esa-sceva/satcom-chunk-collection huggingface dataset:
# Diverse Feature-Level Guidance Adjustments for Unsupervised Domain Adapative Object Detection
## 2 Related Works
### Unsupervised Domain Adaptation for Object Detection  
Compared to classic detectors, domain adaptation posits that there exists an inherent domain shift between training data and validation data. To mitigate the performance degradation caused by domain shift [1], researchers have proposed various methods based on Unsupervised Domain Adaptation. These methods include data alignment, feature alignment, and adversarial training [2], as well as other strategies like disentangled representation learning [28] and style-transferred methods [29], and so on.  
Regarding data distribution differences, [PERSON] et al. [30] and [PERSON] et al. [31] incorporated the exchange of features' low-frequency spectral information in the frequency domain between different domains. Furthermore, [PERSON] et al. [32] proposed the intermediate domain, which generated synthetic data by Cycle GAN [33] to mimic target domain data. Nevertheless, the effectiveness of domain adaptation through data-level alignment is limited. Researchers then shifted their attention to feature adaptation through feature alignment. Methods like DA Faster R-CNN [1] pioneered the introduction of both image-level and instance-level feature alignment strategies, incorporating consistency regularization based on the prediction of the two classifiers. [PERSON] et al. [8] measured the prediction of image-level and instance-level classifiers. The matching results are produced and used as standards to separate samples of the foreground objects. [PERSON] et al. [9] achieved feature alignment through multi-granularity level alignment strategies, such as pixel-level, instance-level, and category-level strategies.  
Since applying local regions' information [14; 15; 16; 34; 35; 36] with instance-level alignment, these recent works have achieved significant improvements in detection performance. However, domain adaptation requires more than the alignment of features from backbone or RPN. It is fundamental to take contextual connections between features into account.  
Our PMA loss compensates for the insufficient information interaction between the source domain and the target domain in context. Furthermore, enhancing the perception and alignment of foreground information is crucial and cannot be ignored. Although some methods [1; 8; 9] adopt category consistency regularization to enhance the alignment of category information, they also overfit substantial background information. To selectively focus on foreground information, ATCA is proposed for adaptively partitioning samples as foreground samples, background samples, and ignored samples. Consequently, based on the Faster R-CNN [17] framework, a pixel-wise contextual alignment strategy and an adaptative sample division strategy are presented, as detailed in the next section.
## 5 Experiments and Results
### Statistical Transparency  
In this section, the statistical transparency of the proposed system is evaluated in terms of Kullback-Leibler (KL) divergence. KL divergence between probability distributions \(P_{Q_{0}}\) and \(P_{Q_{1}}\) is defined as follows,  
\[D(P_{Q_{0}}||P_{Q_{1}})=\sum_{q\in Q}P_{Q_{0}}(q)\log\frac{P_{Q_{0}}(q)}{P_{Q_{1 }}(q)} \tag{14}\]  
with \(0\log\frac{0}{0}=0\) and \(p\log\frac{p}{0}=\infty\) if \(p>0\). According to the authors of [32], a steganographic system is called \(\epsilon-\)_secure_ if  
\[D(P_{\mathrm{C}}||P_{S})\leq\epsilon \tag{15}\]  
where \(P_{\mathrm{C}}\) is the distribution of \(C\), \(P_{\mathrm{S}}\) is the distribution of \(S\), and \(\epsilon\) is an arbitrary low number. If \(\epsilon=0\), the steganographic system is a \"perfectly secure\" case. However, very low \(\epsilon\) values are enough for steganographic systems for real-world signals. Figure 13 shows KL divergence results for both the additive and multiplicative strategies.  
KL divergence median values for the additive and multiplicative strategies were calculated as \(3.7421\times 10^{-4}\) and \(2.0380\times 10^{-4}\), respectively.  
Figure 12: BER results for watermarking using the multiplicative strategy and incorrect key. (**a**) BER results. (**b**) BER distribution.  
Figure 13: Kullback–Leibler divergence results. (**a**) Additive strategy. (**b**) Multiplicative strategy.  
In [32], the probability \(\beta\) that the adversary does not detect the presence of the embedded message is defined as  
\[\beta\geq 2^{-\epsilon} \tag{16}\]  
For the proposed system, the probability that the adversary does not detect the presence of the embedded message is \(\beta=0.99\) for both the additive and multiplicative strategies; thus, the proposed scheme is secure in terms of the Kullback-Leibler divergence test.
## 2 Underwater Wireless Sensor Network
### Communication Architecture
#### 2.3.2 Mobile UWSNs  
In mobile UWSNs' architecture, sink nodes are deployed on the water's surface and equipped with both radio and acoustic modems, while sensor nodes are deployed at different depths of water and equipped with acoustic modems. The depth of the nodes is controlled by buoyancy control and they can move freely with the water currents [13; 31]. The horizontal movement of these sensor nodes is about 1-3 m/s, while their vertical movement is negligible [4; 34]. Sensor nodes sense underwater events and then forward the information to the sink nodes via acoustic waves. Next, the sink transmits the packets to the onshore base station via radio waves. Most of the routing protocols in UWSNs are designed for mobile UWSNs architecture since it is more challenging compared to static UWSNs architecture.
## Appendix A Derivation of Numerical-Dispersion Relation for FO FDTD
Hence, the stability condition can be written as  
\[2^{\frac{1}{2}}\xi_{\alpha}\leq 2.\] (A26)  
After expanding \(\xi_{\alpha}\), (A26) gives  
\[2^{\frac{1}{2}}\frac{\Delta t}{(\sqrt{\varepsilon_{\alpha}\mu_{\alpha}} \Delta z)^{\frac{1}{\alpha}}}\left|\sin\left(\frac{\xi_{\alpha}\Delta z}{2} \right)\right|^{\frac{1}{\alpha}}\leq 2.\] (A27)  
Transforming further, one obtains  
\[\Delta t\leq 2^{1-\frac{1}{2}}(\sqrt{\varepsilon_{\alpha}\mu_{\alpha}} \Delta z)^{\frac{1}{\alpha}}\frac{1}{\left|\sin\left(\frac{\xi_{\alpha}\Delta z }{2}\right)\right|^{\frac{1}{\alpha}}}.\] (A28)  
The minimum value of \(\frac{1}{\left|\sin\left(\frac{\xi_{\alpha}\Delta z}{2}\right)\right|^{\frac{ 1}{\alpha}}}\) is equal to 1. The stability condition can therefore be written in its final form  
\[\Delta t\leq 2^{1-\frac{1}{2}}(\sqrt{\varepsilon_{\alpha}\mu_{\alpha}}\Delta z )^{\frac{1}{\alpha}}.\] (A29)  
Taking \(\alpha=1\), one obtains the standard Courant condition for one-dimensional IO FDTD  
\[\Delta t\leq\sqrt{\varepsilon_{0}\mu_{0}}\Delta z.\] (A30)
## 4 Development and Simulation of a Predictive S&OP Methodology
### Development of the Conceptual Model
#### 4.1.2 Development of the Conceptual Predictive Sales and Operations Planning Model
In the medium term, preventive planning is generated based on a comparison of expected and real demand to detect deviations and decide whether to implement the options defined in the long-term scenario. In this time horizon, the supply chain system dynamics are analyzed, the similarities to the long-term scenarios can be identified, and the planning can be adjusted based on the deviations among the scenarios. In the short term, measures are related to adjustments of the options prepared in the long term and implemented in the medium term. Unexpected events or a lack of preparation for expected events leads to corrective adjustments in the short term. These adjustments have a low impact on avoiding a decrease in system efficiency. In Table 1, the measures that can be implemented in the supply chain simulation model are described in relation to the expected inefficiencies. As shown in Table 1, the predictive model within a supply chain seeks to detect potential inefficiencies within the given scenarios. Then, we can detect the cause of the inefficiency to determine the potential solutions. In this way, preparation of the solutions can be performed while allowing a shorter reaction time if the measure is intended to be implemented in the future. By following this sequence, any given supply chain within its areas of procurement, production, or distribution in any given set of scenarios can be modelled using system dynamics to identify the expected inefficiencies of the different scenarios, to perform a root-cause analysis, and to define the potential measures or solutions to treat the causes. By doing so, the future system stability is enhanced, and managers can make decisions based on the organizational strategy, goals, and current situation while understanding the future implications of engaging, or not engaging, in such decisions. Given the different scenarios, a predictive demand plan for the planning intervals is derived. Based on this plan and the real demand collected after each period, the original  
Figure 4: Planning based on the time horizon: predictive, preventive, and corrective (own elaboration). demand plan is adjusted based on the average forecasted error between the planned and current demand. As a result, the operations plan can also be adjusted, and management decisions can be guided with the best available information. This planning loop is shown in Figure 5.
## 6 Evaluation
### Performance Evaluation
#### 6.2.4 Recovery Overhead and False-Positive Error Analysis  
Although Bloom filters are efficient in terms of memory and space, their probabilistic nature can lead to false positives. This means that it is possible for a file to be marked as sent even if it was not actually transferred. As a result, the transferred dataset may be incomplete and not suitable for further analysis. Therefore, avoiding false-positive errors while minimizing the recovery time is another critical design aspect of the proposed data integrity framework. This section presents the recovery time overhead and false-positive error analysis.  
To analyze the overhead of recovery and the number of false positives, we artificially introduced errors in the data after transmitting 20%, 40%, 60%, and 80% of the total data. Furthermore, to evaluate the efficacy of the proposed CRBF-based data integrity framework, we randomly modified 10% [16] of the files successfully transferred at the time of the fault.  
Figure 10: Memory load analysis. (**a**) Memory load comparison. (**b**) Bloom filter memory overhead comparison.  
Figure 11 depicts the recovery overhead of the proposed framework for all workloads, as defined in Section 6.1.2. We represented the time taken before the fault, after the fault, and the total recovery time as stacked histograms. From Figure 11, we can observe that, recovery time is higher at higher fault points. This is because the later the fault point, the higher the number of objects to be processed. For example, the 80% fault point has a recovery time of 2%, 9%, and 3% for the D1, D2, and D3 workloads, respectively. Furthermore, Figure 12 depicts the recovery overhead comparison of the TPBF and CRBF frameworks for all workloads. Figure 12 reveals that, for D1 and D3 workloads, CRBF exhibits slightly a lower overhead than TPBF. However, for D2 workloads, both frameworks exhibit a similar overhead. Thus, the recovery overhead of the CRBF framework is negligible compared to the total data transfer time. In addition, the experimental results show that the proposed framework can successfully detect integrity errors and transfer all modified files during fault recovery.  
Figure 13 depicts the false positives of the proposed CRBF framework along with the state-of-the-art/_Sum_ solution and TPBF framework for D3 workloads. This graph illustrates that the proposed CRBF framework outperforms both _/Sum_ and the TPBF in eliminating false positives at all simulated fault points.  
We ran multiple iterations of the experiments with all workloads, and in all cases, we observed no false positives for any workload at any fault point. Based on these results, we conclude that the CRBF method of data integrity effectively eliminates false-positive errors while minimizing recovery time.  
Figure 11: Recovery overhead analysis.
## 6 Experimental Results and Discussion
### Comparison with Other Models  
In this subsection, we conduct a comprehensive comparison of four models: a two-block RNN, BRITS, the GRU encoder-decoder, and our LSTM-GRU encoder-decoder model. The evaluation was primarily based on their respective L1 loss values, providing a quantitative measure of their performance.  
In Table 5, the L1 loss values for each model are presented. The two-block RNN model yielded an L1 loss of 0.363, followed by BRITS with a loss of 0.312. The GRU-based encoder-decoder model exhibited an L1 loss of 0.306. Notably, our proposed LSTM-GRU encoder-decoder model outperformed the others with an impressively low L1 loss of 0.2264.
## 4 Qualitative and Quantitative Analyses
### Quantitative Analysis
#### 4.2.2 Slope Aspect  
Slope aspect STD serves as a statistical measure of internal consistency within slope units by quantifying the variability of aspect grid values. Using identical analytical procedures, Figure 9 compares the STD distributions between SUD-CGHP-derived and HPAM-derived slope units. The results reveal distinct patterns: SUD-CGHP units exhibit a dominant proportion (over 20%) of STD values in the 0-20% range (Figure 9a), while HPAM-derived units are characterized by higher dominance in the 20-40% range. Cumulative frequency analysis further demonstrates that SUD-CGHP maintains superior  
Figure 8: (**a**) Variety of SUD-CGHP; (**b**) slope unit via SUD-CGHP. (**c**) Variety of HPAM. (**d**) Slope unit via HPAM.  
cumulative percentages across all STD intervals up to 40%, converging with results by HPAM only at extreme values (>40%). These findings collectively indicate that SUD-CGHP achieves greater internal aspect consistency compared to HPAM.  
Figure 9b,c present directional distribution patterns and their associated average aspect STDs for both methods. While both approaches yield comparable directional distributions across all slope units, significant differences emerge in STD performance: SUD-CGHP demonstrates superior consistency in the northwest (NW), north (N), and northeast (NE) sectors while showing inferior results in the southwest (SW) and south (S) directions compared to the HPAM. This directional variability reveals that SUD-CGHP achieves optimized balance between geometric coherence and hydrological representativeness, particularly in regions influenced by dominant northward drainage systems. The methodological strength lies in its ability to maintain natural aspect variability while minimizing artificial discretization artifacts commonly associated with traditional watershed-based approaches.  
Figure 10 presents a comparative visualization of slope aspect STDs between SUD-CGHP-derived and HPAM-derived slope units, accompanied by slope unit examples obtained using two methods and background aspect grids. At the regional scale, SUD-CGHP units (Figure 10a) exhibit high-standard-deviation values that are primarily isolated as point-like clusters embedded within extensive low-standard-deviation zones. In contrast, HPAM-derived units (Figure 10c) display higher-standard-deviation regions predominantly as contiguous patches covering larger areas, suggesting less spatial fragmentation. When analyzing intra-unit aspect variability, SUD-CGHP-derived slope units demonstrate significantly greater internal homogeneity in aspect grid values compared to their hydrological counterparts. This superior consistency indicates that SUD-CGHP achieves more physiologically plausible segmentation by preserving natural aspect variability patterns while avoiding artificial discretization artifacts commonly associated with traditional watershed-based approaches.  
Figure 9: STD of aspect. (**a**) STD frequency statistics; (**b**) mean aspect distribution of slope units; (**c**) average STD in all directions.
###### Abstract  
The present study aimed to compare the stability of dental implants placed in low-quality bone prepared for the healing chamber with osseodesification technique and a standard undersized drilling. Sixteen subjects presenting D3 or D4 bone density according to [PERSON]'s classification were randomly distributed to receive dental implants following either osseodesification (G1: \(n=29\)) or standard undersized drilling (G2: \(n=26\)) preparation techniques. Implant insertion torque (IT) and implant stability quotient (ISQ) were measured immediately after implant placement. Implant survival and secondary stability (ISQ) were evaluated after six months. The G1 group showed higher IT (39.0 \(\pm\) 6.4 Ncm) than G2 (32.0 \(\pm\) 3.4 Ncm) (\(p<\) 0.001). ISQ values were similar (\(p>\) 0.05) at the implant insertion (67.1 \(\pm\) 3.2 and 65.5 \(\pm\) 2.7 for G1 and G2, respectively). After six months healing, implant survival was equally comparable in both groups (\(p>\) 0.05), and ISQ values were higher than those of implant insertion (\(p<\) 0.001) but similar (\(p>\) 0.05) for both groups (74.0 \(\pm\) 3.6 and 73.3 \(\pm\) 3.2 for G1 and G2, respectively). Within the limitations of this study, the present RCT demonstrated that a wider surgical bed prepared by osseodesification instrumentation allowed for the bone healing-chamber concept in low-quality bone without any reduction in implant stability and success rate.  
osseodesification; clinical trial; bone chamber healing; clinical assessment; bone-implant interactions
## 2 Materials and Methods
### Data Processing  
For comparison of the reflectivity of uncoated and CsI-coated steel with tabulated values, the measured broadband XUV spectra were processed in the following way:  
1. The measured spectra were converted from wavelength to photon energy scale.
2. These spectra were normalized and the offset noise of the spectrometer was subtracted.
3. These spectra were interpolated to 1000 data points and multiplied with the tabulated reflectivity values either from iron or CsI.
4. Both were then spectrally integrated to obtain the integrated reflected XUV yield \(R\). From this, the ratio \(\frac{R_{Fe}}{R_{CsI}}\) was calculated.
5. The same ratio can be determined using the reflection by the uncoated or coated steel surface onto the Channeltron. Here, the temporally resolved signal must be integrated in a time interval for both surfaces, and its ratio again is \(\frac{R_{Fe}}{R_{CsI}}\).
# Optimization of Coverage and Capacity Using Smart Antennae
## 2 Small-Cell Interference
Assuming there were N base stations in the entire network system, each base station had M levels of adjustable power and C antenna directions. The total number of possibilities was then (M \(\times\) C)\({}^{\text{N}}\). The research objective of this study was to determine the power level and the combination of antenna directions (on or off) for each base station throughout the network system in a way that simultaneously achieved a high network coverage rate and reduced UE signal interference issues. Figure 4: Smart directional antenna.
# GPS + BDS Network Real-Time Differential Positioning Using a Position Domain Estimation Method
## 3 Results  
### Static Experiment  
Two sets of data, defined as Data set I and Data set II, were selected in the Guangdong Province of China, as shown in Figure 2. The data were collected from 00:00:00 to 23:59:59 (UTC, Universal Time Coordinated) on day of year 77, 2019 (18 March 2019), and from 00:00:00 to 23:59:59 (UTC) on day of year 112, 2019 (22 April 2019), respectively. The sampling interval was set as 1 s, and the cut-off angle was set at 10\({}^{\circ}\). In Data set I, the stations LVTI, HELI and SSGT were chosen as the reference stations to form a triangle network, and the other four stations were selected as the user stations. The stations SHAT and SHJU are located inside the network, while the stations HUAD and HMSC are outside. In Data set II, the stations HJGT, HZDY and YXGT were chosen as the reference stations, and the other three stations ZQGT, GMGT and JMGT were selected as the user stations. The GAMIT/GLOBK 10.6 software developed by Massachusetts Institute of Technology, Cambridge, MA, USA. [39; 40], was used to obtain precise coordinates for all stations as the reference true values.  
The network real-time differential positioning results were used to evaluate the performance of different position estimation methods. The traditional algorithm in the range domain and the improved algorithm in the position domain were denoted as RD and PD, respectively. The performances of GPS + BDS and GPS-only solutions were compared as well. The position errors between the network real-time differential positioning results and the true values were calculated. The root mean square (RMS) errors, the standard deviation (STD) values and the percentages of the position errors falling within predefined thresholds of \(\pm\)0.25 m, \(\pm\)0.5 m and \(\pm\)1 m were discussed.  
The thresholds were derived from [PERSON] et al. [17], where it is indicated that most of the position errors in the network real-time differential positioning are smaller than 1 m, and the positioning accuracy are around 0.25 m in the north (N) and east (E) components, and 0.5 m in the up (U) component. Moreover, the 0.5 m threshold also coincides with the positioning accuracy requirement of \"Where in Lane\" in connected and autonomous vehicles [41].