function (VNF) placement and traffic routing policy. Numerical results reveal that the proposed scheme can enable the transport network latency optimization with a reduction of up to 30% and 41.6% compared to the Network Slice Design Problem (NSDP) and Random Fit Placement Algorithm (RFPA) schemes respectively. In the meanwhile, the network load balance and serviceability have been improved efficiently with better resource utilization as well.  
network slicing; latency-optimal resource allocation; complex network theory; end-to-end traffic flow
## 4 Conclusions  
This work focused on investigating the effect of mixotrophic conditions induced by utilizing BWW, as a sustainable substrate, on the biomass growth, carotenoids, and accumulation of astaxanthin by \(H\). _phvialis_. Mixing BWW with the BG11 medium in various ratios, along with the application of light stress conditions, considerably improved both biomass production and astaxanthin accumulation. The combination of BWW and BG11 in a 1:1 ratio was the most effective, yielding the highest biomass (5.46 g L\({}^{-1}\)) and astaxanthin content (2.32 %dw), underscoring the synergistic benefits of organic carbon from BWW and essential nutrients from BG11. This study also demonstrated the key role of light intensity in enhancing carotenoid synthesis. Cultivation under an initial low-light phase followed by high light exposure further improved the production of astaxanthin, confirming that stress-induced conditions are beneficial for secondary metabolite accumulation. The use of BWW in algal cultivation can greatly increase the techno-economic feasibility of astaxanthin production using microalgae in a framework of the circular bio-economy, turning waste into valuable bioproducts. Future studies should focus on the scalability of this method and explore its applicability in large-scale commercial systems.  
Conceptualization, G.A.L. and W.Z.; methodology, G.A.L. and L.C.; software, A.C.; validation, G.A.L., A.C. and E.D.; formal analysis, G.A.L.; investigation, G.A.L.; resources, T.L.; data curation, A.C.; writing--original draft preparation, G.A.L.; writing--review and editing, G.A.L., E.D., A.C. and T.L.; supervision, W.Z and L.C.; funding acquisition, T.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Qingdao Municipality Program Research Bursary [Y43304110E], National Natural Science Foundation of China [41276144], and Science and Technology Development Planning of Shandong Province [2013 GHY11520].  
Not applicable. The study didn't require ethical approval.  
Data Availability Statement: Data will be available upon request.  
The authors declare no conflicts of interest.
## 3 Internal Contact Pressure Model Validation  
To validate the PIC theoretical model for contact pressure established in the previous chapter, a comparative analysis was conducted between the computational outcomes and finite element simulation results, and the similarity of the law is illustrated by comparing with the experimental results in other studies.  
Theoretical validation involves studying and calculating using parallel wires 91\(\phi\)7 as the subject. In the model, the length of the cable is 60 m, the weight per unit length is 40 kN/m, the elastic modulus of the wire is taken as 1.97 GPa, Poisson's ratio is 0.3, the radius of the wire is 3.5 mm, the twist angle is set at 5\({}^{\circ}\), and the friction coefficient is 0.3.
## 1 Introduction  
With the increase of esthetic requirements, facial's micro and macro-esthetic and smile have become a priority for adolescents and adults [1, 2]. Consequently, patients lean to more esthetic and comfortable orthodontic treatments [3].  
In 1945, [PERSON] introduced the concept of clear aligners, that aimed to stress minor tooth movement, usually at the end of orthodontic treatment or to treat minor alignment relapse [4]. Since 1998, clear aligners have become popular and quickly the preferred orthodontic appliances for patients with high esthetic demands [5, 6]. In addition, mechanical tooth movement is associated with cognitive, affective and behavioral responses [7] and periodontal mechanoreceptors and chewing muscles (as source of pain receptors) also contribute to pain experience [8; 9].  
Clear aligners are esthetically more appealing than fixed appliances with brackets and wires [1] and patients benefit by being able to have full access to remove clear aligners to eat and for oral hygiene. Still, patients treated with clear aligners have better periodontal health than those with fixed appliances, and they promote better compliance with oral hygiene in teenagers [10; 11; 12].  
Moreover, pain experience after initial archive placement in fixed appliances is well established, comparing multistrand stainless steel and superelastic NiTi archives [13; 14; 15; 16; 17; 18; 19; 20]. However, the difference in the pain perception of clear aligners compared with fixed appliances remains unclear. A recent systematic review concluded that orthodontic patients treated with Invisalign appear to feel lower levels of pain than those treated with fixed appliances during the first few days of treatment, however they were not able to synthetize the magnitude of this difference [21].  
Patients choose clear aligners in hope that these appliances will have low impact on their quality of life [22]. Therefore, it becomes relevant to evaluate whether there is a difference in pain perception between fixed appliances and clear aligners. In this sense, we may contribute to a more grounded decision by the patient and the clinician.  
Our main objective in this systematic review was to compare the discomfort levels between clear aligners and fixed appliances. Second, we analyzed analgesic consumption difference between the aforementioned orthodontic appliances. The review PICO research question is \"Do clear aligners have less pain discomfort impact than fixed appliances treatment in orthodontic patients?\", with the following statements: orthodontic patients (patients--P); clear aligners (intervention/exposure--I); fixed appliances (comparison--C); pain discomfort (outcome--O). Our null hypothesis was that clear aligners cause similar pain perception than conventional fixed appliances.
# A Monopole UWB Antenna for WIFI 7/Bluetooth and Satellite Communication
## 2 Structure and Analysis  
### Antenna Geometry  
The antenna structure design is shown in Figure 1. The antenna is feed by a 50 \(\Omega\) microstrip feed line, the patch is printed on FR-4 substrate (\(\epsilon_{\rm r}\) = 4.4, tan \(\delta\) = 0.02) using copper material, tin is sprayed on the copper to facilitate soldering of the SMA interface, and the antenna size is 46 \(\times\) 42 \(\times\) 1.6 mm\({}^{3}\). The upper surface of the antenna is composed of microstrip lines and improved short-sleeved metal, while the bottom surface is a rectangular metal ground. To expand the bandwidth and improve the impedance-matching capability, this paper modifies the short-sleeve-shaped patch. By etching a semicircular slot on the radiator and subtracting the left and right corners of the lower half of the radiator, the bandwidth of the antenna is effectively improved. At the same time, in order to improve the impedance-matching effect of the antenna at high frequency (11 GHz), two L-shaped metal patches are symmetrically arranged on both sides of the microstrip line. Moreover, the simulated and measured results validate the feasibility of operation. Table 1 lists all the parameters for the proposed antenna.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value/mm** & **Parameter** & **Value/mm** \\ \hline W & 42 & W\({}_{5}\) & 1.1 \\ L & 46 & L\({}_{5}\) & 8 \\ W\({}_{1}\) & 2.9 & W\({}_{6}\) & 1 \\ L\({}_{1}\) & 14.5 & L\({}_{6}\) & 6.1 \\ W\({}_{2}\) & 14 & W\({}_{7}\) & 0.4 \\ L\({}_{2}\) & 16.5 & L\({}_{7}\) & 1.2 \\ W\({}_{3}\) & 2.4 & L\({}_{8}\) & 3 \\ L\({}_{3}\) & 6.3 & L\({}_{9}\) & 12.6 \\ W\({}_{4}\) & 0.5 & R\({}_{1}\) & 10 \\ L\({}_{4}\) & 6 & R\({}_{2}\) & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Recommended design dimensions for antenna.  
Figure 1: Geometry and parameters of the proposed antenna.
## 5 IETF ROLL Working Group
### Research Challenges
#### 6.1.2 CoRE Link Format  
Resource discovery is important for machine-to-machine (M2M) interactions, and is supported in CoAP using the _CoRE Link Format_ as described in RFC 6690 [87]. A well-known relative URI \"/.well-known/core\" is defined as a default entry-point for requesting the list of links about resources hosted by a server. Once the list of available resources is obtained from the server, the client can send further requests to obtain the value of a certain resource. The example in Figure 15 shows a client requesting the list of the available resources on the server (GET/.well-known/core). The returned list shows that the server has, amongst others, a resource called/s/t that would return back the temperature in degrees Celsius. The client then requests the value of this resource (GET/s/t) and gets a reply back from the server (23.5C).
# State of Health Prediction for Lithium-Ion Batteries Using Transformer-LSTM Fusion Model
## 4 Results and Discussion
### Generalization Performance  
In Section 4.3, SOH prediction for the same battery demonstrates that the sizes of the training and test sets are uneven due to the varying end-of-life cycles of each battery, which partially evaluates the model's generalization capability. Despite these differences, the model demonstrates its generalization capability by predicting the battery's SOH changes with reasonable accuracy. In Section 4.4, the training set is constructed using batteries from different batches, which further improves the generalization ability of the model. In particular, the data of the \"2018-04-12\" batch is used as a secondary dataset. This batch is one year apart and uses different batteries for prediction. As shown in Table 4, the average _RMSE_ and _MAE_ values predicted by the model on the two test sets are shown. The results thoroughly verify the adaptability and robustness of the model when facing different batteries, and further prove that prediction ability based on the transformer-LSTM model can still be stable under different batteries and different usage cycles.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Set** & _RMSE_ & _MAE_ \\ \hline Primary test set & 0.00711 & 0.00557 \\ Secondary test set & 0.00785 & 0.00706 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Prediction results of the transformer–LSTM model on two test sets.  
Figure 12: (**a**) Prediction results for battery with batch number 44 on “2017-05-12”, (**b**) prediction results for battery with batch number 48 on “2018-04-12”.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**2017-05-12\_44**} & \multicolumn{2}{c}{**2018-04-12\_48**} \\  & _RMSE_ & _MAE_ & _RMSE_ & _MAE_ \\ \hline LSTM & 0.01325 & 0.01064 & 0.01034 & 0.00900 \\ CNN–LSTM & 0.00976 & 0.00768 & 0.00877 & 0.00739 \\ Proposed & 0.00209 & 0.00151 & 0.00180 & 0.00144 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Prediction evaluation indicators based on different methods.