esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Probiotic S. salivarius Reduced C. albicans Counts  
Counts of _C. albicans_ were obtained from all patients at T0 and T30 by collecting two different samples, a swab streaked for a length of 30 mm on the diseased mucosa underlying the denture and an oral rinse with 10 mL of sterile physiologic saline. As expected from previous reports [25], counts obtained from oral rinses were, on average, 2.5 times higher than those obtained from swabs.  
While counts of _C. albicans_ obtained at T0 from samples of patients of both the EXP and CTR group were comparable, analysis of both swab and rinse samples showed that the EXP group underwent a reduction of about 60% of _C. albicans_ counts between T0 and T30, whereas the reduction in the CTR group, although significant, was only about 20% (Figure 2). Colonies with phenotypes typical of non-_albicans Candida_ spp. were observed in many cases but their counts were always low so that they were considered as not relevant for the purposes of the study.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Group**} \\ \cline{3-6}  & & \multicolumn{2}{c}{**Control**} & \multicolumn{2}{c}{**Experimental**} \\ \cline{3-6}  & & **Count** & **Column N \%** & **Count** & **Column N \%** \\ \hline Pain T0 & No & 0 & 0.0\% & 0 & 0.0\% \\  & Yes & 25 & 100.0\% & 25 & 100.0\% \\ Pain T30 & No & 11 & 44.0\% & 18 & 72.0\% \\  & Yes & 14 & 56.0\% & 7 & 28.0\% \\ Pain T60 & No & 8 & 32.0\% & 21 & 84.0\% \\  & Yes & 17 & 68.0\% & 4 & 16.0\% \\ Dryness T0 & No & 14 & 56.0\% & 16 & 64.0\% \\  & Yes & 11 & 44.0\% & 9 & 36.0\% \\ Dryness T30 & No & 20 & 80.0\% & 23 & 92.0\% \\  & Yes & 5 & 20.0\% & 2 & 8.0\% \\ Dryness T60 & No & 18 & 72.0\% & 23 & 92.0\% \\  & Yes & 7 & 28.0\% & 2 & 8.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Distribution of pain and dryness between groups before treatment (T0), after 30 days of treatment (T30) and after 30 days after treatment had stopped (T60).  
Figure 2: _Candida albicans_ counts before (T0) and after 30 days treatment regimens (T30) with (EXP) and without (CTR) probiotic _S. salivarius_ BLIS®K12. _C. albicans_ counts obtained by two distinct sampling procedures (swap and rinse) are represented in the plot and reported in the explanatory table as colony forming units/ml (CFU/mL) means (\(\pm\)standard deviation). Significance of differences between single groups of data was calculated by the Student T test and is reported in the table as values of \(P\).
## 6 Conclusions
On average, with the increase in R + LT value for a single time unit, average Well-to-Wheel emissions decrease by 7.5% in a 5-day and 8.4% in a 7-day WS. Maximal Well-to-Wheel emissions occur for instantaneous deliveries with R = 1 and LT = 0 days. Reducing the R + LT period from 30 to 1 increases total emissions of transport 12.3 times in a 5-day and 16.3 times in a 7-day WS. The average Well-to-Wheel emissions for a 7-day is approximately 26% higher than that for a 5-day WS. The change in demand variability does not significantly influenceemissions. In 99% of R + LT groups, average Well-to-Wheel emissions are higher for a fill rate of 99.9% compared to 89.9%. They are, on average, 7.0% higher in a 5-day WS compared to 9.5% in a 7-day WS, regardless of demand variability. Differences between maximal and minimal absolute Well-to-Wheel emissions within R + LT groups are above 62%, regardless of the WS and demand variability. Our results confirmed that simulation experiments are necessary for the optimal management of SCs since minimal changes, such as extending the lead time for one unit or changes in market demand variability, can have significant economic and environmental benefits. In a global market where GHG emissions are part of the decision process, the influence and importance of systematic simulation analysis are even more significant. These findings are particularly relevant for inventory managers and researchers. In a real-world environment, performance can negatively differ from results presented in this research due to human decision-making, shorter observed periods, and rationalization, all common factors in supply chain management. Future research in inventory simulation for supply chains will focus on multi-objective optimization, dynamic models, and carbon-neutral strategies, requiring sophisticated IT infrastructure and advanced technologies. Prerequisites include access to real-time data, extensive simulations, cross-disciplinary collaboration, and the adoption of cloud computing, AI, and IoT to create resilient and sustainable supply chains. These advancements will allow businesses to balance economic and environmental goals more effectively while enhancing supply chain transparency, adaptability, and performance. Conceptualization, J.Z., S.Z. and G.D.; methodology, J.Z., S.Z. and G.D.; software, S.Z. ; validation, J.Z. and S.Z. ; formal analysis, J.Z. and S.Z. ; investigation, J.Z. and S.Z. ; resources, J.Z. and S.Z. ; data curation, J.Z. ; writing--original draft preparation, J.Z. and S.Z. ; writing--review and editing, J.Z., S.Z. and G.D.; visualization, S.Z. ; supervision, G.D. and S.Z. ; project administration, J.Z. ; funding acquisition, J.Z. and S.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors. The authors declare no conflicts of interest.
# Stability Analysis of Soil Flow Protector and Design Method for Estimating Optimal Length
## 3 Results and Analysis  
### Settlement  
The largest settlement occurred in areas adjacent to the structure, similar to the results reported by [PERSON] et al. [9]. However, the various ground settlements studied by [PERSON] et al. [3] and [PERSON] et al. [2] were mostly due to surcharge or the weight of the ground. In this study, the forced settlement was due to the vibration loads, yielding a different result in each experiment. Therefore, we averaged the test results when the SFPs were not installed to ensure the reliability of the research outcome.  
Table 4 summarizes the average settlement ratios with and without the SFP. With the SFP, the surface settlement ratio ranged from 0.44 to 0.72, indicating a reduction in the surface settlement. In the absence of the SFP, the soil flowed beneath the box structure, as shown in Figure 7. This resulted in a more rapid settlement of the adjoining ground of the box structure. In comparison, in the presence of the SFP (Figure 7), the inflow of the soil into the cavity beneath the box structure was blocked, which reduced the surface settlement.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Minimum** & **Average** & **Maximum** & **Standard** & **Average Settlement Ratio** \\  & **Settlement (mm)** & **Settlement (mm)** & **Deviation** & **Deviation** & **Average Settlement Ratio** \\ \hline Non\_SFP & 63.3 & 77.1 & 89.9 & 6.81 & Non\_SFP/Non\_SFP & 1.00 \\ U50\_L50 & 46.1 & 55.6 & 65.6 & 6.47 & U50\_L50/Non\_SFP & 0.72 \\ U100\_L50 & 42.2 & 54.2 & 63.7 & 7.42 & U100\_L50/Non\_SFP & 0.70 \\ U150\_L50 & 42.6 & 53  
The average settlement ratios for the lower SFP lengths of 50, 100, and 150 mm were 0.71, 0.47, and 0.44, respectively; the settlement decreased with an increase in the lower length. However, the results obtained with an increase in the upper length were as follows. (1) The settlement reduction was minor in comparison to that due to the increase in the lower length (Figure 8 and Table 4). (2) The settlement tended to increase across the test cases, as depicted in Figure 9. Based on this result, the surface settlement was predominantly affected by the changes in the lower length of the SFP. (3) There was a proportional relationship, in which greater settlement corresponded to greater dry density. This shows that settlement farther away from the box structure correlates to greater dry soil density after the test.  
Figure 8: Test results.  
Figure 7: Soil inflow behavior: without SFP; with SFP.  
Figure 9: SFP settlement trend across the test cases.
# Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN
## 2 Related Works and Contributions
Besides, a detailed optimization study of LoRa/LoRaWAN parameters such as spreading factor, coding rate, bandwidth, communication range and transmission power is presented to maximize the sensor lifetime. The energy model takes into account the transmission acknowledgment and its energy consumption cost using different LoRaWAN scenarios.
## 2 Materials and Methods  
### Chemicals and Reagents  
Aluminum chloride (AlCl\({}_{3}\)), rutin standard, sodium nitrite (NaNO\({}_{2}\)), DPPH (2,2-Diphenyl-1-(2,4,6-trinitrophenyl)hydrazyl), ABTS (diammonium 2,2'-azino-bis(3-ethylbenzothiazazoline-6-sulfonate), and potassium persulfate (K\({}_{2}\)S\({}_{2}\)O\({}_{8}\)) were purchased from Sigma Aldrich Chemie (Schnelldorf, Germany); the standards of gallic acid, ferulic acid, and synaptic acid were from Roth (Karlsruhe, Germany); the standards of isoquercetin, chlorogenic acid, p-coumaric acid, and quercetin were from Sigma (St. Louis, MO, USA); sodium hydroxide (NaOH), Folin-Ciocalteu reagent, calcium carbonate (CaCO\({}_{3}\)), ethanol 96%, and methanol were from Merck (Darmstadt, Germany); chitosan and sodium alginate were from Sigma Aldrich Co (Burlington, MA, USA); anhydrous calcium chloride (CaCl\({}_{2}\)) was from Chempur (Piekary Slaskie, Poland); and glacial acetic acid was from Promochem (Wesel, Germany).  
Apparatus: Analytical balance type ABT 220-5 DNM from Kern and Sohn GmbH (Balingen, Germany), ultrasonic bath type Elmasonic S 100 H from Elma Schmidbauer GmbH (Singen, Germany), pH meter type pH 7310 from Inolab xylem Analytics GmbH (Wellheim, Germany), magnetic stirrer from Ecostris Dlab Scientific Co., Ltd. (Beijing, China), universal centrifuge 320 R from Hettich GmbH and Co., KG (Tuttingen, Germany), Pipette Ratiopetta 1000-5000 \(\upmu\)L from Ratiolab, GmbH (Dreieich, Germany), and 20 mL syringe with 23 G needle (0.60 mm \(\times\) 30 mm) from KDM Becton Dickinson Discardit.
# Wireless Networks under a Backoff Attack: A Game Theoretical Perspective
## 7 Conclusions  
In this paper, we study a CSMA/CA wireless sensor network under a backoff attack: some of the sensors of the network are malicious and deviate from the defined contention mechanism. We first use [PERSON]'s network model to theoretically study the network throughput and observe that the malicious sensors have a gain on throughputs, at the expense of other sensors in the network. Even though the total throughput in some situations stays the same, it is not fairly distributed among sensors. This effect depends on the backoff parameters used by the malicious sensors, as well as on the number of malicious sensors present in the network.  
We then proceed to model the situation as a static game between the malicious sensors and a network gateway (thus, using a star topology): the gateway (server) tries to enforce the malicious sensors to behave following the contention mechanism, whereas the malicious sensors try to obtain a higher throughput. We solve analytically the game for the case that there is only one malicious sensor  
Figure 4: Example of the evolution of the mixed action for each player, using RM algorithm, where Cl stands for client. In each simulation, all clients tend to play \(ns\), except for one. This one randomly arises at each simulation using RM algorithm. This means that the game tends to the two player situation.  
and propose an algorithm based on Regret-Matching to learn the equilibrium with any number of players. Our approach is validated via simulations.  
The framework we introduce in this paper can be further deepened in different ways. The malicious sensors could vary their parameters (for instance, their contention window) in order not to be easily detected by the defense system: this would mean that the action set of the client grows up and also, the game complexity. Another line of research would be modeling the game using dynamic game tools: in this case, the stations would choose their actions in order to maximize not their immediate rewards, but taking into account future interactions: in a wireless network, it is rare that the stations communicate only once.  
Finally, our approach shows that there is a trade-off between modeling complexity and computational complexity. By making use of payoff matrices, we alleviate this trade-off: the game theoretic solutions we provide are agnostic with regards to where these payoffs come from. That is, we could use [PERSON]'s model as we do to relate rewards with the throughput, or we could relate rewards to other network parameters (as delay or any other measure of the quality of service) and yet our game modeling would be valid: we should only replace the payoff matrix and solve the game, with these new matrices. Hence, we believe that we introduce a framework simple enough to accommodate different situations, but also complex enough as to model the conflict and the actions of the different stations involved by using game theory tools.
## 3 Performance Analysis  
To perform the comparison and analysis, a database with the following headers is required: latitude, longitude, and power level. To achieve this, a drive test is conducted using a Raspberry Pi computer equipped with a geolocation card to record the geographical coordinates and signal power levels in dBm at each sampled point in the repeater installation area. Data are collected within a distance of no more than 100 m from both the repeater and the hotspot in the parish of Checa. Figure 2 illustrates the geographical locations of both the repeater and the hotspot in the park of the Checa parish.  
Figure 1: Flowchart for adjusting a radio frequency signal propagation model.  
Figure 2: Photograph of Checa’s central park, showing the location of the repeater at the top, and the location of the hotspot at the bottom.
## 6 Therapeutic Approaches of Dentin Hypersensitivity
### At-Home Therapeutic Treatments of Dentin Hypersensitivity
#### 6.1.5 Nano-Hydroxyapatite  
Currently, toothpastes containing nano-hydroxyapatite (n-HA) have emerged delivering positive effects in DH reduction [82; 83; 84]. Nano-hydroxyapatite is a form of calcium phosphate that is structurally similar to the hydroxyapatite found in natural hard tooth tissues. When applied to the tooth surface, n-HA can mimic the natural mineral composition of the tooth structure, which helps to replenish lost mineral content in areas where dentin is exposed. By strengthening and remineralizing the tooth, n-HA can reduce the sensitivity caused by the exposure of dentin tubules [85; 86]. Furthermore, mouthwashes containing n-HA, and fluoride are also deployed for daily use, significantly contributing to DH reduction [87; 88].
## 4 Methods
### TSX Backscatter and NDVI of DTLBs  
Both backscatter and NDVI of DTLBs were examined using the desktop geographical information system (GIS) software ArcMap 10. Using the Zonal Statistics tool, the mean backscatter value (\(\sigma^{\mathrm{o}}\) intensity) of 60 DTLBs (younger than 50 years) was extracted from all six TSX scenes, and the mean backscatter (\(\sigma^{\mathrm{o}}\) intensity) of 14 radiocarbon-dated DTLBs (older than 50 years) was extracted from 27 August and 09 September 2009 TSX scenes. We selected these two scenes because their spatial coverage includes all 14 radiocarbon-dated DTLBs completely. The final mean \(\sigma^{\mathrm{o}}\) was converted fromintensity to dB. We could extract NDVI of only 57 modern DTLBs that were visible in the cloud- and cloud shadow-free portions of the Landsat image from 13 July 2009. We were able to extract NDVI for all 14 radiocarbon-dated DTLBs. Mean basin backscatter (dB) and mean basin NDVI were used to investigate the backscatter and spectral properties of DTLBs with different ages. We used age classes based on calendar years in correspondence with those used by [PERSON] _et al._[10] (Table 2): 0-50 years (modern), 50-500 years (young), 500-2,000 years (medium), 2,000-5,000 years (old) and older than 5,000 years (ancient).  
To investigate the relationship between backscatter and NDVI of DTLBs, we performed a regression analysis between mean basin backscatter and mean basin NDVI with basin age in three subsets of our DTLB dataset: (1) 0-50 years since drainage, or modern DTLBs; (2) 50-10,000 years since drainage, or radiocarbon-dated DTLBs; and (3) 0-10,000 years since drainage, or combined modern and radiocarbon-dated DTLBs.  
The analysis of mean backscatter _versus_ basin age was performed for each TSX image separately on all 60 modern DTLBs that were covered by respective scenes acquired on different dates (Figure 1) to avoid biases in our basin age analysis that could be produced from seasonal dynamics of basin parameters. Similarly, for 14 radiocarbon dated DTLBs, we used mean basin backscatter extracted from 27 August 2009 and 09 September 2009 TSX scenes (Figure 1) to investigate if backscatter signals reflect their successional stage. Mean NDVI of 57 modern and 14 radiocarbon-dated DTLBs were used to investigate the relationship between NDVI of modern and radiocarbon-dated DTLBs and their age.  
To explore the relationship between backscatter properties with age of basins ranging from 0 to 10,000 years, we analyzed backscatter signals of DTLBs that were covered in 27 August 2009 and 09 September 2009 TSX scenes separately. Also, we explored the relationship between NDVI signals on 13 July of these basins with their age. We excluded three modern DTLBs from our analysis of basin backscatter and NDVI due to clouds or cloud shadows or being located outside of the Landsat extent from our analysis to make the basin backscatter property comparable with its NDVI property. In total, there were 39 DTLBs, among which 25 were modern and 14 were radiocarbon-dated.
# Multimodal Deep Learning for Group Activity Recognition in Smart Office Environments
## 1 Introduction  
Activity recognition is nowadays an active research topic, with ramifications over numerous application domains. To name a couple, activity recognition plays an important role in providing personalized support for healthcare monitoring systems [1] and assisted living for elderly care [2]. Additionally, as energy management is regarded the highest operational expenditure in commercial and residential buildings [3], several activity-based solutions have already been proposed to optimize the use of energy in smart home and building environments, by providing the adequate level of automation through dedicated services (e.g., [4]).  
Advances in this area are primarily fostered by the ever-increasing number of sensing devices that are permeating our surrounding environment. At the same time, activity recognition is attracting evermore attention from the field of machine learning which is rapidly expanding, oftentimes as a result of new and specific problem definitions driven by practical applications. In this regard, we put forward in this work a deep learning-based approach, which can model and account for different input modalities by fusing the data in order to perform activity recognition in the context of office environments. Our empirical results were validated based on the AMI Corpus dataset [5], which has been put forward by a European consortium that is developing meeting browsing technology (see illustration in Figures 1 and 2). In particular, the multimodal approach introduced in this paper was designed to integrate the audio-visual cues in order to distinguish among several typically occurring activity classes. The workplace environment is undergoing considerable transformationsaimed towards fostering agile and flexible work, by means of enabling the environment to adapt and contextualize the experience to the needs and preferences of its users. This can range from controlling things such as heating, lighting, and ventilation in order to increase user satisfaction (e.g., [6]), to more complex scenarios, wherein a dynamic set of devices, with their functionalities and services, cooperate temporarily to achieve a user's goal (e.g., [7]). It is therefore paramount to provide mechanisms that can lead to improved performance in activity recognition tasks, which can then, in turn, communicate this information to various computational decision nodes in the environment with the goal of assisting office users or maintenance personal with their daily activities.  
The rest of the paper is organized as follows. In Section 2 we introduce the relevant background and related work. Section 3 introduces the proposed model design for activity recognition in smart office environments. Section 4 presents the evaluation results and discusses the performance of our approach against previous work on the AMI dataset. The paper concludes in Section 5.
## 2 Problem Formulation and Preliminaries
### Lemmas and Assumptions  
**Lemma 1 [20].**_If there exists a positive, definite, and continuous function \(V(x)\) that satisfies_  
\[\dot{V}(x)=-aV^{p}(x)-bV^{q}(x)+\theta \tag{10}\]  
_where \(a>0,\ b>0,\ 0<p<1\), and \(q>1\), then the system is practically stable with a fixed time, and the system state can converge to the following residual set:_  
\[\left\{\underset{x\to T}{\text{\rm lim}}\bigg{|}V(x)\leq\min\left\{a^{-\frac{ 1}{p}}(\frac{\theta}{1-\theta})^{\frac{1}{p}},b^{-\frac{1}{q}}(\frac{\theta}{ 1-\theta})^{\frac{1}{q}}\right\}\right\} \tag{11}\]  
_where \(0<\theta<1\), and the convergence time \(T\) satisfies:_  
\[T\leq T_{upper}=\frac{1}{a\theta(1-p)}+\frac{1}{b\theta(q-1)} \tag{12}\]  
**Lemma 2 [21].**_For the following second-order system,_  
\[\left\{\begin{array}{l}\dot{x}_{1}=&x_{2}\\ \dot{x}_{2}=&-l_{1}sig^{\alpha_{1}}x_{1}-l^{\prime}_{1}\text{\rm sgn}x_{1}-l^{ \prime\prime}_{1}sig^{\alpha^{\prime}_{1}}x_{1}\\ &-l_{2}sig^{\alpha_{2}}x_{2}-l^{\prime}_{2}\text{\rm sgn}x_{2}-l^{\prime\prime }_{2}\text{\rm sign}x_{2}\\ \end{array}\right. \tag{13}\]  
_where \(l_{i}>0\), \(l^{\prime}_{i}>0\), \(l^{\prime\prime}_{i}>0\), and \(i=1,2\) and \(\alpha_{i}\) and \(\alpha^{\prime}_{i}\) satisfy \(\alpha_{1}=\frac{\alpha}{2-\alpha^{\prime}}\), \(\alpha_{2}=\alpha\), \(\alpha^{\prime}_{1}=\frac{4-3\alpha}{2-\alpha}\), \(\alpha^{\prime}_{2}=\frac{4-3\alpha}{3-2\alpha}\), and \(0<\alpha<1\), then the second-order system state can converge to the equilibrium point within a fixed time \(T\)._  
**Lemma 3 [22].**_For \(x_{1}\in R\), \(x_{2}\in R\), \(p>0\), \(q>0\), and \(r>0\), the following inequality holds:_  
\[|x_{1}|^{p}|x_{2}|^{q}\leq\frac{p}{p+q}r|x_{1}|^{p+q}+\frac{q}{p+q}r^{-\frac{p }{q}}|x_{2}|^{p+q} \tag{14}\]  
**Lemma 4 [23].**_For \(x_{i}\in R\), \(i=1,2,\ldots,n,0<p\leq 1\), and \(q>1\), the following inequalities hold:_  
\[\left(\sum_{i=1}^{n}\lvert x_{i}\rvert\right)^{p}\leq\sum_{i=1}^{n}\lvert x_{i} \rvert^{p},\left(\sum_{i=1}^{n}\lvert x_{i}\rvert\right)^{q}\leq n^{1-q}\sum_ {i=1}^{n}\lvert x_{i}\rvert^{q} \tag{15}\]  
**Assumption 1 [22].**_The lumped uncertainty \(\Omega\) is assumed to be bounded with \(\lVert\Omega\rVert\leq B\), where \(B>0\) is an unknown constant._