esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Verification of the Proposed Protocols
### Formal Analysis of DDSec Protocol
#### 6.3.2 Attack Derivation and Trace  
**TRACE 1**  
-- Query not attacker(rand_a[])  
Completing...  
Starting query not attacker(rand_a[])  
goal reachable: attacker(rand_a[])  
13 The attacker has the message rand_a.  
14 A trace has been found.  
15 RESULT not attacker(rand_a[]) is false.  
**TRACE 2**  
16 new ua_66: id creating ua_7831 at {57} in copy a_7503  
17 new ub_67: id creating ub_7832 at {58} in copy a_7503  
18 new dataname_68: bitstring creating dataname_7833 at {59} in copy a_7503  
19 out(c, M_7890) with M_7890 = sencrypt((ub_7832,dataname_7833, pk(skueb_7504)),kgnbb_7509) at {62} in copy a_7503  
20 in(c, (PublMgg,a,a_7502)) at {63} in copy a_7503  
21 event endUEB(A,B,pk(skueb_7504),rand_a,rand_b) at {64} in copy a_7503 (goal)  
22 The event endUEB(A,B,pk(skueb_7504),rand_a,rand_b) is executed.  
23 A trace has been found.  
24 RESULT event(endUEB(A_107,B_108,K,rand_a_109,rand_b_110)) ==>  
event(beginUEA(A_107,B_108,K,rand_a_109,rand_b_110)) is false.  
The attacker's actions are explained in derivations and trace steps concisely, as shown in Figure 7; some text is omitted for simplicity. The three attack traces follow similar steps but have varying inputs, outputs and events as follows.  
* The first trace, by eavesdropping the attacker knows rand_a, line 1-6 at inputs [1]-[6] corresponds to the creation of keys. Lines 7-10 at outputs [8], [10], [12], [14] correspond to the public keys; they are stored in fresh variables M, M_2190, M_2262, M_2333, respectively, by the attacker for later reuse. Lines 11-12 at inputs [15], [16] show the public keys are inserted in the key table in the main process for \(UE_{A}\) and \(UE_{B}\). Then, the attacker gets rand_a[].
* The second and third traces, Lines 1-12 at [1]-[16], are the same as in Trace 1 while in Traces 2 and 3 the steps are the same but in different sessions a_7503 and a_9678, respectively. With Lines 16-17 at inputs [57] and [58] corresponding to creations of ua, ub, the attacker creates ua_7831, ub_7832 and stores them in a_7503. Thus, Line 19 at output [62] corresponds to a session copy a_7503 \(UE_{B}\) has with the attacker, sending its ID ub_7832 and data name data name_7833, which the attacker stores in a new variable M_7890 for later use. In the same session, the attacker receives(PublMsg,a,a_7502) at input [(63)]. Line 21 shows the attacker receiving his goal at [(64)] when event endUEB(A,B,pk(skueb_7504),rand_a,rand_b) is executed in a_7503 with \(UE_{B}\) as \(UE_{B}\) ends the protocol believing it was communicating with \(UE_{A}\), but in fact it was communicating with the attacker. Trace 2 shows an attack on non-injective agreement, while Trace 3 shows an attack on injective agreement executed in session a_9678.
## 2 System Model
### Interference Scenario
#### 2.3.2 Wiretap Channel for Interference Scenario  
The number of bits reaching ED\({}_{n}\) over the wiretap channel is given by the following equation:  
\[R_{\rm{ED}_{n}}^{3}=\beta_{1}\log_{2}\left(1+\frac{P_{\rm{BS}}|g_{\rm{BS-ED}_{ n}}|^{2}+P_{\rm{active}}^{3}|g_{\rm{HT-ED}_{n}}|^{2}}{\sigma_{\rm{ED}_{n}}^{2}+P_{ \rm{}}\big{|}g_{\rm{}}\big{|}^{2}}\right). \tag{16}\]  
In Equation (16), it is assumed that \(k_{\rm{BS}}=k_{\rm{HT}}=1\). The total number of bits transmitted to all EDs is given as  
\[R_{\rm{ED}}^{3}=\sum_{n=1}^{N}R_{\rm{ED}_{n}}^{3}. \tag{17}\]
# Cyber-Physical Attack Detection and Recovery Based on RNN in Automotive Brake Systems
###### Abstract  
The violation of data integrity in automotive Cyber-Physical Systems (CPS) may lead to dangerous situations for drivers and pedestrians in terms of safety. In particular, cyber-attacks on the sensor could easily degrade data accuracy and consistency over any other attack, we investigate attack detection and identification based on a deep learning technology on wheel speed sensors of automotive CPS. For faster recovery of a physical system with detection of the cyber-attacks, estimation of a specific value is conducted to substitute false data. To the best of our knowledge, there has not been a case of joining sensor attack detection and vehicle speed estimation in existing literature. In this work, we design a novel method to combine attack detection and identification, vehicle speed estimation of wheel speed sensors to improve the safety of CPS even under the attacks. First, we define states of the sensors based on the cases of attacks that can occur in the sensors. Second, Recurrent Neural Network (RNN) is applied to detect and identify wheel speed sensor attacks. Third, in order to estimate the vehicle speeds accurately, we employ Weighted Average (WA), as one of the fusion algorithms, in order to assign a different weight to each sensor. Since environment uncertainty while driving has an impact on different characteristics of vehicles and causes performance degradation, the recovery mechanism needs the ability adaptive to changing environments. Therefore, we estimate the vehicle speeds after assigning a different weight to each sensor depending on driving situations classified by analyzing driving data. Experiments including training, validation, and test are carried out with actual measurements obtained while driving on the real road. In case of the fault detection and identification, classification accuracy is evaluated. Mean Squared Error (MSE) is calculated to verify that the speed is estimated accurately. The classification accuracy about test additive attack data is 99.4978%. MSE of our proposed speed estimation algorithm is 1.7786. It is about 0.2 lower than MSEs of other algorithms. We demonstrate that our system maintains data integrity well and is safe relatively in comparison with systems which apply other algorithms.  
Cyber-Physical Systems; Automotive; Cyber-Physical Attack; integrity; Sensor Attack Detection; speed estimation; deep learning +
Footnote †: journal: Applied Physics Letters
## 3 Methodology of STST Based on TVCM-VPF
### Implementation Procedures  
The block diagram of the proposed STST based on the TVCM-VPF is presented in Figure 2. When conducting STST with the integration algorithm of the CDM, the following procedures are recommended:  
1. Initialize the experimental parameters, e.g., \(\mathbf{M}_{\text{N}}\), \(\mathbf{C}_{\text{N}}\), \(\mathbf{K}_{\text{N}}\), \(\Delta t\), \(\mathbf{F}_{i}\).
2. Calculate the displacement \(\mathbf{d}_{\text{N},i+1}\) using Equation (4) at the initial sampling instant of the \((i+1)\)th integration time step with the measured reaction force \(\mathbf{F}_{\text{E},i}\) and the known \(\mathbf{F}_{i}\), \(\mathbf{d}_{\text{N},i}\), \(\mathbf{d}_{\text{N},i-1}\).
3. Update the velocity \(\mathbf{v}_{\text{N},i}\) and acceleration \(\mathbf{a}_{\text{N},i}\) at the \(i\)th step using Equations (2) and (3).
4. Determine the target acceleration of the specimen \(a_{\text{EC},i+1}\) using Equation (6).
5. Calculate the target displacement \(d_{\text{EC},i+1}(t)\) and target velocity \(v_{\text{EC},i+1}(t)\) using Equations (5) and (9) at each sampling time \(t\) with the calculated \(d_{\text{N},i+1}^{\mathbf{d}}\).
6. Generate the actuator command at each sampling time \(t\) with the TVCM, i.e., the feedforward block and the feedback block, loading the shaking table.
7. Go back to Step (5) until the end of each integration time interval.
8. Measure the corresponding reaction force and return to Step (2) until the end of the test.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Different** & & & & & \\
**Concentrations** & **Notch Groove** & **Oviposition** & **Larval Survival** & **Larval Weight** & **Comprehensive** \\
**of MeJA (mol/L)** & & **Rate (\%)** & **Rate (\%)** & **(mg)** & **Evaluation** \\ \hline
0 & 29.00 \(\pm\) 4.00 a & 71.67 \(\pm\) 6.01 a & 49.33 \(\pm\) 5.81 a & 43.00 \(\pm\) 6.00 a & 10.25 \\
10\({}^{-3}\) & 11.33 \(\pm\) 1.76 bc & 72.22 \(\pm\) 10.23 a & 27.21 \(\pm\) 2.26 bc & 24.00 \(\pm\) 5.00 c & 2.23 \\
10\({}^{-4}\) & 9.33 \(\pm\) 1.33 bc & 68.73 \(\pm\) 3.61 a & 22.07 \(\pm\) 4.16 c & 33.00 \(\pm\) 3.00 b & 1.42 \\
10\({}^{-5}\) & 6.67 \(\pm\) 2.18 c & 66.21 \(\pm\) 4.52 a & 37.22 \(\pm\) 3.89 b & 36.00 \(\pm\) 3.00 b & 1.63 \\
10\({}^{-6}\) & 16.33 \(\pm\) 3.66 b & 73.20 \(\pm\) 10.69 a & 21.16 \(\pm\) 1.96 c & 44.00 \(\pm\) 5.00 a & 2.53 \\ \hline \hline \end{tabular} Note: Data are means \(\pm\) SE. One-way ANOVA was used to compare differences between treatments. Values in the same column followed by different lowercase letters were significantly different to each other at \(\alpha\) = 0.05.  
\end{table} 
Table 5: Oviposition of ALB females and larval survival rate on trunks of PaP treated with MeJA aqueous solutions. Plants can develop an immune memory called defense priming, whereby exposure to trigger stimuli enables a faster or stronger response to subsequent attacks by pests and pathogens. Initiation of this induced defense provides increased protection and reduces the allocation cost of defense [19]. In this study, defense priming by MeJA was verified in a subsequent experiment investigating ALB selection and feeding on PaP as well as activating defense proteases in the tree. Compared with the control, the degree of damage on PaP treated with MeJA was reduced. It was dose-dependent, and different concentrations of MeJA aqueous solutions induced the emission of different quantities of Z3 HA and Z3H, with the concentration of \(10^{-4}\) mol/L MeJA aqueous solution inducing the greatest insect resistance in PaP. Therefore, JA and MeJA, especially MeJA, play a significant role in the defense response of PaP to ALB. Exogenous MeJA can also enhance drought resistance in plants [35], which is particularly important for PaP in arid or semi-arid areas in China. However, how long the effects of MeJA persist requires more investigation. Moreover, application techniques for MeJA should also be studied, particularly the development of a sustainable release of MeJA at a particular rate, since plant responses are concentration-dependent.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Cross-Layer Routing
#### 3.3.4 Cross-Layer Optimization Based on Prediction  
[PERSON] et al. [28] proposed a cross-layer optimization scheme based on link quality prediction. The author believes that all human activities have periodicity to predict the network topology, in order to design a more efficient routing, which is indeed worth affirming. Considering the capability  
Figure 10: CLRS Protocol Superframe Classification.  
difference between the nodes and the coordinators, two kinds of link quality prediction methods, with different complexity, have been proposed, for the links between two nodes and the links between nodes and coordinators, respectively, as shown in (7) and (8):  
\[\hat{R}_{ij}(\mathrm{t})=\beta\hat{R}_{ij}(\mathrm{t}-1)+(1-\beta)\sum_{k=t-W_{T }}^{t-2}R_{ij}(\mathrm{k}) \tag{7}\]  
\[\hat{R}_{is}(\mathrm{t})=R_{is}(\mathrm{t})-\mathrm{ECV_{t}} \tag{8}\]  
where \(R_{is}(\mathrm{t})\) is the instantaneous link quality between node \(\mathrm{i}\) and coordinator \(\mathrm{s}\), \(\mathrm{ECV_{t}}\) is the error correction value at time \(\mathrm{t}\), and \(\beta\) is the weight factor, which coordinates the weight of historical data and the latest predicted value.  
In order to better adapt to the mobile network environment, the protocol exploits the relay multi-hop transmission mode. Considering that the fixed transmission power in the mobile environment inevitably results in energy waste or data packet loss, this study adopted an adaptive adjustment of the transmission power of nodes. By comparing the predicted value of link quality with the standard value, when the predicted value was larger than the standard value, the transmission power was reduced to save energy, and vice versa. Moreover, considering the data priority, combining the random back-off time of MAC layer and increasing the transmission power to ensure reliable transmission of high priority data.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 4 Grounding Configurations and Optimization Techniques
### Use of Enhancing Materials (Bentonite, Conductive Concrete)  
Enhancing materials like bentonite and conductive concrete reduces soil resistivity, improving grounding efficiency. The resistance of an electrode with an enhancement layer can be estimated using the following:  
\[R_{e}=\frac{\rho_{m}}{2\pi L}ln\left(\frac{4L}{d+2t}\right) \tag{17}\]  
where \(\rho_{m}\) is the modified resistivity, and \(t\) is the thickness of the enhancement layer.  
Table 5 shows the comparison of ground resistance with enhancing materials. The reduction in ground resistance was computed using the baseline resistance without enhancement material \(R_{0}\) and the resistance with material \(R_{1}\) via the following formula:  
\[Reduction\ (\%)=\left(\frac{R_{0}-R_{1}}{R_{0}}\right)\times 100 \tag{18}\]
# Matching-Based Resource Allocation for Satellite-Ground Network
## 3 System Model
### Three-Sided Cyclic Matching Algorithm
\(\Sigma^{t}=\Sigma^{t}\backslash\{u_{i}^{t},\Sigma^{t}(u_{i}^{t}),\ *\}\);
20. symbol \(=1\);
21. end if:
22. \(\Sigma^{t}=\Sigma^{t}\cup(u_{i}^{t},l_{j}^{t},\ g_{k}^{t})\);
23. end if:
24. end for;
25. end while:
26. Output the stable result \(\Sigma^{t}\). ```  
**Algorithm 1**Three-sided Matching Satellite-Ground Network Resource Allocation Algorithm  
Starting with an empty set, Algorithm 1 runs. As indicated in line 8, \(\hat{\mu}=\Pi^{+1}\big{(}\Sigma^{t},\ g_{k}^{t}\big{)}\cap\Pi^{-2}\big{(} \Sigma^{t},\ g_{k}^{t}\big{)}\) adds the better triples to \(\Sigma^{t}\). If \(\hat{\hat{\mu}}\
eq\varnothing\) is true, line 10-23 will be executed. It is performed by choosing a more suitable user \(u_{i}^{t}\) for \(g_{k}^{t}\) in line 10. Note that \(Top(\hat{\mu},g_{k}^{t})\) represents the user in \(\hat{\mu}\) that has the highest priority, and then performing the same for \(u_{i}^{t}\) in line 13, choosing a more suitable LEO satellite. Finally, in line 22, this triple is added to \(\Sigma^{t}\).
## 4 Simulation Tests of the Fuzzy Control System with Smith's Predictor  
Before the final implementation of the control system, simulation experiments were carried out using the Matlab/Simulink program. The aim of the simulation tests was to select the controller parameters based on analysis of the selected integral control quality criteria.  
To assess the control quality, an indicator was adopted that is the absolute average value of the error signal. It is required in the steady state of the system that this value be as small as possible:  
\[min|\overline{e}|=\frac{1}{n-1}\sum_{i=0}^{n}e(i) \tag{10}\]  
where the error terms are defined as follows:  
\(\overline{e}\)--mean value of a control error,  
\(e(i)\)--discrete sequence of values constituting the control signal error.  
When analyzing the controller's operation, it is necessary to check its operation in transient states. The ITSE (integral time square error) indicator was used for this purpose. This indicator is often used in constant value systems, where the transient state is to disappear as quickly as possible:  
\[minITSE=min\sum_{i=0}^{n}e^{2}(i) \tag{11}\]  
where the error term is defined as follows:  
\(e(i)\)--control signal error.  
In the experiment, a control system was used, for which the block diagram, made in a simulation program, is shown in Figure 7[17].  
The block diagram shows a system with a fuzzy controller. Simulations were carried out for both the PI structure controller and the PD structure controller. During the test, in the 2000 th second of the simulation, there was a change in the opening degree of the discharge flaps, from 90% to 120%. Then, the control error was recorded, and the average value as well as the ITSE were calculated. Counting the control errors was continued for another 1000 s. The obtained values are shown in Table 2.  
Based on the computer simulations, it can be concluded that in relation to the selected controller settings, both control systems behaved stably. Nevertheless, better integral quality indicator results were obtained when using a system with a fuzzy controller of a PI structure. This controller was used in the industrial test.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **e** & **ITSE** \\ \hline FUZZY PI & 130.66 & 356375893 \\ FUZZY PD & 131.79 & 369961286 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Assumed control quality factors.
## 4 Results
### Reaction Wall Case Study  
The Civil Engineering Program of Universidad de Lima has been implementing several specialized laboratories in Lima, Peru since the launch of the degree program in 2017. Among them is a structures laboratory which needs to implement a reaction wall for supporting different types of structural tests. The reaction wall is 8.5 m high, 1.5 m thick, 11 m long in one direction, and 10.19 m long in the other direction, and it is attached to a 1.5 m-wide reaction slab. The materials used in the construction process were mostly steel rods, ready-mixed concrete, and anchor boxes. The steel rods were mostly 1 3/8\" in diameter and 1\" and 1\" rods with a coating of 5 cm in a double mesh for 74 tons of steel. For the boxes, 126 anchor boxes were used, being distributed every 90 cm. For the concrete, 250 m\({}^{3}\) of ready-mixed concrete was used and delivered to the site by truck. The above details can be seen in Figures 3 and 4.  
Figure 5 details the distribution of the steel rods in the reaction wall and their encounter with the anchorage boxes.  
Figure 3: A 3D view of the reaction wall with dimensions.  
Figure 2: VDC component interrelation flow for the revised processes.  
Table 1 presents the main characteristics of the retaining wall, including the dimensions, material type, number of anchor boxes, concrete volume, steel quantity, dowel diameter, reinforcement specifics for the rods, L-shaped wall type, and dimensions of the upper side.  
Initially, an overall schedule for the entire structures laboratory was received, from which the initial retaining wall schedule was extracted as a Gantt chart (Figure 6). This initial construction scheduling process gave 38 days as the time required to construct the reaction wall for 17 tasks. However, based on the experience of those involved, the days initially projected seemed insufficient compared with the complexity of the structure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type of Wall**} & \multirow{2}{*}{**Dimensions**} & \multicolumn{2}{c}{**Quantity of Steel**} & \multirow{2}{*}{**Material**} & \multicolumn{1}{c}{**Number of**} \\  & & **Per Bar Diameter in** & & **Anchor Boxes** \\ \hline \multirow{3}{*}{L-shaped} & \(11\times 7\times 1.5\) m & 35.8 mm (50 tons) & \multirow{3}{*}{Reinforced} & 126 (80 length \(\times\) 22.2 width \(\times\) 34.3 cm height) \\ \cline{1-1}  & \(10.22\times 7\times 1.5\) & 25.4 mm (11.54 tons) & & \\ \cline{1-1}  & m & 12.7 mm (12.58 tons) & & 34.3 cm height) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the reaction wall.  
Figure 4: A 3D view of section A.  
Figure 5: View of section A of the reaction wall with the details of the double 1 3/8-inch steel mesh and a 3D model of the anchor boxes.  
Therefore, the VDC methodology was applied to help understand and revise the initial schedule based on a collaborative approach.
# MiFT: A Moment-Based Local Feature Extraction Algorithm
## 2 Scale Invariant Feature Transform and Modified Discrete Gaussian-Hermite Moment
### Discrete Gaussian-Hermite Moment  
The MDGHM has orthogonality, and calculates the moment for a local image area, acting like a filter mask to describe local features using neighboring information. Suppose \(l(i,j)\) is an image and \(t(u,v)\) is a mask with size \(M\times N\)\([0\leq u\leq M-1,0\leq v\leq N-1]\). The coordinates for \(t(u,v)\) are transformed to \(-1\leq x,y\leq 1\) by:  
\[x=\frac{2u-M+1}{M-1},\;y=\frac{2v-N+1}{N-1} \tag{1}\]  
Hence, the Hermite polynomial is:  
\[H_{p}(x)=(-1)^{p}\exp(x^{2})\frac{d^{p}}{dt^{p}}\exp(-x^{2}) \tag{2}\]  
and the MDGHM mask can be expressed as:  
\[\left\{\begin{array}{l}\overset{\wedge}{H}_{p}(x,\sigma)=\frac{2}{M-1}\bar{ H}_{p}(x/\sigma)=\frac{2}{M-1}\frac{1}{\sqrt{2^{p}p!\sqrt{\pi\sigma}}}\exp(-x^{2}/2 \sigma^{2})H_{p}(x/\sigma)\\ \overset{\wedge}{H}_{q}(y,\sigma)=\frac{2}{N-1}\bar{H}_{q}(y/\sigma)=\frac{2} {N-1}\frac{1}{\sqrt{2^{q}q!\sqrt{\pi\sigma}}}\exp(-y^{2}/2\sigma^{2})H_{q}(y/ \sigma)\end{array}\right. \tag{3}\]  
Thus, MDGHM of an image at point \((i,j)\) can be expressed as:  
\[\overset{\wedge}{\eta}_{p,q}(i,j)=\frac{4}{(M-1)(N-1)}\sum_{u=0}^{M-1}\sum_{ v=0}^{N-1}I(u+i-\frac{M}{2}+1,v+j-\frac{N}{2}+1)\times\overset{\wedge}{H}_{p}(x, \sigma)\overset{\wedge}{H}_{q}(y,\sigma) \tag{4}\]  
Figure 1 shows MDGHM examples with three derivative orders. MDGHM acts like a Gaussian filter with multi-order derivatives. Since the base function of the \(n\)th order MDGHM changes sign  
times, MDGHM can efficiently represent spatial characteristics and strongly separate image features using the multi-order derivatives. Therefore, MDGHM can be used as a filter to describe local features.
_Article_  
**Photo-Biomodulation as a Prevention Modality of Oral Mucositis in Patients Undergoing Allogeneic Hematopoietic Stem Cell Transplantation**  
**[PERSON] \({}^{1}\)\({}^{\copyright}\)**, [PERSON] \({}^{1}\)\({}^{\copyright}\), [PERSON] \({}^{1}\)\({}^{\copyright}\), [PERSON] \({}^{1}\),  
**[PERSON] \({}^{2}\)\({}^{\copyright}\), [PERSON] \({}^{2}\), [PERSON] \({}^{2}\), [PERSON] \({}^{2}\), [PERSON] \({}^{1}\),**  
**[PERSON] \({}^{1}\)\({}^{\copyright}\) and [PERSON] \({}^{1,\star}\)\({}^{\copyright}\)**  
\({}^{1}\) Department of Oral Sciences and Institafical Surgery, Sapienza University of Rome, 00161 Rome, Italy;  
[PERSON]SS] (A.M.); [PERSON] (G.T.); [PERSON]F.R.); [EMAIL_ADDRESS] (A.D.V.); [EMAIL_ADDRESS] (R.P.); [EMAIL_ADDRESS] (A.P.);  
\({}^{2}\) Department of Cellular Biotechnologies and Hematology, Policinico Umberto I,  
Sapienza University of Rome, 00161 Rome, Italy;  
[EMAIL_ADDRESS] (R.R.); [EMAIL_ADDRESS] (W.B.); [EMAIL_ADDRESS] (C.C.); [EMAIL_ADDRESS] (A.P.I.)  
\({}^{\star}\) Correspondence:[EMAIL_ADDRESS]; Tel./Fax +39-064-991-8165  
**Abstract:** The aim of the study was to observe the effectiveness of a photo-biomodulation (PBM) protocol for the prevention of oral mucositis (OM) in patients undergoing allogeneic hematopoietic stem cell transplantation (aHSCT). A case-control study was conducted on 40 patients undergoing aHSCT. The patients were divided into two groups; the preventive group (PG) included 20 patients (7 females and 13 males) who were subjected to intra-oral PBM for five sessions a week, starting one day before the conditioning regimen and continuing until the 10 th day after transplantation (D+10). In each session, ten points on the at-risk mucosal surfaces were irradiated using a double diode laser that emits two wavelengths simultaneously at 650 nm and at 904-910 nm with the following parameters at each point: energy of 4 J, and power of 88.9 mW. The control group (CG) included 20 patients (10 females and 10 males) who were not subjected to laser therapy and were selected retrospectively to compare the obtained results. For all patients, OM was assessed by the World Health Organization (WHO) grading scale. Eight patients in the PG did not experience OM during their hospitalization period (with grade 0). Severe OM was observed in 40% of the patients in the PG, while in the CG, severe OM was shown in 85% of the patients. The mean duration of OM in the PG was significantly lower than that of CG (4.7 days in the PG and 15 days in the CG) (\(p<0.001\)). The study demonstrated that the preventive PBM protocol reduced the severity and duration of OM in patients undergoing aHSCT.  
**Keywords:** allogeneic hematopoietic stem cell transplantation; chemotherapy; conditioning regimen; diode laser; oral mucositis; photo-biomodulation