esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology and Implementation
### ZeroC-Ice Implementation on SMP-OTF41 OT  
We implement the protocol in ZeroC-Ice v.3.7, (Internet Communication Engine developed by ZeroC), as described in [16], using Python 3.12, as the implementation language. The advantage of using this approach lies in the possibility of connecting three devices from three different platforms and architectures, for example, a single-board computer (Raspberry Pi), a mobile device, and a cloud application. Each platform runs the corresponding script according to the current role in the protocol, i.e., client, server, and dummy, according to Listing 1.  
```
moduleSMPC{ interfaceServer{ voiddeliverFromClient(intshare, outintshareServer, outintshareDummy, outintshumDummy, voiddeliverFromServer(intshare, outintshareDummy); }
```  
```
interfaceDummy{ voiddeliverFromClient(intshare, outintshareDummy, outintshumDummy, outintshumDummy); }
```  
Figure 3: Simple SMP-OTF41 OT’s “Hello secret world”: client sending encrypted message and server receiving and decrypting the message.  
It is essential to mention that the server can retrieve the key and the message in a single call. Similarly, when making a remote call, it is possible for the call to return multiple values. For example, the server can return their share and partial sum as a tuple of integers.  
By adopting this method, the number of messaging instances for communication is reduced from 11 to 8, including the transmission of the encrypted message (approximately 30% reduction in bandwidth). Notice that the roles described above (Client, Server, Dummy) are used 'as a whole' to secretly communicate between two parties by using (true) random number acquisition and a dummy third party that is not granted access to the shared encrypted message. However, these roles can be continuously and dynamically changed so that, in principle, the protocol extends naturally to peer-to-peer secure communication between any number of IoT agents in a real-life application. Note that, if a message \(m\) of size \(|m|\) must be transmitted while encrypted according to our algorithm, then the total communication cost for key generation and transmission of an encrypted message would be approximately twice the size of the message simply because, in our setting, the communication time between parties \(U_{1}\) and \(U_{2}\) is bounded above by  
\[time(U_{1},U_{2},K\oplus m)\sim\sum_{i,j=1}^{3}|r_{ij}|+\sum_{k=1}^{3}|s_{k}|+| K\oplus m|\leq 2|m|.\]
## 3 Results and Discussion
### Ecological Conditions of the Caspian Sea
#### 3.3.1 Contamination by Petroleum Hydrocarbons  
According to the results of our joint research with <<Biomedpreparat>> LLP, in the Kazakhstan sector of the Caspian Sea, the concentration of petroleum hydrocarbons in Caspian Sea water exceeded the level of fisheries management MPC from 2 to 23 times in 2008; both in 2009 and 2010 up to 16 and 21 times (0.8-1.05 mg/L), respectively, respectively. The maximum concentrations of these contaminants are recorded in the Caspian Sea water affected by the inflow of the Volga River and the Ural River, as well as in areas of active oil fields, such as Tengiz, Kalamkas, Karazhanbas, etc. (Figure 8) [51; 52].  
Fairly high levels of petroleum hydrocarbon, organochlorine pesticide, and heavy metal accumulation in the muscles of marine fish were detected during the period of comprehensive research within the Kazakh sector of the Caspian Sea in 2008-2010. The concentration of petroleum hydrocarbons in the muscles of fish varied in the range from 2.4 to 216 mg/kg in 2008, from 2.3 to 513 mg/kg in 2009. The concentration of contaminants in the muscle of sturgeon fish reached 200-363 mg/kg, and in muscular tissues of herring 212-264 mg/kg.  
From _Cyprinidae_ breeds, the maximum accumulation of this contaminant was up to 107 and 216 mg/kg for _Abramis brama_, and up to 332 and 513 mg/kg for Caspian roaches [53; 54]. For petroleum hydrocarbons, sanitary standards of the maximum permissible level (MPL) in fish have not been developed yet. Figure 9 shows the fishing points and the concentration of petroleum hydrocarbons registered in the muscle tissue of _Acipenser gueldenstaediti_ and _Alosa brashnikovii_ fish species in this zone [55]. The illustration shows that the highest level of accumulation of petroleum hydrocarbons is registered in _Acipenser gueldenstaediti_ caught in water east of the Ural River mouth and in the Kulalynskiy deep water zone. This may be due to increased oil contamination of water and feed facilities in these areas. Also, their concentration in muscles of fish species in the Eastern and South-Eastern sections of the  
Figure 8: Water concentrations and distribution of petroleum hydrocarbons in the eastern part of the Northern Caspian Sea.  
According to the analytical data obtained from representatives of the ichthyofauna of the Kazakh sector of the Caspian Sea sequences of descending concentration of petroleum hydrocarbons in the muscle tissue have been detected: _Rutilus rutilus_, _Acipenser gueldenstaediti_, _Alosa braslnikovii_, _Abramis brama_, _Acipenser stellatus_, _Aspius aspius_, _Sander volgensis_, _Abramis sapa_, _Acipenser ruthenus_, _Cyprinus carpio_, _Sander lucioperca_. The results of the study also suggest that the _Cyprinidae_ (_Rutilus rutilus_ and _Abramis brama_) are promising accumulative bioindicators of contamination of the water ecosystem of the Caspian Sea with oil-based hydrocarbons [53; 55].
# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 2 Technology Overview
### Transmission Parameters
#### 2.3.2 Bandwidth  
In terms of LoRa modulation, bandwidth is a very crucial parameter. A LoRa symbol comprises of \(2^{SF}\) chirps, that spread in the whole frequency domain. A chirp is a signal wherein the frequency increases, called an up-chirp or decreases, known as a down-chirp. The LoRa symbol begins with a set of up-chirps whose frequency increases with time. When it reaches the maximum frequency, it skips back to its lowest frequency and starts over. The down-chirp is the inverse of the up-chirp which starts at the maximum frequency and decreases with time. When it reaches the minimum frequency, it skips back to the maximum frequency and the cycle starts over. BW is a range of frequencies within a given transmission band [22]. High values of BW give higher data rates which implies a shorter ToA. This results in reduced sensitivity because of the additional noise that is integrated. A lower BW produces better sensitivity but achieves lowered data rates. Data transmission occurs at a chip rate corresponding to the BW where a BW of 125 kHz corresponds to a chip rate of 125 kilo chips per second (kcps). While the BW could be selected ranging between 7.8 kHz and 500 kHz, a standard LoRaWAN functions at either 500 kHz, 250 kHz, or 125 kHz (BW500, BW250 and BW125) according to the regional parameters [9].
## 5 Results and Discussion
### Critical Discussion  
Simulation results presented in the previous subsections show that the use of the secured NTRUEncrypt routing algorithm reasonably improves network performance. Simulations have been performed by evaluating different performance metrics, such as throughput, goodput, end-to-end delay, and packet delivery ratio. Figure 15 presents the goodput of all the considered networks. It shows the goodput of a healthy network where no malicious node exists, while it also shows the goodput in the presence of a malicious node and NTRUEncrypt cryptography. It can be observed from Figure 15 that NTRUEncrypt almost achieves the same trend as depicted in the healthy case, even under a malicious node. Similarly, it can be observed from Figure 16a that NTRUEncrypt decreases the delay by 69% compared to the malicious network. However, due to cryptography and processing overhead, NTRUEncrypt increases the delay by 13% compared to a healthy network. As shown in Figure 16b, NTRUEncrypt achieves about 54% higher throughput compared to the malicious network and decreases throughput by 2.5% compared to the healthy network. Similarly, Figure 16c shows that NTRUEncrypt achieves 32% higher PDR when compared to the malicious network. This shows that the proposed system operates safely under black hole attacks, and the cryptography technique has no severe effects on system performance. Moreover, this study helps implement secure smart manufacturing for global and local industries, as shown in Tables 11 and 12 in Appendix A, respectively.
# Fatigue Assessment of Explosive Bolts Considering Vibration of Fixtures
## 5 Fatigue Life Assessment of Explosive Bolt  
The Mises and Tresca stress response of the dangerous point on explosive bolt C when driving on different class roads are counted by rainflow method [14]. The extreme condition, Class E road, is taken as an example to explain the fatigue assessment process during the carrying of the missile.  
It is required that the key release devices will not fail during the 6000 km carrying distance. We take a stress-time response of FEM analysis shown in Figure 8 as a load block, i.e., 400 m carrying distance, ignoring the unstable period induced by the startup. According to [PERSON]'s rule, the fatigue life of the explosive bolt is required to be larger than the 15,000 load block.  
The obtained 178 cyclic stresses rank in an ascending order by stress ratio \(R\), ranging from 0.9777 to 0.9975. Table 4 lists only the first five cyclic Tresca stresses for references.  
Figure 8: Variation of the Mises and Tresca stress of the dangerous point on explosive bolt C with time under Class E road.  
Figure 7: Typical Tresca stress distribution at the weaken groove of the explosive bolt C under Class E road.  
Generally, fatigue damage produced by vibration during driving of a vehicle is a high cycle fatigue issue. Thus, the stress-life method based on S-N curve can express the vibration fatigue life varying with loads by a three-parameter model as  
\[N=A(\sigma_{\max}-\sigma_{0})^{b}. \tag{6}\]  
where \(N\) is the number of stress cycles, i.e., life, and \(\sigma_{\max}\) the applied maximum stress. \(A\), \(b\), and \(\sigma_{0}\) are constants.  
Referring to the axial-fatigue testing data of 30 GrMnSiNi2A in [15], the constant life curves of \(K_{t}\) = 3 and \(K_{t}\) = 5, and the S-N curve at \(K_{t}\) = 1 and \(R\) = 0.1 based on median fatigue life, are given, as plotted in Figures 9-11. In Figure 11, we also give the S-N curves at \(K_{t}\) = 1 and \(R\) = 0.1 with different survival rates \(P\). Concerning Figures 9-11, through linear interpolation, we can get the constant life curves at any given \(K_{t}\), \(P\), and \(R\). Figure 12 shows the constant life curve of \(N\) = \(10^{7}\) at different \(K_{t}\) and \(P\), and the dashed curves are obtained through linear interpolation.  
Among all of the 178 cyclic stresses, \(R_{\min}\) = 0.9777. The corresponding fatigue limit is 1490 MPa, based on fatigue life curve for 99.99% survival rate, which is the minimum one with \(R\) ranging from 0.9777 to 0.9975. However, as we counted, the maximum stress of all the cycles is 1222.32 MPa, lower than the minimum fatigue limit. Hence, based on fatigue life curve for 99.99% survival rate, fatigue damage will not occur when driving on Class E roads and no fatigue damage accumulated. Consequently, with the designed geometry and pretension force in this paper, these explosive bolts can undergo more than 15,000 load blocks, i.e., 6000 km carrying distance on Class A to E roads.  
Figure 11: S-N curves of \(K_{t}=1\), \(R=0.1\) with different survival rates.  
Figure 10: Constant life curve of \(K_{t}=5\) based on median fatigue life.
## 1 Introduction  
The use of platform matching in implant dentistry entails selecting an abutment with a diameter identical to that of the implant platform. On the other hand, the platform-switching approach involves using an abutment with a smaller diameter than the implant platform [1]. Research has indicated that this technique can help preserve the crestal bone surrounding the implants [2]. A defining characteristic of platform switching is the presence of an implant collar and platform that control the micro-gap [3]. However, there is ongoing debate about the effectiveness of this approach. Studies have shown that Morse taper connections, when combined with platform switching, can lead to reduced inflammation and potential bone loss in the peri-implant soft tissues [4]. Furthermore, the 11-degree Morse taper has demonstrated superior resistance to microbial leakage compared to butt-joint connections [5]. Nevertheless, a prior randomized controlled study conducted over one year revealed that conical implant-abutment connections yield comparable peri-implant tissue responses with both platform switching and non-platform switching abutments [6].  
Previous investigations have demonstrated that the clinical and radiographic outcomes were similar between implants restored using platform-switching and platform-matching [7]. Furthermore, a study did not reveal any significant discrepancies in peri-implant marginal bone changes after one year of prosthetic loading between platform-switched implants with a conical connection that were inserted 1 or 2 mm subcrestally [8]. The results of the previously mentioned study disclosed no statistically significant distinctions between platform-switched implants and platform-matched implants in terms of pocket depths during a 6-year follow-up period, with the exception of the loading phase at 24 months and at 72 months [9].  
However, several positive outcomes have been reported in relation to platform switching [8; 9; 10]. The subcrestal placement of implants with platform-switching and an internal conical connection exhibited reduced MBL and demonstrated greater probing depth and peri-implant soft tissue height compared to implants placed at the crestal level after 1 year of functional loading [9]. Similarly, a clinical investigation evaluated the effects of platform switching and the subcrestal placement of implants, suggesting that this approach may be effective in preventing bone loss and preserving esthetics around dental implants [10]. Additionally, a 2 mm subcrestal placement resulted in deeper implant positioning after 1 year of prosthetic loading, with no exposure of the treated implant surface and a potential preventive effect against subsequent peri-implant pathology [8]. Consequently, this study aims to conduct a meta-analysis to assess the differences in the stability of alveolar bone around dental implants and implant failure between platform switching and platform mating. The null hypothesis proposes that there will be no significant difference in the stability of dental implants.
## 5 Discussion
### Efficiency Analysis in Real Scenarios  
The comparative experiment conducted in this study highlighted significant improvements in efficiency and accuracy using the MAMRS compared to manual inspection.  
Specifically, the following merit consideration:  
* The MAMRS achieved accuracies of 87.27% for pointer meters and 93.33% for digital meters, surpassing manual methods in both accuracy and speed.
* The automated nature of MAMRS reduced the time required for inspection, demonstrating its potential for enhancing operational efficiency.  
However, these results were obtained in controlled environments such as underground power distribution rooms. To provide a more comprehensive evaluation of the system's efficiency, it is essential to incorporate additional real-world variables, such as the following:  
**(1)**: **Larger Operational Areas:**  
Future testing will involve larger areas, such as mine tunnels and extraction zones, to assess scalability and navigation performance. Metrics such as time efficiency, coverage area, and energy consumption will be evaluated.  
**(2)**: **Dynamic Environments and Obstacles:**  
The system's ability to navigate and operate in environments with uneven terrain, debris, and moving machinery will be tested to validate its robustness.  
**(3)**: **Economic Impact:**  
A detailed cost-benefit analysis will be conducted to evaluate operational costs and potential savings from reduced labor requirements and increased inspection consistency.  
These additional analyses will ensure that the MAMRS's efficiency is validated under realistic mining conditions.
## 2 Engineering Overview and Numerical Modeling  
### Typical Foundation Pit Profile  
A pile-anchor foundation pit site in Shenzhen, China, is shown in Figure 1, and a typical profile is shown in Figure 2. The soil parameters of the profile are divided into the following four layers from the top to the bottom: (1) artificial fill, the layer thickness of which was around 1 m; (2) granite residual soil, the layer thickness of which was 20.5 m; (3) fully weathered granite, the layer thickness of which was near 4.8 m; and (4) strongly weathered granite. This pit profile is representative, and the support structures involved in this profile include slope spray mixing, piles, a crown beam, waist beams, and anchors. The construction process information is shown in Table 1.  
Figure 1: Foundation pit site in Shenzhen, China.
## 2 Modeling and Research Methods
### Temporally Topological Measures
#### 2.2.4 Strength  
During operational processes, aviation network traffic distribution is often non-uniform. The strength of a node is usually represented by the sum of the routes or flights. Weighted networks can be implemented to better represent and monitor such traffic. Temporal networks, likewise, can also be represented as undirected weighted graphs \(G(t)=(V(t),E(t),w)\), where \(V(t)\) and \(E(t)\) have the same definitions as in undirected unweighted networks previously described. The function \(w:E(t)\rightarrow\mathbb{R}\) maps the set of edges to the real number set \(\mathbb{R}\) and denotes the weight of each edge. The adjacency matrix is defined by the following equation:  
\[A_{ij}(t)=\left\{\begin{array}{ll}w(i,j)&\text{if }(i,j)\in E(t)\\ 0&\text{otherwise}\end{array}\right. \tag{6}\]
# Correction Compensation and Adaptive Cost Aggregation for Deep Laparoscopic Stereo Matching
## 1 Introduction
Figure 1: A schematic diagramof vertical calibration errors. The blue solid lines connect the matching point pairs, and the white dashed lines represent the horizontal lines. These blue key points were detected by SIFT [19] and matched using brute-force method. The angle between these two lines indicates the vertical correction error between the matching points. In summary, the main contributions of this paper are as follows:  
* A correction compensation module is proposed to overcome correction errors. Unlike previous work, we learn about correction error information from a real laparoscopic dataset and compensate for errors in both the horizontal and vertical directions. * An adaptive cost aggregation module is proposed to improve prediction performance in ill-posed regions. We skillfully quantify the value of cost volume elements based on their distribution and theoretically link high-value elements to those in ill-posed regions. By emphasizing high-value elements during the cost aggregation process, we improve the regularization effect on elements in ill-posed areas. * Without ground truth labels, we propose an unsupervised depth estimation method for stereo laparoscopic images based on the proposed modules. Our model outperforms the state-of-the-art unsupervised methods and well-generalized models.
## 4 Discussion  
### The Reliability of Using \(RSEI\) to Characterize the Ecological Environment  
In this study, the spatiotemporal changes of the \(RSEI\) in the Shendong mining area were analyzed. The partial derivative method was used to quantitatively investigate the contribution of human activities and climatic factors (precipitation and temperature) to the spatiotemporal changes of the \(RSEI\). Currently, analyses of the ecological environment are primarily focused on individual environmental factors such as surface vegetation, land surface temperature, soil moisture, and net primary productivity. There is a scarcity of studies that comprehensively analyze multiple environmental factors. The Remote Sensing-based Ecological Index (\(RSEI\)) is a composite index based on remote sensing information, constructed using indicators of wetness, greenness, heat, and dryness [18]. The \(RSEI\) is not a weighted sum of its individual sub-indices; rather, it is an objective index that relies on objective natural and environmental information [7]. \(RSEI\) can objectively couple wetness, greenness, heat, and dryness sub-indices, and can reasonably reflect the current status of regional ecological environmental quality.  
Figure 10: The relative contribution of climate and human activities to the change of \(RSEI\); Contribution of climate factors (**a**); and human activities (**b**) to \(RSEI\) changes. (**c**) Dominant factor zoning of \(RSEI\) changes.
## 4 Discussion  
### Discrepancies in Model Capability Across Temporal Scales  
The model performs best at short temporal scales (e.g., half-hourly), but its performance deteriorates as the temporal resolution increases to monthly and annual scales, due to the accumulation of systematic errors. This highlights the model's varying capacity to represent processes across different time scales, which primarily stems from its differential ability to characterize dominant processes and transmit the \"memory effect\" of slow variables.  
At sub-daily to daily scales, fluxes are primarily driven by immediate atmospheric forcing and rapid physical and physiological responses of the vegetation canopy and upper soil layers. These processes are generally well-represented in Noah-MP through established physical laws and biochemical equations [17].  
However, at longer time scales (e.g., monthly, seasonal, or annual), system memory becomes increasingly important--meaning that current flux states are not only influenced by the immediate forcings, but by the accumulated conditions of slow variables over time, such as deep soil moisture, vegetation biomass, and LAI. Noah-MP's simulation of these slow processes often relies on empirical parameters, simplified assumptions, and limited representation of complex feedbacks. These structural uncertainties, coupled with imperfect coupling among model components, contribute to the accumulation and amplification of short-term biases over time [48; 49; 50].